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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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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‘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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NHS Endorses AI Notetaking to Expand Face-to-Face Patient Care
Key Takeaways:
- NHS-backed AI notetaking tools could enable clinicians to spend up to a quarter more time with patients by reducing administrative burden.
- A new national registry of approved suppliers sets standards for clinical safety, technology assurance and data protection.
- Evidence from more than 17,000 patient encounters shows increased direct patient interaction and shorter appointment times when AI-scribing is used.
NHS support for ambient voice technologies
New artificial intelligence notetaking tools supported by the NHS could allow doctors to spend up to a quarter more time with people receiving care. NHS organisations across England are being encouraged to make use of a newly published national registry of approved suppliers offering this technology.
Often referred to as ambient voice technologies, these tools capture clinician–patient conversations and use AI to generate real-time transcriptions and clinical summaries. The aim is to reduce the time clinicians spend typing notes or navigating screens during consultations, while maintaining high standards of accuracy, privacy and data protection.
By adopting these systems, clinicians could save approximately two to three minutes per patient consultation. At scale, this time saving could be redirected towards additional appointments or more in-depth conversations with people seeking care.
New national registry sets standards for safety and data protection
NHS England has published a new self-certified registry for AI notetaking technologies, listing 19 suppliers that meet national requirements. The registry requires participating suppliers to comply with established standards covering clinical safety, technological performance and data protection.
The launch follows NHS guidance issued last year, which advised NHS organisations to adopt AI notetaking tools only where they are safe, evidence-based and demonstrably beneficial for patients. The registry is intended to give local NHS teams confidence when selecting and implementing these tools.
Clinical leadership highlights potential benefits
Dr Alec Price-Forbes, NHS England National Chief Clinical Information Officer, said:
“The AI revolution is here and we want to arm our NHS staff with the latest technology, which has the potential to transform the quality, safety and experience of care patients receive, as well as improving efficiency.
“AI notetaking tools will help free up more time for clinicians to focus on their patients, rather than typing up notes or looking at a screen – enhancing the quality of consultations and improving overall patient satisfaction.
“We are working with NHS organisations to help them implement the technology safely and effectively – helping to make the NHS the most AI-enabled healthcare system in the world, as we shift from analogue to digital.”
Minister for Digital Government Ian Murray also emphasised the wider public sector impact, stating:
“AI has enormous potential to transform public services, and this is a prime example of how we can use it to make a real difference. By cutting down on admin and paperwork, we’re giving clinicians back valuable time to do what they do best – caring for patients.
“We’re committed to making the UK an exemplar for how technology can be used to improve public services. Supporting the NHS to adopt tools like these safely and effectively is a key part of that mission.”
Evidence from NHS pilots and large-scale evaluation
AI notetaking technology has already been tested across nine NHS sites, where it was shown to free up clinicians to spend nearly a quarter more time with patients. A major NHS England-sponsored study published last year found that AI-scribing technology can significantly reduce clinician workload while supporting improvements in patient care. The findings suggest that national adoption could unlock millions of pounds worth of additional clinical activity.
The study was led by Great Ormond Street Hospital for Children NHS Foundation Trust Innovation Unit, known as GOSH DRIVE. It assessed the impact of an AI-scribing tool that automatically transcribes consultations and drafts summarised clinical notes for clinicians to review and approve.
Measurable improvements across care settings
More than 17,000 patient encounters were evaluated across a wide range of NHS settings, including hospitals, GP practices, mental health services and ambulance teams. The results demonstrated a 23.5 per cent increase in direct patient interaction time during appointments when AI-scribes were used. In addition, overall appointment length fell by 8.2 per cent.
Emergency departments saw particularly notable benefits, with a 13.4 per cent increase in the number of patients seen per shift. Together, these findings indicate that AI notetaking tools have the potential to improve both the experience of people receiving care and the efficiency of clinical services when implemented safely and appropriately.
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AI-Enabled Digital Stethoscope Doubles Detection of Serious Valve Disease in Primary Care Study
Key Takeaways:
- An AI-enabled digital stethoscope more than doubled the sensitivity of detecting audible valvular heart disease compared with standard auscultation in primary care.
- The technology identified twice as many previously undiagnosed cases of moderate-to-severe disease, supporting its potential role as a screening adjunct.
- Higher sensitivity came with lower specificity, raising important considerations around false positives, referral rates, and cost-effectiveness.
Overview of the study
In a recent prospective study published in the European Heart Journal Digital Health, researchers compared the diagnostic accuracy of primary care providers using conventional stethoscopes with that of a relatively novel artificial intelligence-enabled digital stethoscope. The aim was to determine whether AI-supported auscultation could improve current approaches to identifying valvular heart disease in primary care settings.
The findings showed a marked improvement in sensitivity when AI support was used. The AI system demonstrated a sensitivity of 92.3 percent for detecting audible valvular heart disease, compared with 46.2 percent for standard care (P = 0.01). Although the AI tool showed slightly lower specificity, it identified twice as many cases of previously undiagnosed moderate-to-severe disease. This pattern suggests a potential role for AI-enabled auscultation as a screening adjunct rather than a replacement for clinical judgement and assessment.
Background
Valvular heart disease is a serious cardiac condition in which one or more of the heart valves, including the aortic, mitral, tricuspid, or pulmonary valves, fail to open or close properly, disrupting normal blood flow through the heart.
People living with valvular heart disease may experience symptoms such as shortness of breath, fatigue, chest pain, and palpitations. Prevalence increases with age and is estimated to affect more than half of adults aged over 65 to some degree, although moderate-to-severe disease is considerably less common.
Diagnosis remains challenging, in part because more than half of people with clinically significant disease are asymptomatic. Traditionally, detection relies on clinician-performed cardiac auscultation. However, previous research indicates that even experienced general practitioners may have limited sensitivity when screening asymptomatic individuals, contributing to delayed diagnosis and disease progression.
Study design and methods
The study investigated whether deep learning algorithms, combined with digital acoustic recordings, could improve the detection of cardiac abnormalities that may be missed during routine examinations.
This was a prospective, single-arm diagnostic accuracy study conducted across three primary care clinics between June 2021 and May 2023. The study included 357 participants aged 50 years and older who were considered at elevated cardiovascular risk but had no prior diagnosis of valvular heart disease or a known cardiac murmur.
Risk factors included hypertension, a body mass index of 30 or higher, diabetes, hyperlipidaemia, atrial fibrillation, previous myocardial infarction, stroke or transient ischaemic attack, coronary revascularisation, or other established cardiovascular disease.
Each participant underwent two independent screening protocols:
- Standard-of-care screening: Primary care providers performed four-point cardiac auscultation using conventional stethoscopes.
- AI-augmented screening: Study coordinators recorded phonocardiogram data using a digital stethoscope. These recordings were analysed by an AI algorithm that has received clearance from the US Food and Drug Administration to detect heart murmurs.
All participants subsequently underwent echocardiography to confirm the presence or absence of structural heart disease. An independent expert panel reviewed the digital audio recordings to verify whether an audible murmur was present. This panel was blinded to the AI results.
For the purposes of the study, audible valvular heart disease was defined as moderate-to-severe disease confirmed on echocardiography together with an expert-confirmed audible murmur. This definition acknowledged that some people with structurally significant disease may not produce a clearly audible murmur.
Study findings
The AI-augmented system substantially outperformed standard auscultation in detecting audible valvular heart disease. Sensitivity was 92.3 percent with AI support compared with 46.2 percent using standard-of-care screening (P = 0.01).
Among people with confirmed disease, standard examination missed seven of thirteen cases, whereas the AI system missed only one. In terms of previously undiagnosed moderate-to-severe valvular heart disease, the AI tool identified 12 cases, compared with 6 detected by primary care providers.
This improvement in sensitivity was accompanied by reduced specificity. The AI system demonstrated a specificity of 86.9 percent, compared with 95.6 percent for clinicians using conventional auscultation (P < 0.001), resulting in a higher number of false-positive findings.
When echocardiography alone was used as the reference standard for moderate-to-severe disease, regardless of whether a murmur was audible, the AI system continued to outperform standard care. Sensitivity in this analysis was 39.7 percent for the AI system versus 13.8 percent for clinicians (P = 0.01).
Interpretation and conclusions
The findings suggest that integrating AI-enabled digital stethoscopes into primary care could substantially improve the detection of valvular heart disease compared with traditional auscultation alone. Rather than replacing clinical assessment, these tools may provide an additional layer of screening support, helping clinicians identify people who may benefit from earlier referral and further investigation.
However, improved detection does not automatically translate into better clinical outcomes. The study assessed diagnostic accuracy but did not evaluate downstream management, patient experience, or long-term prognosis.
Several authors reported affiliations with the device manufacturer, a factor that should be considered when interpreting the results, despite transparent disclosure of conflicts of interest.
The lower specificity observed with AI-augmented screening may lead to increased referrals for echocardiography and higher healthcare utilisation. This highlights the importance of future research examining cost-effectiveness, workflow impact, and optimal integration into primary care pathways.
Study limitations included a modest sample size, a limited geographic scope, incomplete demographic detail, and the absence of systematic symptom assessment. Despite these constraints, the results indicate that AI-supported auscultation may represent a meaningful advance in point-of-care cardiac screening for people at increased cardiovascular risk.
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AI-Enabled Social Robots Show Early Promise for Patient and Clinician Acceptance
Key Takeaways:
- A pilot study suggests that a GPT-controlled social robot is acceptable to both patients and healthcare professionals in a hospital setting.
- The research focused on technical, organisational and ethical feasibility, rather than on demonstrating improvements in care quality.
- Careful system design, including restricting information sources to clinician-validated content, was central to building trust and reducing risk.
Early insights into acceptance and feasibility
Researchers from University of Twente, Medisch Spectrum Twente and Politecnico di Milano have conducted a pilot study examining whether a GPT-controlled social robot could support people receiving care with medical information in a hospital environment. The initial findings suggest cautious optimism. Both patients and caregivers found the technology acceptable in practice.
The study examined not whether such a system improves clinical outcomes, but whether it can function safely and appropriately within real healthcare settings. Technical robustness, organisational fit and ethical considerations were all central to the research design.
Healthcare systems are facing sustained pressure from workforce shortages and rising demand. At the same time, clear, accessible communication remains essential, particularly for people living with chronic conditions. Digital tools may help address these challenges, but they also raise important questions around reliability, trust and governance.
The findings have been published in the journal Frontiers in Digital Health.
Exploring artificial intelligence with a physical presence
Within this context, the research team investigated whether a social robot, powered by GPT technology, could provide people receiving care with information about their condition and treatment. The system combined a physical robot with a human-like face, facial expressions and speech capabilities, enabling natural spoken interaction.
According to the study, this physical presence was well received by both patients and healthcare professionals. People described the conversations as accessible and pleasant. However, the researchers were careful to frame these findings appropriately.
“This should not be interpreted as evidence that care quality improves,” emphasised lead researcher Jan-Willem van ‘t Klooster. “We investigated whether such a system can function in practice, not whether it already improves care.”
Tested in real clinical settings
The research began with a controlled laboratory study before moving into everyday clinical practice. In total, 21 people with osteoarthritis and seven healthcare professionals interacted with the robot in the hospital setting. Both groups rated the system positively in terms of usability and overall acceptance.
Van ’t Klooster highlighted the importance of this early step. “Acceptance is a first step. Then you can investigate whether such a technology really contributes to better information provision, therapy adherence or time savings for health care providers.”
Managing risk through controlled use of AI
A key aspect of the project was how artificial intelligence was implemented. The GPT system did not have unrestricted access to the internet. Instead, it was limited to information drawn from pre-approved, clinician-validated medical websites. This approach was designed to reduce the risk of incorrect or fabricated responses, often referred to as hallucinations.
“The debate is often about whether you should use AI in health care,” said Van ’t Klooster. “We show that it is mainly about how you set it up. By setting clear boundaries, control remains in the hands of health care professionals.”
Collaboration across disciplines
The project brought together expertise from behavioural science, clinical practice, design and technology. Alongside researchers from the University of Twente, healthcare professionals, designers and international partners contributed to the study.
“It is precisely this collaboration that makes this kind of research possible,” Van ’t Klooster noted.
The authors stress that further work is needed before such systems could be considered for broader implementation. Planned follow-up research includes examining long-term use, knowledge transfer and the appropriate language level for patient communication, ensuring that future applications remain accessible, safe and trustworthy for people receiving care.
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AI Tools Show Promise for Improving Diagnostics and Outcome Prediction in Resource-Limited Health Care Settings
Key Takeaways:
- Transfer learning enables AI models trained in data-rich settings to be safely adapted for use in health care systems with limited local data, improving predictive performance without rebuilding models from scratch.
- In a Vietnam case study, an adapted AI model substantially improved prediction of neurological recovery after cardiac arrest compared with the unadapted model.
- Wider adoption of AI in low- and middle-income countries will require targeted skills development, infrastructure support and robust global governance frameworks.
Reducing uncertainty after cardiac arrest in constrained settings
After a cardiac arrest, families and clinicians are often confronted with profound uncertainty about a person’s chances of neurological recovery. This uncertainty is particularly acute in hospitals with limited resources, where access to advanced diagnostics and large datasets is constrained.
Researchers from Duke-NUS Medical School and collaborators have demonstrated how artificial intelligence can help address this challenge. By adapting an advanced AI model, the team improved the accuracy of neurological outcome prediction following cardiac arrest in a resource-limited setting.
Published in npj Digital Medicine, the study focused on the use of transfer learning, an AI technique that adapts models pre-trained on large datasets to new environments with limited local data. This approach can enhance performance in new contexts without requiring extensive and costly data collection, making it particularly relevant for low- and middle-income countries.
Adapting a high-income model for local use
The researchers began with a brain-recovery prediction model developed in Japan using data from 46,918 people who experienced out-of-hospital cardiac arrest. They then adapted this model for use in Vietnam, where it was tested on a smaller cohort of 243 patients.
The adapted model performed markedly better than the original model when applied directly to the Vietnamese data. It correctly distinguished between people at higher and lower risk of poor neurological outcomes approximately 80 percent of the time, compared with around 46 percent accuracy when the original, unadapted model was used.
Senior author Associate Professor Liu Nan, from Duke-NUS’ Center for Biomedical Data Science and Director of the Duke-NUS AI + Medical Sciences Initiative, said,
“The study shows AI models do not need to be rebuilt from scratch for every new setting. By adapting existing tools safely and effectively, transfer learning can lower costs, reduce development time and help extend the benefits of AI to health care systems with fewer resources.”
Expanding AI’s role beyond outcome prediction
Beyond predicting outcomes after cardiac arrest, AI has potential applications across a broad range of health care needs in low- and middle-income countries. In a separate study published in Nature Health, Duke-NUS researchers and collaborators, including colleagues from University College London, explored how large language models, trained on extensive text data to understand and generate human language, could support global health.
In resource-constrained environments, such tools may improve access to care, diagnostics and clinical decision-making. Examples highlighted by the researchers included a chatbot providing pregnancy-related information to expectant mothers in South Africa and smartphone-based applications used by community health workers in Sierra Leone to detect malaria infections from blood smear samples. These approaches offer more cost-efficient alternatives to conventional microscope-based systems.
Despite these advances, the researchers noted that AI development and deployment remain concentrated in high-income and upper-middle-income settings. While 63 percent of surveyed researchers, clinicians and service providers reported actively using AI tools, many low- and middle-income countries continue to face significant barriers, including limited infrastructure, insufficient technical expertise and a lack of locally generated evidence on how best to address these gaps.
Co-author Siegfried Wagner, from UCL Institute of Ophthalmology and Moorfields Eye Hospital NHS Foundation Trust, said,
“LLMs have the greatest opportunity to transform health care in settings where specialist physicians are scarcest, but the global health community needs to work together with some urgency to ensure the implementation of LLMs is supported in regions where adoption is most challenging.”
Dr Ning Yilin, Senior Research Fellow at Duke-NUS’ Center for Biomedical Data Science and a co-first author of the study, emphasised the importance of prioritising people when integrating AI into health care:
“Strengthening digital literacy and building confidence in using these tools will ensure AI supports, rather than disrupts, the workforce. Tailored skills-development pathways can help under-resourced workers adapt and thrive, allowing AI to uplift and add value to clinical and administrative roles.”
Charting the path forward: Governance and guardrails
While AI tools have clear potential to improve health care delivery, the researchers stressed that appropriate governance frameworks are essential to ensure safe and ethical implementation. Existing regulations for medical technologies often do not adequately address AI-specific risks, such as data privacy concerns, model hallucinations or unclear accountability for deployment and oversight.
To help close these gaps, researchers led by Duke-NUS have proposed the creation of an international consortium known as the Partnership for Oversight, Leadership, and Accountability in Regulating Intelligent Systems-Generative Models in Medicine, or POLARIS-GM.
The consortium aims to develop actionable best-practice guidance for regulating emerging AI tools, monitoring their impact, establishing safety guardrails and adapting them for use in resource-limited settings. By bringing together health care leaders, regulators, ethicists and patient groups from around the world, POLARIS-GM plans to adopt a phased approach, beginning with a review of existing research before working towards global consensus on AI governance in health care.
Dr Jasmine Ong, from the Duke-NUS AI + Medical Sciences Initiative and a Principal Clinical Pharmacist at Singapore General Hospital, and first author of the correspondence published in Nature Medicine, said,
“With clear oversight and clearly defined guidelines, health care systems can confidently leverage AI’s many strengths to improve health outcomes while steering clear of potential pitfalls.
From policymakers to patient groups, all stakeholders have a crucial role to play in making this goal a reality.”
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Walmart Unveils ‘Better Care Services’ Digital Health Platform to Expand Access to Affordable Care
Key Takeaways:
- Walmart has launched Better Care Services, a new digital health platform designed as a single access point for healthcare services, wellness tools and products.
- The platform connects customers to third-party providers, including urgent care, behavioural health services and Eli Lilly’s LillyDirect telehealth platform.
- The launch is accompanied by price reductions on wellness products, a limited-time telehealth discount and a nationwide in-pharmacy Wellness Event.
A new one-stop digital health destination
Walmart has launched a new digital healthcare platform, Better Care Services, positioning it as a streamlined, one-stop destination intended to help people navigate healthcare and wellness more easily.
In announcing the launch, the company said the platform is “designed to help empower millions of customers to take control of their health journeys with ease, transparency and confidence.” The initiative reflects Walmart’s broader strategy to integrate digital health services with its existing retail, pharmacy and wellness offerings.
Access to third-party care and telehealth services
Better Care Services provides customers with access to a curated network of third-party healthcare providers, with an initial focus on urgent care and behavioural health services. Through the platform, people can connect to providers offering virtual care options aimed at addressing both immediate and ongoing health needs.
The platform also integrates LillyDirect, the direct-to-consumer telehealth platform developed by Eli Lilly. This allows customers to access telehealth services linked to Lilly’s digital health ecosystem through the Walmart interface.
According to a Walmart news release dated 8 January, the company is also introducing a limited-time USD 15 discount on select telehealth services with participating providers. This offer is set to begin on 15 January, reinforcing the company’s emphasis on affordability and access.
AI-powered nutrition and personalised recommendations
Beyond clinical services, Better Care Services includes a nutrition hub that uses artificial intelligence to support people in making food choices aligned with their health goals. The tool provides personalised food and recipe recommendations, drawing on Walmart’s extensive grocery range and existing digital infrastructure.
The inclusion of AI-enabled nutrition support reflects growing interest in digital tools that link dietary guidance with everyday purchasing decisions, particularly within large retail and pharmacy ecosystems.
Removing barriers to care
Kevin Host, Senior Vice President of Health and Wellness and Pharmacy at Walmart, framed the platform as a response to persistent challenges in accessing care.
“We know that when health care feels hard, many people do not get the care they need. We can fix that,” Host said.
“Better Care Services is about making wellness simple and affordable to fit into your life; we are removing barriers so more people can get the care they deserve, right when they need it.”
These comments underline Walmart’s stated aim to reduce complexity, cost and friction within healthcare journeys, particularly for people who may delay or avoid care when systems feel difficult to navigate.
Price reductions and nationwide wellness initiatives
Alongside the digital platform launch, Walmart announced plans to reduce prices on more than 1,000 wellness-focused items, spanning food, supplements and fitness products. The move is intended to complement the digital offering by addressing affordability across both services and everyday health-related purchases.
The company will also host its annual Wellness Event on 24 January at nearly 4,600 Walmart pharmacies nationwide. The in-store event is set to include free health screenings, low-cost immunisations and wellness consultations, further linking digital access with physical pharmacy-based care.
Positioning digital health within retail healthcare
Taken together, Better Care Services, price reductions and in-pharmacy events highlight Walmart’s continued efforts to position itself as a central player in retail-based healthcare. By combining telehealth access, AI-driven nutrition tools, pharmacy services and discounted wellness products, the company is seeking to create a more integrated and accessible healthcare experience for people across the United States.
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AI Giants Expand Healthcare Offerings as Anthropic and Google Unveil New Medical Tools
Key Takeaways:
- Anthropic and Google have launched new healthcare-focused AI tools, following OpenAI’s recent release of ChatGPT Health in the United States.
- The tools are designed to support people and healthcare professionals in understanding medical information, not to replace clinical decision making.
- Regulatory scrutiny remains high, particularly in the UK, amid concerns about safety, accuracy, and governance.
Growing momentum in healthcare AI
Major artificial intelligence developers are accelerating their push into healthcare, with Anthropic and Google announcing new medical AI tools shortly after OpenAI launched ChatGPT Health in the United States.
The announcements signal increasing interest from large technology companies in applying generative and multimodal AI to health data, patient engagement, and clinical workflows, while also highlighting ongoing debates around regulation, safety, and the appropriate role of AI in care delivery.
Anthropic introduces Claude for Healthcare
Anthropic has launched Claude for Healthcare, a suite of tools and resources aimed at healthcare providers, payers, and members of the public. The offering enables the use of Anthropic’s Claude AI for medical-related purposes.
In a blog post published on 11 January, Anthropic said the new integrations are “designed to make it easier for individuals to understand their health information and prepare for important medical conversations with clinicians”.
When connected to a person’s laboratory results and health records, Claude can summarise medical history, explain test results in plain language, identify patterns across fitness and health metrics, and help people prepare questions ahead of clinical appointments. The focus, according to Anthropic, is on improving understanding and readiness rather than delivering diagnoses.
Google expands medical imaging capabilities
Google has also announced the release of MedGemma 1.5, an expanded version of its open medical AI model. The updated system is capable of interpreting three-dimensional CT and MRI scans, alongside whole-slide histopathology images.
The move reflects Google’s growing emphasis on multimodal medical AI, particularly in imaging-heavy specialties, where pattern recognition and visual analysis are central to clinical decision making.
OpenAI’s ChatGPT Health and regulatory considerations
Both announcements follow the recent launch of ChatGPT Health by OpenAI earlier this month. The product can analyse people’s medical records and data from health apps to provide personalised health-related insights.
OpenAI has emphasised that the tool is not intended to replace clinical care. On its website, the company states:
“Health is designed to support, not replace, medical care. It is not intended for diagnosis or treatment.
Instead, it helps you navigate everyday questions and understand patterns over time – not just moments of illness – so you can feel more informed and prepared for important medical conversations.”
ChatGPT Health is currently only available in the United States. A spokesperson for OpenAI told Digital Health News that the company is working through local regulatory requirements that require additional compliance measures before a UK launch. They added that OpenAI often engages in advance consultations with regulators in the UK and the EU prior to introducing new products or services in those regions.
Acquisitions and safety concerns
Further underscoring OpenAI’s healthcare ambitions, the co-founder of health data startup Torch announced last week that the company had been acquired by OpenAI for more than $100 million (£75m). Torch focuses on connecting health data from a wide range of sources to provide answers to common health-related questions.
At the same time, concerns about the risks of AI-generated health information remain prominent. Google recently removed some of its AI health summaries after an investigation by The Guardian found that people were being put at risk of harm due to misleading information. In some cases, summaries reportedly omitted critical safety details, including side effects and allergy warnings.
Calls for regulation and human-led care
Commenting on the rapid expansion of generative AI in healthcare, Euan McComiskie, health informatics lead at the Chartered Society of Physiotherapists, warned that governance frameworks have yet to catch up with technological development.
“These platforms are also not yet governed by any regulatory, strategic nor policy authority as is the case with our existing healthcare provider organisations,” he said.
“Until those issues are resolved, it is unlikely that generative AI platforms will entirely replace the human-led healthcare interactions.
“An AI-supported, human-led healthcare organisation can use multiple tools and platforms to operate efficiently, deliver high-quality healthcare whilst also enhancing the trusting and caring relationships that registered healthcare professionals have with the people we work with.”
UK regulator urges caution
Regulatory bodies in the UK have also urged caution. In November, the Medicines and Healthcare products Regulatory Agency advised that AI chatbots should not replace advice from healthcare professionals. The guidance followed research indicating that one in four people in the UK are turning to AI tools and social media for health guidance.
As major technology companies continue to invest in healthcare AI, the balance between innovation, safety, and regulation is likely to remain a central issue for policymakers, clinicians, and the people these technologies aim to support.
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Mayo Clinic Study Uses AI and CT Imaging to Identify Midlife Risk of Falls
Key Takeaways:
- Artificial intelligence applied to routine abdominal CT imaging can identify adults at increased risk of falls as early as midlife.
- Muscle density, a marker of muscle quality, is a far stronger predictor of fall risk than muscle size.
- Abdominal muscle health and core strength appear to play an important role in physical function and fall prevention across adulthood.
AI reveals early markers of fall risk
Researchers at Mayo Clinic have demonstrated that artificial intelligence applied to abdominal imaging can help predict which adults are at higher risk of falling, even from middle age onwards. The study, published in Mayo Clinic Proceedings: Digital Health, highlights abdominal muscle quality as a key predictor of future falls among adults aged 45 years and older.
Falls remain a leading cause of injury, particularly in older populations. However, the research team found that early indicators of fall risk may already be visible in CT scans that many people undergo for unrelated clinical reasons. This raises the possibility of identifying and addressing fall risk much earlier in the life course.
Using CT imaging beyond its original purpose
Working alongside radiology bioinformatics specialists, the researchers examined whether AI-derived measurements from abdominal CT scans could uncover subtle physical changes associated with falls. These measurements included fat distribution, muscle size, muscle density and indicators of bone quality.
Their analysis showed that muscle density, rather than muscle size, was most strongly associated with fall risk. Muscle density reflects muscle quality and the degree of fat infiltration within muscle tissue, whereas muscle size simply measures overall volume.
Muscle density matters more than muscle size
“Muscle size is just a measure of how big your muscles are,” says lead author Jennifer St. Sauver, an epidemiologist at Mayo Clinic in Rochester. “Muscle density is different; on a CT scan, it’s a measure of how ‘dark’ and homogenous the muscles are.”
Dr. St. Sauver explains that more homogenous muscles tend to be denser and contain less fat. This distinction is clinically meaningful, as muscle quality is more closely linked to physical strength and function than size alone.
“Previous studies have suggested that muscle density, not size, is more strongly associated with physical strength and function,” she says. “Our results support the idea that we should be focusing on muscle density, not muscle size, when we try to understand physical function.”
Strong associations seen even in midlife
While the research team anticipated finding associations between poorer abdominal muscle measures and falls among older adults, they were surprised by how pronounced these relationships were in middle-aged adults. The strength of the association suggests that meaningful declines in muscle quality may begin earlier than traditionally recognised and that these changes can significantly predict future fall risk.
“Leg muscles have been associated with physical function, but our findings show that abdominal muscles also play a significant role,” Dr. St. Sauver says.
Implications for lifelong core strength
The findings reinforce the importance of maintaining core strength and muscle quality throughout adulthood, not only in later life. According to the researchers, prioritising abdominal muscle health may offer long-term benefits for balance, stability and physical independence.
“One of the most important messages from this research is to keep your abdominal muscles in the best shape possible,” Dr. St Sauver says. “Doing so may provide benefits that start in midlife and continue well into older adulthood.”
Together, these results suggest that AI-enhanced imaging could one day support earlier identification of people at increased risk of falls, allowing preventative strategies to be introduced well before injuries occur.
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Worldwide Use of Wearable Healthcare Technology Could Rise Nearly 42-Fold by 2050, Study Finds
Key Takeaways:
- Global use of wearable healthcare devices could rise almost 42-fold by 2050, reaching close to two billion units annually.
- Non-invasive continuous glucose monitors are projected to dominate the market, accounting for nearly three-quarters of all wearable healthcare devices by mid-century.
- Without changes in design and manufacturing, this growth could carry a substantial environmental cost, including rising carbon emissions, ecotoxicity, and electronic waste.
Rapid global expansion of wearable health technologies
The global use of wearable healthcare technologies is projected to increase dramatically by 2050, according to a new analysis conducted by researchers from Cornell University and the University of Chicago. The study estimates that annual consumption of wearable health devices could approach two billion units worldwide by mid-century, representing an almost 42-fold increase compared with current levels.
The analysis, published in the journal Nature, focuses on a range of wearable healthcare technologies, including continuous glucose monitors, electrocardiogram (ECG) devices, blood pressure monitors, and point-of-care ultrasound patches. While these technologies offer significant potential benefits for clinical monitoring and disease management, the researchers warn that their rapid expansion could come with a sizable environmental footprint if sustainability is not addressed early in the innovation process.
Environmental impact and carbon emissions
The researchers estimate that the projected global use of wearable healthcare devices could generate approximately 3.4 metric tonnes of carbon dioxide equivalent emissions each year by 2050. In addition to greenhouse gas emissions, the study raises concerns about increasing ecotoxicity and the accumulation of electronic waste associated with large-scale deployment of these devices.
China is expected to contribute the highest share of annual greenhouse gas emissions linked to wearable healthcare electronics by mid-century, followed by India. These projections reflect both population size and anticipated growth in access to digital health technologies, particularly in rapidly developing economies.
Life cycle assessment of wearable devices
To quantify environmental impacts, the researchers used a life cycle assessment approach, examining each stage of a device’s lifespan. This included raw material extraction, component manufacturing, device assembly, use during its operational life, and eventual disposal.
Their analysis found that a single wearable healthcare device can emit between 1.1 and 6.1 kilograms of carbon dioxide equivalent over its lifetime, depending on the type of device and its specific design characteristics. Differences in sensing technology, materials, power requirements, and expected duration of use all influenced the overall environmental burden.
Devices included in the analysis
Four representative wearable healthcare devices were assessed in detail:
- A non-invasive continuous glucose monitor
- A continuous electrocardiogram (ECG) monitor
- A wearable blood pressure monitor
- A point-of-care ultrasound patch
These devices were selected based on their clinical relevance, diversity of sensing modalities, and representation of different stages of technological maturity within the wearable health sector.
Shifting market dynamics towards continuous glucose monitoring
At present, the wearable healthcare market is largely dominated by continuous ECG and blood pressure monitoring devices. However, the study projects a major shift in device usage patterns over the coming decades.
By 2050, non-invasive continuous glucose monitors are expected to account for approximately 72 percent of global wearable healthcare device use. Continuous ECG monitors are projected to represent 19 percent of usage, while blood pressure monitors are expected to make up around eight percent.
The researchers noted that by mid-century, annual global sales of non-invasive continuous glucose monitors alone could exceed current worldwide smartphone sales, which were estimated at 1.2 billion units in 2024.
Limited gains from bioplastics, greater potential from design changes
The study also explored potential strategies to reduce the environmental impact of wearable healthcare technologies. The researchers found that switching to recyclable or biodegradable plastics provides relatively limited environmental benefits when considered across the full device lifecycle.
In contrast, more substantial reductions in emissions could be achieved by replacing critical-metal conductors, optimising circuit architectures, and improving overall electronic design. Importantly, these changes could lower environmental impacts without compromising device performance or clinical functionality.
Supporting more sustainable digital health innovation
The researchers concluded that their engineering-based framework for assessing environmental impacts across a wearable device’s lifecycle could help guide more ecologically responsible innovation in next-generation healthcare electronics.
As wearable health technologies continue to expand rapidly across global healthcare systems, the study highlights the importance of integrating sustainability considerations into design, manufacturing, and scale-up processes from the outset, rather than treating environmental impact as a secondary concern.
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AI Adoption Accelerates in UK General Practice Despite Safety and Legal Concerns
Key Takeaways:
- Nearly three in 10 GPs in the UK are already using AI tools, including generative systems such as ChatGPT, during patient consultations and for administrative tasks.
- Widespread concern remains about clinical risk, liability and data security, with most GPs warning that the current lack of national regulation leaves them exposed.
- Time saved through AI is largely used to reduce burnout rather than increase appointments, challenging policy assumptions about productivity gains.
Growing use of AI in GP consultations
Almost 30 per cent of general practitioners in the UK are now using artificial intelligence tools in consultations with patients, according to new research, despite concerns that such use could lead to clinical errors and legal action.
The findings highlight how rapidly AI has moved into everyday general practice, largely as a response to intense workload pressures. Tools such as ChatGPT are being used to support tasks including appointment summaries, elements of clinical reasoning and routine administrative work.
However, this expansion is taking place in what researchers describe as a largely unregulated environment, leaving many clinicians uncertain about which tools are safe, appropriate and compliant with NHS standards.
Findings from the Nuffield Trust and RCGP survey
The research was conducted by the Nuffield Trust thinktank and is based on a survey of 2,108 family doctors carried out by the Royal College of General Practitioners, alongside focus groups involving GPs.
In total, 598 respondents, representing 28 per cent of those surveyed, said they were already using AI in their work. Usage varied notably across demographic and geographic lines, with 33 per cent of male GPs reporting use compared with 25 per cent of female GPs. Uptake was also significantly higher in more affluent areas than in deprived communities.
The study found that AI is most commonly being used to:
- generate summaries of patient consultations
- assist with aspects of diagnosis
- support routine administrative and documentation tasks
Ministers have expressed hopes that AI could help reduce waiting times and improve access to general practice. However, the report suggests that the reality on the ground is more complex.
Concerns about risk, liability and data security
Despite the pace of adoption, large majorities of GPs, including both users and non-users of AI, expressed serious concerns about the risks involved. According to the report, clinicians fear that practices adopting AI could face “professional liability and medico-legal issues”, alongside “risks of clinical errors” and challenges relating to “patient privacy and data security”.
Dr Becks Fisher, a GP and director of research and policy at the Nuffield Trust, said the current situation falls far short of national ambitions.
“The government is pinning its hopes on the potential of AI to transform the NHS. But there is a huge chasm between policy ambitions and the current disorganised reality of how AI is being rolled out and used in general practice”, she said.
Dr Fisher added that uncertainty around regulation is undermining confidence among clinicians.
“It is very hard for GPs to feel confident about using AI when they’re faced with a wild west of tools which are unregulated at a national level in the NHS.”
Inconsistent guidance across the NHS
The report also highlights variation in local policy. While some NHS integrated care boards actively support GPs in using AI tools, others prohibit their use altogether. This inconsistency adds to confusion and reinforces concerns about accountability and governance.
Productivity gains do not translate into more appointments
In a setback for policymakers, the research found that time saved through AI adoption is not typically used to see more patients. Instead, GPs reported using that time to manage exhaustion and prevent burnout.
“While policymakers hope that this saved time will be used to offer more appointments, GPs reported using it primarily for self-care and rest, including reducing overtime working hours to prevent burnout”, the report states.
Evidence from wider academic research
Similar conclusions were reached in a separate study published last month in the journal Digital Health. That research found that the proportion of UK family doctors using AI rose from 20 per cent to 25 per cent over the course of a single year.
Dr Charlotte Blease of Uppsala University in Sweden, the study’s lead author, described the pace of change as striking.
“In just 12 months, generative AI has gone from taboo to tool in British medicine”, she said.
Like the Nuffield Trust, Dr Blease emphasised the risks of adoption without adequate safeguards.
“The real risk isn’t that GPs are using AI. It’s that they’re doing it without training or oversight.”
She added: “AI is already being used in everyday medicine. The challenge now is to ensure it’s deployed safely, ethically and openly.”
Patients increasingly turning to AI
The growing role of AI is not limited to clinicians. Healthwatch England reports that increasing numbers of patients are also using AI tools to support their healthcare, particularly when they struggle to access GP appointments.
“Our recent research shows that while patients continue to trust the NHS for health information, around one in 10 (9%) are using AI tools for information on staying healthy”, said Chris McCann, deputy chief executive of Healthwatch England.
He noted that access barriers and convenience are driving this trend, but warned about variable quality.
“There are various reasons people may turn to AI tools, including when they cannot access GP services. However, the quality of the advice from AI tools is inconsistent. For example, one person received advice from an AI tool that confused shingles with Lyme disease.”
Government response and next steps
In September, the government launched a commission to examine how AI can be used safely, effectively and within an appropriate regulatory framework across healthcare. The commission is expected to publish recommendations on governance, oversight and implementation when it reports.
Until then, the research suggests that AI will continue to spread in general practice faster than the systems designed to regulate and support its use.
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