Large Language Models Show Promise in Detecting Drug Safety Signals from Clinical Notes
Key Takeaways:
- Large language models can identify immune-related adverse events in clinical notes without task-specific training, offering a potential alternative to labour-intensive manual review
- Performance remains below the threshold required for clinical decision support, with models tending to overpredict adverse events
- Despite limitations, this approach may support large-scale safety monitoring and accelerate research into cancer immunotherapies
The challenge of detecting drug safety signals
Drug safety signals are often embedded within unstructured clinical text, particularly in electronic health records. Identifying these signals has traditionally required either manual chart abstraction, which is resource-intensive, or natural language processing systems tailored to specific drugs and healthcare settings.
This challenge is particularly evident in the case of immune checkpoint inhibitors. These cancer therapies, first introduced in 2011, are associated with a broad range of immune-related adverse events. These events can affect multiple organ systems, including the colon, liver, lungs, heart, nervous system, skin, and endocrine system, making systematic detection complex and time-consuming.
Exploring large language models as a solution
Large language models are increasingly being explored as a way to streamline the identification of drug safety signals within clinical text. A multicentre study, published in eBioMedicine, evaluated whether these models could detect immune-related adverse events associated with immune checkpoint inhibitors.
The study focused on a zero-shot learning approach. In this setting, the model receives a single, detailed prompt without prior examples. The prompt used by the researchers began: “You are a clinical expert in identifying immune-related adverse events caused by immune checkpoint inhibitors …” and included a list of six immune checkpoint inhibitors alongside numerous associated adverse events.
This prompt was applied to clinical notes from multiple sources. These included records from 100 people treated at Vanderbilt Health, 70 people from the University of California, San Francisco, and 272 people enrolled in seven Roche-sponsored clinical trials.
Study design and model performance
The research team evaluated three models: GPT-3.5, GPT-4, and GPT-4o, with GPT-4o demonstrating the strongest overall performance.
To assess accuracy, the investigators used F1 scores, a metric that balances false positives and false negatives. Scores range from zero to one, with values above 90 percent considered excellent. A score of 80 percent or higher may be sufficient for use in automated clinical decision support systems.
At the patient level, GPT-4o achieved average F1 scores of 56 percent for Vanderbilt Health data, 66 percent for University of California, San Francisco data, and 62 percent for Roche clinical trial data. The models showed a consistent tendency to overpredict the presence of immune-related adverse events.
When analysing individual clinical notes, the model achieved an average F1 score of 57 percent across 667 notes from Vanderbilt Health, evaluating 17 different adverse events.
Implications for clinical practice and research
The findings suggest that large language models can play a role in identifying drug safety signals, even without task-specific training data.
“Manual patient chart abstraction for monitoring the safety and efficacy of drugs already at market requires tremendous resources and puts a drag on the pace of discovery in precision medicine. And that’s especially true with immune checkpoint inhibitors, where the adverse events are so varied. If zero-shot learning with LLMs could help with these notes, it could significantly reduce time and costs for all concerned,” said the report’s corresponding author, Cosmin Bejan, PhD, assistant professor of Biomedical Informatics at Vanderbilt Health.
However, the current level of performance falls short of what would be required for clinical decision support.
“These results show that zero-shot learning with a powerful LLM is useful for detecting these adverse events,” Bejan said. “This performance does not rise to the level required for clinical decision support, but the method could be valuable for automated irAE extraction across multiple sites, potentially speeding discovery and enhancing the safety and effectiveness of cancer immunotherapies.”
Wider research context
The study involved collaboration among multiple researchers at Vanderbilt Health, including Yaomin Xu, PhD, Eric Mukherjee, MD, PhD, Matthew Krantz, MD, Douglas Johnson, MD, MSCI, Elizabeth Phillips, MD, and Justin Balko, PhD. Funding support was provided in part by the National Institutes of Health.
Related research further highlights safety concerns associated with immune checkpoint inhibitors. In a research letter published in JAMA Oncology, Mukherjee, Phillips, and colleagues used logistic regression analysis of adverse event reports from the Food and Drug Administration. They confirmed that these therapies are independently associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis, which are severe and potentially life-threatening skin reactions. The study also found that this risk may be linked to exposure to human leukocyte antigen–restricted drugs.
Conclusion
Large language models represent a promising tool for extracting clinically meaningful insights from unstructured health data. While their current performance limits direct clinical application, their ability to operate across multiple datasets without task-specific training suggests potential for supporting large-scale pharmacovigilance efforts. As these models continue to improve, they may contribute to more efficient and comprehensive monitoring of drug safety in clinical practice.
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AI Scribes Deliver Modest Time Savings in Clinical Documentation, Large Study Finds
Key Takeaways:
- AI scribes were associated with small but measurable reductions in electronic health record use and documentation time
- Greater benefits were seen among clinicians who used the tools more frequently
- The reductions observed do not fully explain previously reported improvements in clinician burnout
The burden of clinical documentation
Documenting patient encounters within the electronic health record is a core component of modern healthcare delivery. However, it remains one of the most time-intensive aspects of clinical practice and is widely recognised as a contributor to clinician burnout.
In response, artificial intelligence-enabled ambient documentation tools, commonly referred to as AI scribes, have emerged. These systems automatically generate draft clinical notes based on conversations during patient appointments, allowing clinicians to review and edit them afterwards. While earlier research has suggested these tools may reduce burnout, there has been limited large-scale evidence examining how they affect day-to-day clinical workflows.
A large, real-world study across multiple hospitals
A new study co-led by researchers from Mass General Brigham and the University of California, San Francisco provides insight into this question. The study tracked the use of ambient documentation tools across five hospitals in the United States over a period exceeding two years.
More than 1,800 clinicians using AI scribes were compared with 6,770 clinicians who did not use the technology within the same institutions. This work forms part of the Ambient Clinical Documentation Collaborative, a multi-organisational research initiative.
Modest reductions in time spent on documentation
The findings, published in JAMA, indicate that AI scribes were associated with modest efficiency gains. On average, clinicians using these tools spent 13 fewer minutes per day on the electronic health record and 16 fewer minutes on documentation tasks.
These reductions correspond to relative decreases of 3% in overall EHR usage and 10% in documentation time.
The study also identified a small increase in productivity. Clinicians using AI scribes completed approximately 0.5 additional patient visits per week compared with those who did not use the technology.
Frequency of use influences impact
The benefits of AI scribes were not evenly distributed. The most notable improvements were observed among primary care physicians, advanced practice providers, female clinicians, and those who used the tools in at least half of their patient encounters.
Clinicians who used AI scribes for more than 50% of visits experienced roughly twice the reduction in total EHR time and three times the reduction in documentation time compared with less frequent users. Despite this, only 32% of clinicians adopted the technology at this level of regular use.
Financial impact remains limited
Although the increase in patient visits translated into higher revenue, the financial gains were modest. On average, clinicians using AI scribes generated an additional $167 per month.
This suggests that while the tools may offer efficiency benefits, their economic impact at an individual clinician level remains relatively small.
No change in after-hours workload
One notable finding was that time spent using the electronic health record outside of standard working hours did not differ significantly between clinicians using AI scribes and those who were not.
This raises important questions about how time savings during the working day are being redistributed and whether they meaningfully reduce workload burden or are absorbed by other clinical or administrative tasks.
Understanding the link to burnout
Despite prior evidence suggesting that ambient documentation tools may reduce clinician burnout, the mechanisms behind this effect remain unclear.
“Previous studies link ambient documentation to a significant decrease in burnout, but the underlying drivers of this reduction have been unclear,” said senior author Rebecca G. Mishuris, MD, MS, MPH, Chief Health Information Officer at Mass General Brigham.
“The modest reductions in documentation time we observed are unlikely to fully account for changes in burnout, underscoring the need to understand how these tools change how clinicians approach care delivery while using them.”
Adoption and real-world implementation
The study highlights both the promise and the limitations of AI scribes in real-world clinical settings. While measurable improvements were observed, their magnitude was relatively small and depended heavily on consistent use.
“Ambient documentation use is expanding rapidly across U.S. health care, making it essential to study how these technologies are impacting clinicians in real time,” said lead and corresponding study author Lisa Rotenstein, MD, MBA, an associate professor of medicine at the UCSF School of Medicine, and director of The Center for Physician Experience and Practice Excellence at Brigham and Women’s Hospital.
“Our study demonstrates the impact of AI scribes in diverse real-world implementations at multiple sites. It also emphasizes the value of helping clinicians become comfortable with the technology so that they are reaping its full benefits via frequent use.”
The need for further research
The findings suggest that while AI scribes can improve efficiency, they are not a complete solution to the challenges associated with clinical documentation or clinician burnout.
Further research is needed to understand how these tools influence clinician behaviour, how saved time is reallocated, and whether broader system-level changes are required to fully realise their potential benefits.
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Automated Weight Loss Programme Shows Promise for People Living with Cancer in Landmark Trial
Key Takeaways:
- A fully automated, web-based programme delivered clinically meaningful weight loss in people living with and beyond cancer, without any in-person support
- More than 43 percent of participants achieved at least 3 percent weight loss, with nearly one in three reaching 5 percent or more
- The intervention also improved a range of health outcomes, including diet quality, physical functioning, and cardiometabolic markers
A new model for post-cancer care
A large national randomised clinical trial has demonstrated that a fully automated, web-based weight loss intervention can deliver substantial health benefits for people living with and beyond cancer. The programme, developed by researchers at the University of Alabama at Birmingham, represents a significant shift in how post-cancer care may be delivered in the future.
Published in the Journal of the National Comprehensive Cancer Network, the study reported the highest level of weight loss ever achieved through a fully automated intervention in this population. The programme, known as the AMPLIFY Diet (AiM, PLan and act on LIFestYles), was designed to provide structured, evidence-based lifestyle support without requiring direct clinician involvement.
Addressing a major unmet need
A substantial proportion of people living with and beyond cancer are also living with overweight or obesity. In the United States, this figure is estimated to be around 70 percent. This places individuals at increased risk of cardiovascular disease, type 2 diabetes, functional decline, cancer recurrence, and the development of second primary cancers.
Despite this, access to specialist oncology dietitians remains limited. Traditional weight management programmes often rely on in-person consultations or regular coaching, which can be difficult to scale and may not be accessible to all patients.
The AMPLIFY Diet intervention was developed specifically to address these barriers by delivering personalised nutrition and behavioural support entirely online.
A fully automated intervention
The programme operates without live coaching, counselling calls, or face-to-face appointments. Instead, it uses a structured digital platform that includes weekly interactive sessions, goal-setting tools, progress monitoring, and automated personalised feedback.
Participants engage with the system independently, receiving guidance that is grounded in established behavioural and nutritional science. This approach allows for scalability while maintaining a consistent standard of care.
“This is a game changer for cancer survivorship care,” said Wendy Demark-Wahnefried, Ph.D., R.D., senior author and professor at UAB’s School of Health Professions and O’Neal Comprehensive Cancer Center. “We showed that a completely automated online program grounded in decades of behavioral and nutrition science can safely and effectively help cancer survivors lose weight and improve their health at scale.”
Study design and participant profile
Between 2020 and 2024, the study enrolled 349 participants aged between 50 and 82 years from 31 states across the United States. All participants were living with and beyond cancers associated with obesity.
The cohort included individuals with a range of cancer types, including breast, colorectal, prostate, endometrial, ovarian, thyroid, renal, and haematologic cancers. Participants were randomly assigned to either the AMPLIFY Diet programme or a control group receiving standard survivorship information.
Clinically meaningful weight loss outcomes
After six months, the results showed clear differences between the intervention and control groups.
More than 43 percent of participants in the AMPLIFY Diet group achieved weight loss of at least 3 percent of their body weight. In comparison, only 13 percent of those receiving usual care reached this threshold.
In addition, nearly one in three participants in the intervention group lost at least 5 percent of their body weight. This level of weight loss is widely associated with reductions in cardiovascular risk and improvements in cancer-related outcomes.
On average, weight loss in the intervention group was nearly five times greater than that observed in the control group.
Broader health improvements
The benefits of the programme extended beyond weight loss alone. Participants in the AMPLIFY Diet group experienced improvements across multiple domains of health and wellbeing.
These included reductions in waist circumference and overall caloric intake, as well as improvements in diet quality. Biochemical markers also shifted in a favourable direction, with lower circulating levels of leptin, a hormone associated with cancer progression and cardiometabolic disease.
Further gains were observed in blood pressure, physical functioning, and cognitive performance. Participants also reported improvements in depression and their ability to engage in social roles, suggesting a broader impact on quality of life.
Strong engagement without human support
One notable finding from the study was the level of participant engagement. Individuals completed an average of 60 percent of the weekly sessions, which is considerably higher than engagement rates typically reported in other digital lifestyle interventions.
This suggests that a well-designed automated system can maintain user engagement even in the absence of direct human interaction.
Implications for scalable care
Unlike many conventional weight management programmes, the AMPLIFY Diet intervention does not require ongoing staff involvement. This makes it particularly well suited for integration into healthcare systems, cancer centres, and community-based services.
The ability to deliver consistent, evidence-based care at scale may help address longstanding gaps in survivorship support, particularly in settings where specialist resources are limited.
The role of behavioural and nutritional care
The researchers emphasise that lifestyle-based interventions remain a cornerstone of care for people living with and beyond cancer, particularly as pharmacological approaches continue to evolve.
“Behavioral and nutritional interventions are essential,” Demark-Wahnefried said. “Diet quality, muscle preservation, cognition, and long-term sustainability of a healthful lifestyle and body weight are critical for cancer survivors, and even if weight loss medications eventually receive broadscale endorsement, they alone do not address all of these needs.”
Future directions
The research team is now focusing on expanding the reach of the AMPLIFY Diet programme across both clinical and non-clinical settings. The aim is to improve access to effective survivorship care while also contributing to broader cancer prevention efforts.
The study was funded by the National Institutes of Health and the American Cancer Society.
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Growing Use of Emojis in Electronic Health Records Raises Safety Questions
Key Takeaways:
- Emoji use in electronic health records has increased steadily between 2020 and 2025, appearing in thousands of clinical notes.
- While emojis may offer a quicker, more expressive way to communicate, they introduce risks of misinterpretation across clinicians and patients.
- Experts suggest that clearer governance and guidance may be needed to ensure safe and consistent use in clinical documentation.
Study reveals growing presence of emojis in clinical records
A recent study from Michigan Medicine, published on 14 January, examined 218.1 million clinical notes from the electronic health records of 1.6 million people receiving care. The findings revealed a notable rise in emoji usage by both healthcare professionals and patients between January 2020 and September 2025.
Across all records analysed, researchers identified 372 unique emojis appearing in 4,162 notes. While this represents a small proportion of total documentation, the upward trend signals a shift in how digital communication is entering clinical environments.
David Hanauer, clinical associate professor of paediatrics and learning health science at Michigan Medicine, explained that the study was initially driven by simple curiosity.
“It was mostly out of interest, just trying to explore if anything was there at all,” Hanauer said. “Our understanding had been that emojis and other symbols are actually not supposed to be used in a medical record, so we were wondering: Were there any there at all, and how often were they being used, and which ones?”
Concerns around clarity and misinterpretation
Despite their increasing use, emojis raise important concerns about clarity in clinical communication. Hanauer highlighted that the ambiguity of many emojis could lead to misunderstanding.
“Most of the concerns that people have is that it’s hard to understand from an emoji what is being conveyed,” Hanauer said. “Maybe a smiley face is pretty obvious to most people, but there’s a lot of different faces with nuances and other symbols. I think there can be a lot of miscommunication, misinterpretation.”
The issue becomes more complex when considering variation in interpretation across different groups of people receiving care and healthcare professionals.
Kim Ford, a health information business systems analyst lead at Michigan Medicine, emphasised how generational differences may influence understanding.
“If you have older patients who may not be familiar with emojis, it’s almost like a foreign language to them,” Ford said. “(For) our younger generation – or those people that have grown up with technology – it’s a second language for them that they understand very well. That’s my biggest concern.”
Accessibility challenges for some people receiving care
Beyond interpretation, accessibility presents another potential barrier. Hanauer noted that small visual symbols may be difficult for some individuals to distinguish clearly, particularly those with visual impairments.
“For older people, having small emojis might actually be hard for them to see and make out, so they might see its face but they can’t tell what the specific expression is,” Hanauer said. “I think we found over 300 different kinds of emojis being used. That’s a lot of different symbols that people would have to understand what they mean.”
This highlights a broader concern that even seemingly simple visual cues may not be universally interpretable or accessible.
Potential implications for patient care
A key concern raised by the study is whether emoji misinterpretation could affect clinical outcomes. While there is currently no direct evidence linking emoji use to adverse outcomes, the possibility remains.
“We hope that doesn’t happen, but I think because of that concern, there’s probably going to be a little bit more oversight,” Hanauer said. “I don’t think we would easily be able to find a circumstance in which there was actually some sort of better or negative outcome from an emoji being misinterpreted.”
The absence of clear evidence does not eliminate the risk, particularly in high-stakes environments where precise communication is essential.
Balancing efficiency with professionalism
Some healthcare professionals recognise potential benefits in using emojis, particularly in reducing communication burden within electronic systems. However, concerns remain about maintaining professionalism and objectivity.
Leah Beel, a medical assistant at American Family Care in Ann Arbor, expressed reservations about their place in formal documentation.
“From my experience, EHRs are used to get quick information and try to communicate with each other in a fast and reliable way,” Beel said. “The only thing I would use is an exclamation point, which, even then, is kind of out there. It’s a good thing that emojis can show enthusiasm or certain reactions, but I also think to a degree – it’s not unprofessional but just someone might take it the wrong way. My perspective on EHR is that you write very objectively.”
In contrast, Elizabeth Rossmann Beel, a paediatric anaesthesiologist at Texas Children’s Hospital, noted that emojis may offer a more efficient way to communicate in certain contexts.
“It’s a way to react to something without putting as much effort into it, or into making that person who’s reading it feel like they need to reply,” Rossmann Beel said. “I think it can cut down a little bit on the burden of replying to and responding to messages in the EHR, which is nice. However, it’s definitely more casual, and so sometimes that’s not the best tone to be setting in a medical record.”
The case for governance and standardisation
Given the growing use of emojis, there is increasing interest in whether formal guidance or regulation should be introduced.
Ford suggested that healthcare organisations may need to consider structured governance around emoji use.
“Maybe emojis are an acceptable means of communication,” Ford said. “The other piece is, should there be a governance process around what emojis can be used? And in what situations? I need to think a little bit about what their structure might look like – what department should be involved in reviewing and approving those, what should be the process to submit an emoji for consideration for use? There’s a lot of pieces to the governance process that need to be figured out there.”
A shift in digital communication within healthcare
The findings from this study reflect a broader evolution in digital communication, where informal elements are beginning to intersect with traditionally formal systems such as electronic health records.
While emojis may offer efficiency and emotional nuance, their integration into clinical documentation raises important questions about clarity, accessibility, professionalism, and patient safety. As their use continues to grow, healthcare systems may need to balance flexibility with standardisation to ensure communication remains precise, inclusive, and clinically appropriate.
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Algorithm-Guided Insulin Dosing Improves Blood Sugar Control in Type 2 Diabetes
Key Takeaways:
- An algorithm paired with continuous glucose monitoring significantly increased time in target glucose range compared with standard self-monitoring approaches
- The tool provides personalised weekly insulin dose recommendations based on recent glucose data, helping to simplify titration
- Early findings suggest strong patient acceptability and potential to enhance diabetes management at scale, though larger trials are needed
A data-driven approach to insulin adjustment
A novel algorithm developed by researchers at the University of Virginia Center for Diabetes Technology has demonstrated encouraging results in supporting people living with Type 2 Diabetes to better manage their blood glucose levels.
The system works in combination with a continuous glucose monitor and provides tailored recommendations for insulin dose adjustments. Rather than relying solely on manual interpretation of glucose readings, the algorithm analyses patterns over time and offers structured, data-informed guidance.
In a clinical trial involving 30 participants, individuals were randomly assigned to one of two approaches over a 16-week period:
- Algorithm-guided insulin adjustment using continuous glucose monitoring data
- Traditional self-monitoring of blood glucose with independent dose adjustment
The results showed a marked improvement in glycaemic control among those using the algorithm. Participants in this group increased their average time spent within a safe blood glucose range from 54.1% to 75.3%. By contrast, those relying on self-monitoring alone saw a more modest increase from 50.2% to 55.3%.
Moving beyond traditional insulin management
The findings highlight the growing role of digital health tools in diabetes care. According to Marc D. Breton, the study’s lead author:
“These results clearly show that diabetes technology and advanced algorithms can be leveraged to great effects, well beyond the classical paradigm of automated insulin delivery. As continuous glucose monitoring and connected medical devices become ubiquitous, we have the opportunity to provide highly personalized advice and monitoring to people with diabetes and guide their use of insulin and medications. Showing the impact of these technologies in early insulin therapy (only one dose a day) opens the door to helping the vast majority of people using insulin, well beyond what we were able to achieve with automated insulin delivery.”
This perspective reflects a broader shift towards personalised, technology-enabled care. Rather than fully automated systems alone, there is increasing interest in decision-support tools that augment clinical judgement and patient self-management.
Addressing the challenges of insulin titration
For many people living with type 2 diabetes, treatment often begins with oral or non-insulin therapies. However, as the condition progresses, insulin may become necessary to maintain adequate glycaemic control.
Adjusting insulin doses – a process known as titration – can be complex and burdensome. It typically requires frequent monitoring, interpretation of glucose patterns, and iterative dose changes. Importantly, there is no universally standardised titration protocol, which can create variability in care and outcomes.
To address this, Anas El Fathi developed the algorithm with the aim of streamlining and improving this process. The system evaluates two weeks of continuous glucose monitoring data and generates weekly recommendations for insulin dose adjustments, offering a structured and personalised approach.
Strong acceptance and clinical potential
The study also explored how well the technology was received by participants. According to Ralf Nass:
“From a medical point of view, it was fascinating to see that the algorithm was not only better than the standardized insulin titration recommendations, but also how well the technology was accepted by the participants with type 2 diabetes. This type of technology has the potential to help physicians enable their patients to achieve better glycemic control faster by using a personalized approach.”
This combination of improved outcomes and user acceptability is particularly important, as adherence and engagement remain key challenges in long-term diabetes management.
Future directions – towards more personalised diabetes care
While the results are promising, the researchers emphasise that further validation is required. Larger and longer clinical trials will be needed to confirm the effectiveness of the algorithm across more diverse populations.
Looking ahead, the integration of more advanced data-driven approaches may further enhance personalisation. Breton noted:
“It is only the very beginning of these efforts. With early demonstration behind us, we can focus on robust approaches that will be effective with more varied populations. Integrating recently developed data-driven methodologies, especially digital twins, to further improve our capacity to tailor diabetes managements to individuals is likely to once more revolutionize diabetes care.”
Such developments could represent a significant step forward in precision medicine for people living with diabetes.
Study publication and funding
The findings have been published in the peer-reviewed journal Diabetes Technology & Therapeutics, with the article available as open access.
The research team included El Fathi, Nass, Carol J. Levy, Camilla Levister, Grenye O’Malley, Nirali A. Shah, Shaziah Hassan, Cheryl Quainoo, Chaitanya L.K. Koravi, Taylor N. Nguyen, Giulio Matteo Santini, Emma Emory, Carlene Alix, Dillon K. Flanagan, David Fulkerson, Mary Clancy Oliveri, Christian Laugesen, Jonas K. Lineolov, Peter W. Hansen and Breton.
The clinical trial was supported by a grant from Novo Nordisk.
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AI Diet Recommendations for Adolescents Show Significant Nutritional Gaps, Study Finds
Key Takeaways:
- AI-generated diet plans consistently underestimated energy and key macronutrients required by adolescents
- Macronutrient balance was frequently misaligned with clinical guidelines, with lower carbohydrates and higher fat and protein levels
- Researchers caution that AI tools should not replace dietitians for adolescent nutrition without professional oversight
Growing demand for accessible nutrition support
Artificial intelligence is increasingly being used to support dietary planning, particularly in areas where access to qualified professionals is limited. However, a new study published in Frontiers in Nutrition raises important concerns about the reliability of these tools when applied to adolescents living with overweight or obesity.
Globally, adolescent overweight and obesity are rising at pace, affecting an estimated 390 million young people in 2022. In many regions, this now represents the most common form of malnutrition. Excess body weight in adolescence is associated with a range of adverse health outcomes, including type 2 diabetes, dyslipidaemia, hypertension, and sleep apnoea. It also increases the likelihood of obesity in adulthood and is linked to reduced quality of life.
Alongside physical health risks, adolescents may experience body image concerns and engage in harmful weight control behaviours such as self-induced vomiting or misuse of laxatives.
Dietary modification remains central to improving outcomes. Dietitians play a key role in delivering tailored, evidence-based nutrition plans aligned with established guidelines. However, limited access and workforce pressures can restrict the availability of personalised support.
AI tools, including chatbots and large language models, are increasingly being explored as a way to bridge this gap. While they can provide general dietary guidance, concerns remain about their accuracy, safety, and ability to replicate the individualised care provided by trained professionals.
Study design – comparing AI models with dietitian plans
To better understand the role of AI in adolescent nutrition, researchers conducted a direct comparison between AI-generated diet plans and those created by a dietitian.
Five AI systems were evaluated: ChatGPT-4o, Gemini 2.5 Pro, Claude 4.1, Bing Chat-5GPT, and Perplexity. Across two sessions, these models generated a total of 60 diet plans. Each plan covered three days and was based on four standardised adolescent profiles, including boys and girls living with overweight or obesity.
These AI-generated plans were compared with dietitian-designed one-day plans developed in line with established nutritional recommendations. The reference plans followed a macronutrient distribution of:
- 45–50 % carbohydrates
- 30–35 % fat
- 15–20 % protein
The researchers then analysed energy intake, macronutrient composition, micronutrient content, safety, and feasibility.
Consistent underestimation of energy and macronutrients
The findings revealed a clear and consistent pattern across all AI models. Diet plans generated by AI underestimated both total energy intake and key macronutrients when compared with dietitian-designed plans.
On average:
- Energy intake was lower by 695 kcal
- Protein intake was reduced by 20 g
- Fat intake was reduced by 16 g
- Carbohydrate intake was reduced by 115 g
Given the high energy demands of adolescence, such deficits could have meaningful clinical implications, particularly for growth, development, and overall health.
Macronutrient imbalance – a shift away from guidelines
Beyond total intake, the balance of macronutrients was also significantly altered in AI-generated plans.
Some AI models recommended:
- Protein intake up to 23.7 %
- Fat intake up to 44.5 %
Both values exceeded recommended levels. In contrast, carbohydrate intake accounted for no more than 36.3 %, falling below guideline recommendations.
Dietitian-designed plans, by comparison, remained closely aligned with clinical standards:
- Carbohydrates: 44 %–46 %
- Protein: 18 %–20 %
- Fat: 36 %–37 %
The authors noted:
“This pattern illustrates a systematic shift across all AI models to lower CHO, higher protein, and higher lipid meal structures, indicating that the macronutrient balance, not just the amount of gram-based nutrients, is significantly disrupted in AI-generated plans.”
Researchers suggest that AI models may be influenced by popular dietary trends, such as low-carbohydrate or ketogenic approaches, rather than evidence-based adolescent nutrition guidelines. This shift may pose risks during a critical period of physical and cognitive development.
Micronutrient variability raises additional concerns
In addition to macronutrient discrepancies, the study identified significant variability in micronutrient composition across AI-generated plans.
No model consistently matched the dietitian-designed reference diet across all nutrients. This inconsistency raises concerns about potential micronutrient deficiencies, which could further compromise adolescent health.
The findings suggest that AI tools currently lack the technical precision required to accurately estimate both macro- and micronutrient needs in personalised dietary plans for adolescents.
Strengths and limitations of the study
The study offers several notable strengths. It evaluated multiple AI models, allowing for robust comparison across systems. The use of three-day diet plans enabled identification of consistent patterns rather than isolated outputs. Dietitian-designed plans provided a credible clinical benchmark, and the inclusion of both macro- and micronutrient analysis allowed for a comprehensive assessment of dietary quality.
However, there are limitations to consider. The findings are specific to the models tested, which are rapidly evolving. Standardised adolescent profiles may not fully capture real-world complexity, limiting personalisation. The use of simulated scenarios rather than real-life behaviours may reduce ecological validity. Additionally, prompts were standardised and delivered in a single language, which may limit generalisability across populations.
Implications for clinical practice and AI use
The study highlights important risks associated with the unsupervised use of AI for adolescent dietary planning.
As the authors conclude:
“AI models have exhibited clinically significant deviations in diet plans for adolescents at both macro and micro levels.”
These deviations include consistently lower energy and carbohydrate recommendations compared with dietitian-designed plans.
Until these limitations are addressed, AI-generated diet plans should be used with caution. They may serve as a supplementary tool under professional supervision, but they are not currently a safe or reliable substitute for qualified dietary guidance in adolescents.
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Virtual Care Could Reduce Hospital Admissions in Severe Eating Disorders, Study Finds
Key Takeaways:
- A fully virtual, multidisciplinary treatment programme demonstrated strong engagement and positive clinical outcomes for adults living with severe eating disorders
- Structured online support during high-risk transition periods may help reduce hospital admissions and sustain recovery following discharge
- The model highlights the potential for digitally delivered, evidence-based care to bridge gaps between inpatient and community services
Evaluating a new approach to severe eating disorder care
An evaluation conducted by Oxford Health NHS Foundation Trust has examined whether intensive, fully virtual treatment can effectively support people living with severe eating disorders. The study, published in the Journal of Eating Disorders, focused on a service known as Step Care, developed through the HOPE Provider Collaborative.
Researchers describe this as the first prospective study to investigate a completely virtual, intensive treatment model using multidisciplinary enhanced cognitive behavioural therapy (CBT-E). The programme is designed to support individuals as they transition between inpatient treatment and community-based care.
This period of transition is widely recognised as a critical phase in recovery. People living with severe eating disorders face a particularly high risk of relapse shortly after discharge from hospital, especially within the first two months. The study therefore explored whether structured virtual support during this vulnerable period could help maintain recovery and reduce the likelihood of readmission.
Addressing gaps in existing services
Step Care was developed in response to well-documented challenges within eating disorder services. These include fragmented transitions between inpatient and community care, repeated hospital admissions, and limited access to intensive day treatment.
The service is delivered entirely online and brings together a multidisciplinary team, including professionals from psychology, nursing, dietetics, and art therapy. This integrated approach aims to provide consistent and coordinated care across different stages of recovery.
Step Care operates through three distinct pathways:
- Starting Well – for individuals at risk of requiring hospital admission, with a focus on prevention
- Staying Well – for those recently discharged from inpatient care, supporting early recovery
- Working towards Recovery – for individuals who have begun restoring weight and are focusing on longer-term recovery
Lucy Gardner, professional lead dietitian within the Step Care service, highlighted the importance of integrating nutritional support within a broader therapeutic framework:
“Nutrition plays a crucial role in mental health, yet access to the right level of dietetic support is often inconsistent,” she said. “Our model offers a clear, evidence-informed way to tailor dietetic input to individual need, delivering CBT-E virtually as part of a multidisciplinary team.”
Positive outcomes across key measures
The evaluation reported high levels of engagement and programme completion, including among individuals who had been living with eating disorders for an extended period.
Participants within the Starting Well pathway experienced significant improvements across several clinical and psychological measures, including:
- Body mass index (BMI)
- Eating disorder symptoms
- Psychosocial impairment
- Mood
Importantly, most individuals in this group were able to avoid hospital admission during the course of the programme.
For those in the Staying Well pathway, outcomes were also encouraging. Participants maintained their weight and experienced a reduction in the overall impact of their illness during a period typically associated with high relapse risk. Unplanned hospital admissions were reported to be rare, and many individuals were successfully supported in transitioning to community-based care.
Sharon Ryan, nurse lead within the Step Care service, emphasised the importance of this post-discharge phase:
“The weeks after leaving hospital are often the most fragile,” she said. “Step Care provides consistent multi-disciplinary support at that point, helping people maintain their recovery with support to feel safe and confident out of hospital.”
Implications for future care models
The findings suggest that intensive, evidence-based treatment for severe eating disorders can be delivered effectively in a virtual format. This approach may offer a valuable additional option for supporting individuals at home, particularly during critical transition periods.
Agnes Ayton, clinical lead for the HOPE Provider Collaborative, explained the underlying aim of the service:
“Step Care was designed to bridge the gap between inpatient and community services,” she said. “The findings show that intensive, evidence-based treatment can be delivered safely online, providing continuity of care at a time when people are most vulnerable.”
She also noted that both engagement and clinical outcomes were encouraging, including among individuals who had experienced long-term illness.
A complement to existing services
The authors conclude that virtual programmes such as Step Care may serve as an important complement to traditional inpatient and community services. By providing structured, multidisciplinary support during high-risk periods, these models have the potential to enhance continuity of care and support sustained recovery for people living with severe eating disorders.
As healthcare systems continue to explore digital and hybrid models of care, this study adds to a growing body of evidence suggesting that virtual interventions can play a meaningful role in complex, long-term conditions.
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AI in Healthcare: Promise, Pitfalls, and the Risk of Misguided Medical Advice
Key Takeaways:
- People using AI for health advice often struggle to interpret and communicate symptoms effectively, leading to incorrect conclusions in many cases.
- Even when AI identifies a condition correctly, it may fail to recommend appropriate urgency, particularly in time-sensitive or complex scenarios.
- Clinicians see value in AI as a supportive tool, but stress that it should complement, not replace, professional medical care.
AI becomes a common source of health information
As technology companies continue to develop platforms tailored for healthcare consultation, artificial intelligence is becoming an increasingly influential part of how people make decisions about their health. According to OpenAI, more than 40 million people use ChatGPT each day to seek health-related information.
However, emerging research suggests that while these tools offer unprecedented access to medical knowledge, they may also mislead users in certain contexts.
Challenges in how people use AI for medical queries
One of the central issues identified by researchers is not only the capability of AI systems, but how individuals interact with them. Many people lack the knowledge required to communicate symptoms accurately or comprehensively.
A recent study published in Nature Medicine attempted to replicate real-world use of AI chatbots. Participants were given medical scenarios and asked to consult AI tools. The results highlighted notable limitations:
- Participants correctly identified the condition only about one-third of the time.
- Just 43% made the correct decision regarding next steps, such as whether to seek emergency care or remain at home.
“People don’t know what they are supposed to be telling the model,” says Andrew Bean, who studies AI systems at Oxford University and was one of the authors on this study.
Bean explains that effective use of AI often depends on precise wording. “Doctors are trained to ask you questions about symptoms you might not have realised you should have mentioned,” says Bean.
Small differences in input can lead to dangerous outcomes
The study demonstrated how subtle differences in language can significantly alter the advice provided by AI systems.
In one example, two individuals described the same clinical scenario slightly differently. One described experiencing “the worst headache I’ve ever had” and was advised to go to the emergency room immediately. The other, who did not include that specific phrasing, was advised to take aspirin and remain at home.
“Turns out this was actually a life-threatening condition,” says Bean.
This highlights a critical limitation: AI systems rely heavily on the information they are given, and may not prompt for missing but clinically important details in the way a trained clinician would.
When AI gets the diagnosis right but the advice wrong
Even when AI tools successfully identify a medical condition, they may still provide inappropriate guidance regarding urgency or next steps.
In a separate study, researchers evaluated how AI systems responded to a range of medical scenarios. They found that in 52% of emergency cases, the tools “under-triaged” – treating conditions as less serious than they actually were.
In one case, the AI failed to direct a hypothetical patient experiencing diabetic ketoacidosis and impending respiratory failure – both life-threatening conditions – to seek emergency care.
“When there was a textbook medical emergency, ChatGPT got it right,” said Girish Nadkarni, a doctor and AI researcher at Mount Sinai who is an author on the study. However, he noted that performance declined in more complex situations, particularly where timing was critical. In such cases, the system often misjudged how urgently care was required.
An OpenAI spokesperson responded by stating that the study did not reflect typical real-world usage and that it evaluated an older version of ChatGPT, which the company says has since been improved to address some of these concerns.
The role of AI in supporting patient understanding
Despite these concerns, many clinicians believe that AI tools can still play a constructive role in healthcare, particularly in improving patient understanding and engagement.
“I encourage patients to use these tools,” says Robert Wachter, a doctor at UC San Francisco and author of the recently published book, A Giant Leap: How AI Is Transforming Health Care and What That Means for Our Future.
Wachter points out that barriers to accessing healthcare – including cost and availability – mean that AI can sometimes provide a useful alternative source of information. “The advice you get from the tools is substantially better than nothing and better than what you would get from your second cousin,” says Wachter.
However, he emphasises that AI should never be viewed as a substitute for professional medical care.
Enhancing, not replacing, the doctor–patient relationship
Experts suggest that AI is most valuable when used alongside traditional healthcare, rather than in place of it.
Adam Rodman, a hospitalist and researcher at Harvard Medical School, advises against using AI tools to assess emergency situations. Instead, he sees their greatest benefit in preparing for or reflecting on medical consultations.
“A good time to use a large language model is when you’re about to go see a doctor – or after you see your doctor,” says Rodman.
He explains that AI can help people better understand their condition, ask more informed questions, and make more effective use of time during appointments. This can support a more collaborative relationship between patients and clinicians.
“There are no downsides to better understanding your health,” says Rodman.
The future of AI in healthcare
Healthcare professionals broadly agree that AI is now firmly embedded within modern medicine and will continue to evolve alongside clinical practice.
“ My hope is that you might see AI as an extension of a human relationship,” says Rodman. He envisions a future in which both clinicians and patients work with AI to improve communication and navigate healthcare systems more efficiently.
However, he also raises concerns about potential unintended consequences. One particular risk is the possibility that people may receive serious or distressing diagnoses – such as cancer – directly from an AI system, rather than from a clinician.
Research suggests that when healthcare becomes more transactional or resembles a marketplace, trust in clinicians may decline.
”What I hope is that this technology can be used in a way that enhances humanity in medicine,” says Rodman “and not in a way that cuts out the doctor-patient relationship.”
Conclusion
Artificial intelligence is rapidly transforming access to health information, offering both opportunities and risks. While these tools can enhance understanding and support more informed decision-making, their limitations – particularly in how they interpret incomplete or imprecise input – mean they must be used with caution.
Ultimately, AI has the potential to strengthen healthcare delivery, but only if it is integrated in a way that supports, rather than replaces, the human relationships at the heart of medicine.
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First UK Long-Distance Robotic Surgery Connects London Surgeon with Gibraltar Patient
Key Takeaways:
- A London-based surgeon has performed the UK’s first long-distance robotic cancer surgery on a patient in Gibraltar, marking a major milestone in telesurgery
- The procedure demonstrated minimal delay and high precision, suggesting remote surgery could expand access to specialist care in underserved regions
- Patients living far from specialist centres may benefit from reduced travel, lower costs and improved continuity of care
A landmark moment in remote surgery
A surgeon based in London has carried out what is believed to be the United Kingdom’s first long-distance robotic surgical procedure, operating on a patient located approximately 1,500 miles (2,400 km) away in Gibraltar.
Professor Prokar Dasgupta, a leading robotic urological surgeon, performed a prostate removal on 62-year-old Paul Buxton. Reflecting on the experience, he said it felt “almost as if I was there”, despite the geographical distance between surgeon and patient.
For Buxton, who is living with prostate cancer, the decision to participate in the procedure was straightforward. He described it as a “no-brainer” and an opportunity to become “part of medical history”.
Expanding access to specialist care
The development of long-distance robotic surgery is seen as a potential solution to longstanding challenges in healthcare access, particularly for people living in remote or underserved regions.
Such approaches could reduce the “vast expense and inconvenience” associated with travelling for specialist treatment, while enabling patients to receive care closer to home.
This milestone builds on previous advances involving UK-based surgical teams. Earlier work included a transatlantic robotic stroke procedure conducted over a distance of 4,000 miles on a cadaver – a body donated to science – which demonstrated that long-distance surgery was technically feasible.
A patient’s perspective
Buxton, originally from Burnham-on-Sea in Somerset, has lived in Gibraltar for four decades. As a British Overseas Territory, Gibraltar has limited healthcare infrastructure, with only one hospital – St Bernard’s Hospital at Europort. Patients requiring more complex care often need to travel abroad, commonly to the United Kingdom for NHS treatment if eligible.
Following his prostate cancer diagnosis shortly after Christmas, Buxton initially expected to join an NHS waiting list. However, he chose instead to take part in the remote surgery trial.
“A lot of people actually said to me: ‘You’re not going to do it, are you?’”
“I thought, I’m giving something back here,” he said.
Buxton also highlighted the practical advantages of the approach:
“If I hadn’t gone for the telesurgery in Gibraltar, then I would have had to have flown to London, I would have had to go on the NHS waiting list, get the procedure done and I would have probably been in London for three weeks.
“So I thought: ‘This is a no-brainer’.
“It is pioneering for Gibraltar, because you don’t need to leave Gibraltar.”
Following the operation on 11 February, he reported a positive recovery, stating he was “really well looked after” and “feeling fantastic”.
How the technology works
The procedure was conducted from The London Clinic using a robotic surgical system equipped with a high-definition 3D camera and four robotic arms. These were controlled remotely via a surgical console.
The connection between London and Gibraltar was enabled through fibre-optic cables, supported by a backup 5G link. The system achieved an extremely low latency, with a delay of just 0.06 seconds, allowing for precise and responsive control.
A surgical team in Gibraltar remained on standby throughout the operation to intervene if necessary, although the connection remained stable for the duration of the procedure.
The operation utilised the Toumai Robotic System and was delivered through a collaboration between The London Clinic and the Gibraltar Health Authority.
Looking ahead: scaling telesurgery
Professor Dasgupta emphasised the broader implications of the innovation:
“This gives us the opportunity to treat patients in remote areas and smaller communities by literally being able to take the best surgeon anywhere.”
The procedure forms part of an initial series of test cases. A second operation involving a 52-year-old patient in Gibraltar was carried out on 4 March, with a further procedure scheduled for 14 March.
The upcoming operation will be live-streamed to 20,000 leading urological surgeons attending the European Association of Urology congress, highlighting the global interest in this emerging field.
Reflecting on the future, Dasgupta added:
“I think it is very, very exciting, the humanitarian benefit is going to be significant.”
Alignment with broader surgical trends
This development sits alongside wider efforts to expand the use of robotic-assisted surgery within the NHS. Current ambitions include scaling up to 500,000 robot-supported operations annually by 2035.
While the NHS is prioritising local access to robotic surgery, advances in telesurgery suggest a complementary pathway – one that could extend specialist expertise beyond physical borders and reshape how surgical care is delivered globally.
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‘Shadow AI’ on the Rise in Healthcare as Clinicians Turn to Unauthorised Tools to Improve Workflows
Key Takeaways:
- A survey of healthcare professionals found that 57% have encountered or used unauthorised artificial intelligence tools in their organisations, highlighting the growing presence of so-called “shadow AI” in healthcare settings.
- Many clinicians and administrators report using these tools to improve efficiency, analyse data, and manage administrative tasks, particularly when approved solutions or clear guidance are lacking.
- While most respondents believe AI will significantly improve healthcare within five years, concerns about patient safety, data privacy, and security risks remain widespread.
Unauthorised AI tools emerging in healthcare workplaces
A new survey suggests that artificial intelligence tools are already being used in healthcare organisations in ways that fall outside formal governance structures. According to the findings, a significant proportion of healthcare professionals have either encountered or used AI tools that have not been authorised by their employer.
The survey, conducted by Wolters Kluwer Health, gathered responses from 518 healthcare professionals, including both clinical providers and administrators. The research was carried out in December 2025 and was released publicly last week.
Overall, the findings indicate that four in ten healthcare professionals reported encountering unauthorised AI tools within their organisation, while 17% acknowledged personally using such tools.
When responses were analysed by professional role, 15% of physicians admitted to using an unauthorised AI tool, compared with 19% of administrators. In addition, one in ten respondents reported using an unauthorised AI tool in connection with direct patient care.
The report refers to the unauthorised adoption of artificial intelligence tools in professional environments as “shadow AI.”
Why healthcare staff turn to unauthorised AI
The survey findings suggest that healthcare professionals are often motivated by practical needs rather than deliberate attempts to bypass organisational policies.
According to the report:
“Clinical and administrative teams want to adhere to rules surrounding AI usage, but if the organization hasn’t provided guidance or approved solutions, they’ll experiment with generic tools to improve their workflows.”
Many respondents indicated that the absence of formal guidance or approved AI platforms has encouraged individuals to explore publicly available tools on their own.
The most frequently cited motivation for using unauthorised AI tools was the need to accelerate workflows and improve efficiency. Approximately half of respondents identified faster workflows as the primary reason for using these tools.
However, the survey also revealed differences in how clinical and administrative staff tend to use AI technologies.
Administrators were more likely to employ AI tools for operational or analytical tasks such as:
- Data analysis
- Predictive analytics
- Administrative processes
Healthcare providers, meanwhile, reported using AI for activities such as:
- Data analysis
- Patient scheduling
- Patient engagement tasks
The findings also indicate that clinicians were more likely than administrators to experiment with AI tools out of curiosity.
Governance and policy development remain uneven
The survey results highlight a notable imbalance in how different professional groups participate in the development of AI policies within healthcare organisations.
According to the report, administrators were three times more likely than clinical providers to be actively involved in developing AI governance policies.
Specifically:
- 30% of administrators reported involvement in AI policy development
- Only 9% of providers said they had participated in such efforts
This difference suggests that policy ownership around AI adoption may currently be concentrated within administrative leadership rather than clinical teams.
Administrators also reported greater familiarity with their organisation’s AI policies compared with providers, although awareness varied across both groups.
Security and privacy risks associated with “shadow AI”
The use of unauthorised AI tools raises important concerns about data security, privacy protection, and governance oversight.
The Wolters Kluwer report notes that inconsistent or unsanctioned AI usage can expose organisations to potential vulnerabilities. Without clear oversight, the integration of external AI tools may lead to data privacy violations, security breaches, or inappropriate handling of sensitive information.
To illustrate these risks, the report references a 2025 study by IBM, which found that 97% of organisations that experienced an AI-related security incident lacked adequate AI access controls.
Security incidents involving AI systems can have significant consequences, including financial losses, operational disruption, and damage to public trust.
Healthcare professionals remain optimistic about AI’s future
Despite concerns about governance and security, the survey indicates that most healthcare professionals remain broadly optimistic about the long-term role of artificial intelligence in healthcare.
Nearly 90% of respondents said they believe AI will significantly improve healthcare within the next five years. Administrators were found to be slightly more optimistic than clinical providers about the potential benefits of the technology.
At the same time, respondents recognised that AI implementation carries important risks that must be addressed.
Patient safety was identified by around half of respondents as the most significant risk associated with AI adoption.
Meanwhile, nearly half of respondents also expressed concerns about data privacy risks.
These findings suggest that healthcare professionals recognise both the transformative potential of artificial intelligence and the need for careful governance, clear guidance, and secure systems.
Addressing the rise of “shadow AI”
The report concludes that addressing the growth of shadow AI requires organisations to understand why staff are turning to unauthorised tools rather than focusing solely on restricting access.
According to the report:
“Ultimately, addressing shadow AI is not about restricting access to productivity tools. Leaders must understand why teams are using unsanctioned tools and which challenges they’re trying to solve, and then identify enterprise-level tools that can accomplish these goals safely and securely.”
As artificial intelligence becomes increasingly embedded in healthcare workflows, organisations may need to develop clearer policies, provide approved tools, and involve both clinical and administrative staff in governance decisions.
Such measures may help ensure that the benefits of AI can be realised while protecting patient safety, safeguarding sensitive data, and maintaining organisational trust.
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New Self-Aware Biosensor System Could Improve Reliability of Wearable Medical Devices
Key Takeaways:
- Researchers have developed a new biosensor monitoring system that can rapidly detect when electrodes in wearable medical devices begin to detach from the skin.
- The technology evaluates the quality of digital signals transmitted between electrodes through the body, allowing direct monitoring of electrode contact.
- Early testing shows the system can identify early signs of electrode failure that conventional monitoring approaches often miss, potentially improving the reliability of digital health monitoring.
Advances in wearable biosensing for modern healthcare
Smart biomedical technologies are increasingly shaping the future of healthcare. A growing number of digital health tools rely on skin-mounted biosensors that collect detailed physiological data directly from the human body. These devices are commonly used in applications such as heart rhythm monitoring, remote patient monitoring, and long-term health tracking.
As these technologies become more widely adopted in clinical practice and home-based healthcare settings, the accuracy and reliability of the signals they collect become critically important. If the sensors or electrodes attached to the skin begin to loosen or detach, the data captured by the device can become unreliable.
To address this challenge, a research team at King Abdullah University of Science and Technology (KAUST) has developed a new system designed to detect electrode detachment in real time. The technology enables medical devices to identify when electrodes begin to lose proper contact with the skin, allowing clinicians and users to maintain accurate physiological monitoring.
The study describing the system was published in the journal Results in Engineering.
Limitations of traditional electrode monitoring methods
Many wearable medical devices rely on electrodes placed on the skin to detect electrical signals produced by the body, such as those generated by the heart. However, ensuring that these electrodes remain properly attached throughout monitoring can be difficult.
Conventional systems typically rely on indirect methods to determine electrode integrity, such as measuring electrical impedance or using other proxy indicators. These techniques were developed many years ago and often assume stable monitoring conditions.
According to the researchers, these assumptions do not always reflect real-world use.
“Traditional methods for checking whether medical electrodes are properly attached, based on impedance or indirect monitoring, were developed many years ago and assume relatively stable conditions,” explains Rajat Kumar, a student working in the laboratory of Ahmed Eltawil, who led the research.
In everyday situations, however, people move, perspire, and change position. These normal activities can cause electrodes to loosen slightly or temporarily lose contact with the skin.
Such intermittent disruptions can be difficult for conventional monitoring approaches to detect.
“This is especially problematic for home-based wearable medical devices, where poor electrode contact may go unnoticed for long periods, leading to inaccurate data being recorded and relied upon,” says Abdelhay Ali, a postdoctoral researcher in Eltawil’s research group.
Rethinking the body as part of the monitoring system
To overcome these limitations, the KAUST team reconsidered how electrodes interact with the body during monitoring.
Instead of viewing the human body purely as a source of interference in electrical measurements, the researchers explored whether it could become part of the detection mechanism itself.
Eltawil describes this shift in perspective:
“Instead of treating the body as something that interferes with measurements, we considered whether it could be part of the solution.”
Previous research has shown that very small electrical signals can safely travel through the body. The researchers realised that this property could be used to evaluate the condition of electrode attachments.
“We realized that if electrodes could exchange digital signals through the body, then the quality of that communication would directly reflect how well the electrodes were attached,” Kumar says.
If the electrodes remain firmly attached, the signals between them would be transmitted clearly. If the electrodes begin to loosen, the signal quality would deteriorate.
How the self-aware monitoring system operates
To test this concept, the team developed a monitoring system built around a custom-designed microchip created at KAUST.
The system works by sending very small digital signals between electrodes positioned at different locations on the body. These signals pass through the body and are then received by other electrodes.
A small processing unit analyses how well the signals are received.
According to Ali, the signal quality provides a direct indication of electrode contact:
“Clear signals indicate good electrode skin contact; small errors indicate weakening contact; and missing signals indicate disconnection.”
In addition to the chip and signal-processing unit, the system includes a control component that manages the electrode-checking sequence. This allows the device to automatically evaluate multiple electrodes in sequence without interrupting the primary medical measurements being performed by the device.
Testing the system on human skin
To evaluate the system’s effectiveness, the researchers conducted experiments using electrodes placed on human skin.
The testing showed that the system could reliably distinguish between several different conditions of electrode attachment, including:
- Firmly attached electrodes
- Partially loosened electrodes
- Electrodes that intermittently lose contact with the skin
- Completely disconnected electrodes
Importantly, the system demonstrated the ability to detect early stages of contact degradation before full disconnection occurs.
“Importantly, the system detected the early signs of contact degradation that traditional methods often miss,” Kumar says.
This early detection could be particularly valuable in wearable health monitoring devices that operate continuously over long periods.
Potential benefits for long-term wearable monitoring
A key feature of the new system is its very low power consumption, which makes it suitable for wearable technologies that must operate continuously for hours or days at a time.
Ali explains that this efficiency could make the technology practical for real-world use.
“The system’s very low power consumption should enable practical integration with wearable medical devices that need to run continuously for long periods,” he says.
He also notes that the design could be incorporated into existing devices with minimal modifications.
“These components form a compact and efficient solution that can be added to existing medical devices with minimal changes.”
Towards fully integrated wearable medical devices
Although the current system has been demonstrated in laboratory testing, the research team is now working to advance the technology further.
Their next goal is to develop a fully integrated single-chip system capable of monitoring many electrodes simultaneously.
Such a system could be used in a range of clinical monitoring devices, including multi-lead electrocardiogram (ECG) monitors and other wearable biosensing platforms used in both hospital and home environments.
Eltawil emphasises the broader aim of translating the technology into practical healthcare solutions.
“Ultimately, our goal is to translate this KAUST-developed technology into practical medical devices that are more reliable, more trustworthy, and better suited for continuous health monitoring in the clinic and at home,” he says.
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Apple Watch Use in Older Adults Linked to Fourfold Increase in Atrial Fibrillation Detection, Study Finds
Key Takeaways:
- Older adults using an Apple Watch were four times more likely to be diagnosed with atrial fibrillation than those receiving standard care.
- Over half of the people diagnosed in the smartwatch group had no outward symptoms and were identified through watch alerts.
- Researchers suggest that smartwatch screening could reduce stroke risk and healthcare costs by accelerating diagnosis.
Smartwatches and fitness trackers have increasingly incorporated electrocardiogram functionality, allowing users to record heart rhythm data from their wrists. Among these devices, the Apple Watch has emerged as one of the most advanced consumer wearables in this space, capable of detecting irregular heart rhythms and, in some models, identifying indicators associated with raised blood pressure.
While these features have received certification from the US Food and Drug Administration for consumer use, the Apple Watch is not formally approved as a medical diagnostic device for clinical settings. Its purpose is to alert individuals to possible irregularities, prompting them to seek professional medical assessment for confirmation and formal diagnosis. In some cases, such alerts have led to early intervention, including reports of potentially life-saving outcomes.
New research now strengthens the case for wearable technology as a supportive diagnostic tool, particularly in detecting atrial fibrillation – a common heart rhythm disorder that can significantly increase the risk of stroke if left untreated.
Study design and participant profile
The research was conducted by investigators at Amsterdam University Medical Center. The team enrolled 437 adults aged over 65 who were considered to be at elevated risk of stroke.
Participants were divided into two groups:
- 219 individuals were provided with an Apple Watch for heart rhythm monitoring
- 218 individuals received standard care without smartwatch monitoring
All participants were assessed for atrial fibrillation, a condition that can be intermittent and frequently asymptomatic, making it challenging to detect through routine clinical encounters alone.
Fourfold increase in diagnoses
Among those using the Apple Watch, 21 individuals were diagnosed with atrial fibrillation and subsequently received medical care. Notably, 57 per cent of those diagnosed in the smartwatch group had no outward symptoms beyond what was indicated on their device.
In contrast, only five individuals in the standard care group were diagnosed with atrial fibrillation. All of these individuals were symptomatic at the time of diagnosis.
Overall, smartwatch monitoring identified four times as many people who were ultimately diagnosed with the condition compared with standard care alone.
These findings suggest that wearable devices may play a significant role in uncovering otherwise silent arrhythmias in older adults at increased stroke risk.
Clinical and economic implications
Atrial fibrillation is a well-established risk factor for stroke. Early detection allows for timely intervention, including anticoagulation therapy where appropriate, which can substantially reduce the likelihood of stroke.
Cardiologist Michael Winter of Amsterdam University Medical Center advocated for the integration of smartwatch technology into clinical pathways. He argued that the financial benefits could outweigh the initial expense of the devices.
He stated that the savings in medical services would “offset the initial cost of the device”.
Winter further explained:
“Using smartwatches with PPG and ECG functions aids doctors in diagnosing individuals unaware of their arrhythmia, thereby expediting the diagnostic process,” said Winter. “Our findings suggest a potential reduction in the risk of stroke, benefiting both patients and the healthcare system by reducing costs.”
By accelerating diagnosis in people who might otherwise remain undiagnosed until a serious event occurs, smartwatch-assisted monitoring may reduce both clinical burden and long-term healthcare expenditure.
Wearables as an adjunct – not a replacement
Despite these promising findings, the Apple Watch remains a supplementary tool rather than a replacement for clinical evaluation. Alerts generated by the device require follow-up assessment by healthcare professionals to confirm diagnosis and determine appropriate management.
However, for older adults at elevated risk of stroke, especially those who may not experience noticeable symptoms, wearable ECG and photoplethysmography technology may provide an additional layer of protection.
As consumer health technology continues to evolve, research such as this indicates that smartwatches could increasingly bridge the gap between everyday life and preventive cardiovascular care, supporting earlier detection of conditions that might otherwise go unnoticed.
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