Cutting-edge AI tool uses x-rays to foresee diabetes risk in patients
Diabetes, a condition commonly diagnosed in adults under 65, has been increasingly observed in the senior population. As the disease advances in this age group, it often brings forth complex healthcare challenges. This highlights the need for early diagnosis, especially among older adults vulnerable to either the onset or exacerbation of diabetes symptoms.
Innovations in artificial intelligence are offering new avenues for early detection of such health conditions. These AI-powered tools, especially effective when handling vast and precise datasets, are revolutionising the early diagnosis landscape.
A trailblazing AI model, pioneered by researchers at Emory University, stands out with its unique approach. This model is engineered to discern early signs of diabetes by analysing X-ray images obtained during various medical assessments. These X-rays were originally captured for diverse medical reasons such as chest discomfort, respiratory issues, or pre- and post-operative evaluations. Notably, the AI model underwent rigorous training using a whopping 270,000 X-rays sourced from nearly 160,000 individuals.
Historically, X-rays haven’t been a standard diagnostic tool for diabetes. However, this groundbreaking AI model demonstrated its proficiency in identifying correlations between the accumulation of fatty tissues in specific body regions and an increased risk of diabetes, as highlighted by the study authors.
As a next step, the research team is keen on fine-tuning the model’s accuracy. Their vision encompasses integrating this AI tool into electronic health record (EHR) systems, aiming to equip physicians and healthcare providers with timely alerts on potential diabetes risks.
To put things in perspective, the Centers for Disease Control and Prevention (CDC) has estimated that an alarming 300,000 elderly individuals are diagnosed with diabetes for the first time annually. Diabetes’s prevalence is soaring, with a staggering 100% increase observed over the past three and a half decades. The Endocrine Society further reveals that nearly a third of the elderly population is grappling with diabetes.
The challenge, however, doesn’t end at diagnosis. Achieving effective diabetes management, especially in long-term care environments, poses significant hurdles. It is alarming to note that certain treatments lead to a heightened hypoglycemia risk, impacting around 35% of patients, as cited by the McKnight’s Clinical Daily. Overmedication is another pressing concern, with a considerable segment of the senior population not receiving timely medication adjustments. Amidst these challenges, the medical community is optimistic about emerging treatments such as SGLT2Is, which are on the cusp of wider adoption in long-term care settings. Furthermore, recent research has illuminated the potential benefits of kombucha tea in regulating blood glucose levels, offering a glimmer of hope in the fight against diabetes.
Read MoreG20 presidency backs WHO’s unveiling of the Global Initiative on Digital Health
In a significant announcement made during the Health Minister’s Meeting of the G20 Summit, the World Health Organization (WHO) joined forces with the G20 India presidency to introduce the Global Initiative on Digital Health (GIDH). This noteworthy event was held under the aegis of the Government of India.
Designed as an acronym pronounced “guide”, the GIDH serves a dual purpose. Primarily, it will function as a network and platform managed by WHO to bolster the execution of the Global Strategy on Digital Health spanning from 2020 to 2025. Furthermore, WHO is entrusted with the responsibility of acting as the Secretariat, whose role is to synchronise global standards, assimilate best practices, and marshal resources. The ultimate objective is to expedite the transformation of the digital health system on a global scale.
The Director-General of WHO, Dr Tedros Adhanom Ghebreyesus, expressed gratitude towards the G20 nations and the G20 India Presidency for acknowledging WHO’s unparalleled capabilities in this sector. He underscored WHO’s dedication to this cause, emphasising, “It necessitates the collective effort of the G20, development allies, and global institutions to realise our shared vision. WHO is firmly committed to augmenting countries’ capacities, aiming to enhance the availability of reliable digital solutions. Our vision is a future that epitomises health, safety, and equity.”
India’s Union Health Minister, Dr Mansukh Mandaviya, reflecting on the event, stated, “This day will be etched in the annals of the G20 Health Working Group’s history. The member countries not only recognised a pressing priority but also collaborated fervently to bring it to fruition.” He went on to highlight that the Global Initiative on Digital Health is a pivotal achievement during India’s tenure as the G20 Presidency.
Tracing back to 2005, the inception of the WHO resolution on ehealth paved the way for the development and endorsement of the WHO Global Strategy on Digital Health. Since then, an impressive tally of over 120 WHO member nations have conceptualised and implemented a national digital health strategy or policy.
The unprecedented challenges posed by the COVID-19 pandemic underscored the potency of digital health interventions. While numerous nations leveraged digital health tools, many articulated a pressing need. Their focus shifted from mere product-centric and experimental digital health ventures to a more structured national digital health framework. This framework would encompass effective governance, comprehensive policy guidelines, and a skilled health workforce adept at selecting, maintaining, and tailoring digital health solutions.
The GIDH has charted a clear roadmap for its mission, which includes:
- Crafting well-defined, priority-centric investment blueprints for the digital health evolution.
- Enhancing the visibility and reporting of digital health assets.
- Encouraging the dissemination of knowledge and fostering collaboration across diverse geographies to catalyse growth.
- Championing unified government-led strategies for digital health governance at the national level.
- Augmenting both technical and monetary backing for the roll-out of the Global Strategy on Digital Health 2020–2025 and its subsequent phases.
In a testament to its commitment, WHO, along with its partners, declared significant pledges both in monetary terms and resources from a diverse set of stakeholders, marking the grand unveiling of the GIDH.
The promise of digital health is profound. It is viewed as a catalyst propelling improved health outcomes, aligned with the aspiration of achieving Universal Health Coverage and the health-centric Sustainable Development Goals by 2030. The myriad benefits of digital health range from empowering individuals on their health odysseys, facilitating healthcare providers in adhering to best practices and delivering exemplary care, to invigorating the entire health infrastructure through optimised supply chains and effective workforce administration.
Read MoreRevolutionary AI tool forecasts pancreatic cancer risk up to three years in advance
Ground-breaking research spearheaded by Harvard Medical School, in collaboration with the University of Copenhagen, VA Boston Healthcare System, Dana-Farber Cancer Institute, and the Harvard T.H. Chan School of Public Health, has developed an artificial intelligence (AI) instrument capable of identifying individuals at the greatest risk of developing pancreatic cancer up to three years before diagnosis, using solely their medical records.
The study, published in Nature Medicine on May 8, indicates that implementing AI-driven population screening could be a key strategy in detecting those at a high risk of pancreatic cancer earlier. This could, in turn, hasten the diagnosis of a condition often detected at advanced stages when treatment options are less effective, resulting in poorer outcomes. Pancreatic cancer, one of the world’s deadliest malignancies, is anticipated to increase its mortality toll.
At present, there is an absence of population-wide screening tools for pancreatic cancer. Targeted screenings are performed for individuals with certain genetic mutations or a family history that increases their risk of developing the disease. However, these screenings may overlook other cases not fitting these criteria, the researchers highlighted.
The study’s co-senior investigator, Chris Sander, a faculty member in the Department of Systems Biology at the Blavatnik Institute at HMS, underscored the significance of the AI tool. “Deciding who is at a high risk for a disease and would benefit from additional testing is one of the most challenging determinations clinicians have to make. The tests can be more invasive, more costly, and carry their own risks. An AI tool that accurately identifies those at the highest risk for pancreatic cancer and who would gain the most from additional tests could greatly enhance clinical decision-making.”
If implemented widely, this AI-driven method could expedite the detection of pancreatic cancer, lead to earlier treatment, and improve patient outcomes, possibly extending their life spans.
“AI-driven screening provides the opportunity to change the course of pancreatic cancer, a formidable disease that is exceptionally challenging to diagnose early and treat promptly,” said study co-senior investigator Søren Brunak, a professor of disease systems biology and research director at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen.
In this novel study, the researchers trained the AI algorithm on two separate data sets, containing a total of 9 million patient records from Denmark and the United States. They instructed the AI model to identify potential signs of pancreatic cancer risk based on the data in the records.
The model could predict patients likely to develop pancreatic cancer in the future by identifying combinations of disease codes and the timing of their occurrence. Interestingly, many of the symptoms and disease codes were not directly related to or derived from the pancreas.
The researchers evaluated different versions of the AI models for their capacity to identify individuals at a heightened risk of disease development over different timescales – 6 months, one year, two years, and three years.
Overall, each iteration of the AI algorithm proved considerably more precise in predicting who would develop pancreatic cancer than current estimates of disease incidence in the general population. The researchers proposed that the model is likely as accurate in predicting disease onset as the existing genetic sequencing tests, which are generally only accessible to a small subset of patients in data sets.
Screening techniques for certain prevalent cancers, such as breast, cervix, and prostate cancer, rely on relatively straightforward and highly effective techniques, such as a mammogram, a Pap smear, and a blood test. These methods have significantly improved the outcomes for these diseases by ensuring early detection and intervention.
In contrast, pancreatic cancer poses greater challenges and costs in terms of screening and testing. Doctors predominantly focus on family history and the presence of genetic mutations. While these are crucial indicators of future risk, they often overlook many patients.
The AI tool presents a significant advantage in its potential applicability to any patient for whom health records and medical history are available, not solely those with a known family history or genetic predisposition for the disease. This is particularly important, the researchers noted, because many patients at a high risk may not be aware of their genetic predisposition or family history.
In the absence of clear indications that a person is at high risk for pancreatic cancer and without symptoms, clinicians may understandably hesitate to recommend more sophisticated and costlier testing methods such as CT scans, MRI, or endoscopic ultrasound.
When these tests are performed and suspicious lesions are detected, the patient must undergo a procedure to obtain a biopsy. Given its deep placement in the abdomen, the pancreas is difficult to reach and easy to inflame, leading to its nickname as “the angry organ.”
The researchers advocate for an AI tool that singles out those at the greatest risk for pancreatic cancer. This would ensure clinicians are testing the correct population, while also preventing others from undergoing unnecessary testing and additional procedures.
The survival rate for those diagnosed with pancreatic cancer in its early stages is about 44 percent, five years post-diagnosis. However, only 12 percent of cases are diagnosed at this stage. The survival rate decreases dramatically to 2 to 9 percent for those with tumours that have spread beyond their origin, the researchers estimated.
Chris Sander emphasised, “Despite significant advancements in surgical techniques, chemotherapy, and immunotherapy, the survival rate remains low. Therefore, besides advanced treatments, there’s a pressing need for better screening, more focused testing, and earlier diagnosis. This is where the AI-based approach serves as the initial critical step in this process.”
For the current study, the researchers created multiple versions of the AI model and trained them on the health records of 6.2 million patients from Denmark’s national health system over a 41-year span. Of these patients, 23,985 developed pancreatic cancer over time.
During the training, the algorithm identified patterns suggesting future pancreatic cancer risk based on disease trajectories. For instance, diagnoses such as gallstones, anaemia, type 2 diabetes, and other gastrointestinal-related issues pointed to a higher risk for pancreatic cancer within three years of evaluation.
Inflammation of the pancreas was a strong predictor of future pancreatic cancer within an even shorter time span of two years.
The researchers caution that none of these diagnoses on their own should be deemed indicative or causative of future pancreatic cancer. However, the pattern and sequence in which they occur over time provide clues for an AI-based surveillance model and could prompt physicians to closely monitor or test those at elevated risk.
Next, the researchers tested the best-performing algorithm on an entirely new set of patient records it had not previously seen — a U.S. Veterans Health Administration data set comprising nearly 3 million records over 21 years, including 3,864 individuals diagnosed with pancreatic cancer.
The tool’s predictive accuracy was somewhat lower on the US data set. The researchers attributed this to the shorter collection period and the different patient population profiles in the U.S. dataset compared to the Danish dataset.
When the algorithm was retrained from scratch on the U.S. dataset, its predictive accuracy improved. This, the researchers said, underscores the importance of training AI models on high quality, rich data and the necessity of access to large representative datasets of clinical records aggregated nationally and internationally.
In the absence of globally valid models, AI models should be trained on local health data to ensure their training reflects the specific characteristics of local populations.
Read More“Digital health will just be healthcare”: Hospital chiefs predict seamless integration of healthcare and technology
Leading digital authorities within the healthcare sector foresee a more virtual, automated, and user-friendly health system in five years. Their vision includes seamless digital integration, a feature which has already begun to take shape across many hospitals, according to industry leaders interviewed by Becker’s, a leading healthcare publication.
Daniel Barchi, executive vice president and CIO of Chicago-based CommonSpirit Health, equates the evolution of digital health with the development of e-commerce, noting that just as electronic commerce became a mainstream aspect of business, so too will digital health simply become “health”. CommonSpirit, operating 143 hospitals in 22 states, is embracing this digital evolution by using its size and mission to leverage digital population health tools. These tools aggregate data to assist clinicians and patients in managing health and wellness.
Philadelphia-based Thomas Jefferson University and Jefferson Health’s executive vice president and chief information and digital officer, Nassar Nizami, expects to see a broad adoption, integration, and implementation of several technologies within the next five years. He asserts that digital health signifies a cultural revolution within traditional healthcare. The organisation is investing in enhancing its existing AI technology, which aids physicians in assessing cancer risk in lumps or nodules, stroke risk in CT scans, and the potential requirement for blood transfusions in patients. Moreover, the organisation is utilising automation in areas such as IT, human resources, sourcing and in its virtual nursing initiative. Jefferson’s telemedicine program, JeffConnect, showcases the effective use of mobile health and remote patient monitoring, and has served as a model for other healthcare systems.
Brenton Burns, executive vice president of UPMC Enterprises, points out that Pittsburgh-based UPMC is targeting increased access to care and efficiencies through automation in various departments, including call centres and scheduling. He emphasises that digital tools have enabled the healthcare provider to extend beyond traditional settings, offering care through diverse channels such as telemedicine and home visits. Accessible and interoperable data, he insists, are vital to success.
Cincinnati-based Bon Secours Mercy Health is also increasing its digital capacity, while concurrently assisting other health systems through its digital health subsidiary, Accrete Health Partners. Jason Szczuka, the organisation’s chief digital officer, describes how they are developing, investing, and partnering with industry leaders to optimise IT operations, improve patient access to care and unlock crucial data, analytics, and automation capabilities.
New Orleans-based Ochsner Health plans to expand its asynchronous virtual tools such as e-visits and e-consults, enhance its online scheduling system, and bolster its AI and remote monitoring capabilities, explains Denise Basow, MD, executive vice president and chief digital officer. The organisation is utilising technology to predict and prevent health issues, deliver personalised care, manage patients efficiently, and reduce total healthcare costs.
Orlando Health, in Florida, is investing in its foundational IT platforms, infrastructure, data, and analytics to enhance the connection between providers and patients, regardless of their geographical location. Novlet Mattis, the organisation’s senior vice president and chief digital and information officer, reveals plans for an enterprise digital platform infused with clinical decision support tools. She envisions digital health as a standard element of health and wellness management in five years, rather than a novel innovation.
Kelly Jo Golson, executive vice president and chief brand, communications and consumer experience officer at Charlotte, N.C.-based Advocate Health, affirms that their recent merger with Atrium Health and Advocate Aurora Health has enabled an acceleration in digital transformation. For Advocate Health, consumer-centricity is paramount. The strategy includes a flexible, dynamic platform that provides consistent experiences, simple scheduling, interconnected programs for remote patient monitoring, and the incorporation of 24-7 virtual access into clinical workflows.
Ardent Health Services, based in Nashville, Tenn., is endeavouring to make care easier to access, whether in-person or digital. The chief consumer officer, Reed Smith, predicts that in the future, digital health will be synonymous with healthcare, without any segregation in delivery methods. He anticipates that consumers will have more control, and healthcare providers will be able to offer more support, especially for less critical needs, as care delivery adapts to accommodate more individual, do-it-yourself approaches.
Read MoreAI shows potential as a beneficial aid in mental health treatment, UIC study indicates
A recent pilot study by researchers from the University of Illinois Chicago (UIC) brings forth promising insights into the application of Artificial Intelligence (AI) in mental health treatment. The study demonstrates encouraging correlations between the use of an AI voice assistant named Lumen and improvements in symptoms of depression and anxiety in patients, along with noticeable changes in their brain activity.
The UIC study brings hope for the inclusion of virtual therapy in addressing the existing gaps in mental health care. The limited availability of mental health professionals and unequal access to mental health services, particularly among vulnerable communities, often impede proper treatment. The application of AI could potentially circumvent these obstacles.
Dr. Olusola A. Ajilore, UIC Professor of Psychiatry and a co-author of the study, noted the urgent necessity for innovative treatment methods, especially in the aftermath of COVID-19, which resulted in a surge of anxiety and depression cases. He remarked, “This technology could serve as a bridge. It isn’t meant to supersede traditional therapy, but it could be a vital intermediary measure before someone seeks treatment.”
Lumen, which functions as a skill within the Amazon Alexa application, is the brainchild of Dr. Ajilore, Dr. Jun Ma, the senior author of the study, and their colleagues from Washington University in St. Louis and Pennsylvania State University. The National Institute of Mental Health provided a $2 million grant to support the development of Lumen.
The researchers enlisted over 60 patients for this clinical study, which focused on the effect of the application on mild to moderate symptoms of depression and anxiety. The study also looked at activity in brain areas that have been associated with the advantages of problem-solving therapy. Two-thirds of the participants engaged with Lumen through a study-provided iPad for eight problem-solving therapy sessions. The remaining participants served as a control group that did not receive any intervention.
Upon concluding the intervention, the participants who interacted with the Lumen app exhibited reduced scores for depression, anxiety, and psychological distress in comparison to the control group. Moreover, these participants demonstrated enhanced problem-solving skills and increased activity in the dorsolateral prefrontal cortex, a brain region related to cognitive control. The results showed particular promise among women and underrepresented populations.
Dr. Ma highlighted the significance of problem-solving therapy delivered through the Lumen app. He stated, “It’s about reshaping the way people perceive problems and their approach to solving them without being overwhelmed by emotions.”
A comprehensive trial comparing the efficacy of Lumen to a control group on a waitlist and patients receiving human-guided problem-solving therapy is presently underway. However, Dr. Ma emphasises that the aim of the virtual coach is not to outperform human therapists but to address the critical shortages in the mental health system.
He concluded, “Digital mental health services should be viewed as a means to bridge the gap between the supply and demand of mental health care. We need to identify innovative, effective, and safe ways to deliver treatments to individuals who might otherwise lack access, thereby filling this gap.”
Read MoreDigital Transformation in Healthcare: Navigating Challenges and Embracing Change
As medical institutions hasten their journey towards digital modernisation, many fail to address crucial transformations in key areas such as personnel, technology, cultural ethos, and procedural workflows, necessary for the success of their digital initiatives, says Kathy Narain, Chief Digital Officer at Hoag Hospital based in Newport Beach, California.
According to a 2020 study conducted by Boston Consulting Group, a reputable management consulting firm, it is observed that victorious digital transitions are fairly uncommon. Across various industries, a mere 30% of digital transformation endeavours are reportedly successful.
Ms. Narain isn’t taken aback by this statistic. “The figures aligning with success rates don’t shock me. However, the remaining 70% face a multitude of obstacles that are challenging to conquer. When an institution decides to undergo digital metamorphosis, it’s not merely about constituting a team dedicated to digital assignments. To achieve triumph, it necessitates alterations in human resources, technological infrastructure, cultural mindset, and procedural methodologies,” she explained.
Although the hurdles of digital transformation may seem formidable, Ms. Narain believes the most significant obstacles stem from areas such as leadership, outdated systems, and economic repercussions. “In the absence of endorsement from the executive panel, who are instrumental in various organisational functionalities and a transparent blueprint on how technology can bolster outcomes and cater to the future requisites of customers, transformation initiatives falter,” she stated.
The financial aspect is a significant deterrent in the pursuit of digital transformation; healthcare systems may hesitate to invest in innovative technology due to its high cost. This reluctance becomes more conspicuous as the economy wavers and hospitals grapple with declining margins.
“Transformation is expensive and time-consuming, making the investment feel like an expenditure with a return that isn’t as immediate as expected,” Ms. Narain remarked. “The capacity to adhere to the plan while still maintaining financial support for the necessary modifications is challenging for numerous organisations.”
In the healthcare realm, many hospitals and health systems are still dependent on intricate legacy systems. Investing in digital transformation implies restructuring existing workflows or procedures, which can invite resistance and pose challenges.
“Efforts to consolidate, update, and centralise technological systems and data requires a multi-year investment ridden with bouts of exasperation,” said Ms. Narain. “The ability to navigate these hurdles, while retaining the executive team’s support as it means modifying the current processes, is crucial.”
Read MoreBT’s innovative drive to digitally transform UK healthcare
Leading telecommunications entity BT aims to leverage its established presence and specialised connectivity know-how to vitalize the digital infrastructure of the UK healthcare system, and is amplifying its healthcare portfolio to meet this goal.
The corporation has spent the last two years fostering its healthcare division, instituting a clinical advisory board to guide the creation of products tailored for the NHS needs. It also initiated its Vanguard Programme, envisioned as an interactive platform that enables healthcare professionals on the frontline to test and assess technology to guarantee its compatibility with local necessities.
“Our objective is to capitalise on opportunities that complement BT’s core business of connectivity,” Neal Herman, HealthTech Director at BT’s innovation centre, Etc., shared. “Our aim revolves around connecting individuals to the appropriate care at the right moment, and every endeavour we undertake contributes towards achieving that.”
An independent unit of Etc. is testing the implementation of drone technology for medicine deliveries over BT networks, and according to Herman, drones might also be incorporated into future healthcare solutions.
Herman detailed that BT’s vision incorporates three major themes: health navigation that aids in shaping patient interactions with the healthcare system; patient flow that enables hospitals and healthcare providers to streamline patient movement within the system; and remote care.
He depicted the first category as a future where “the healthcare professional contacts you, instead of the other way around.” Health navigation is fundamentally a guiding tool that enhances digital platforms and interactive voice response (IVR) technology. Commencing at general practice, the initial point of patient access, these solutions ensure that individuals reach the suitable healthcare provider from the outset, whether it be an immediate referral to a specialist or a physiotherapy session.
Patient flow, as Herman explained, becomes effective once patients arrive at the hospital, facilitating efficient management of patient capacity by nursing staff and site managers. “It’s about offering site managers real-time data flow to track the availability of beds,” Herman noted. These solutions are presently active in northeast Essex.
The remote component of the process incorporates products that vary from wearable technology to virtual ward monitoring platforms and is currently being piloted in Warrington for patients with chronic obstructive pulmonary disease (COPD) and hypertension.
Recently, BT unveiled a virtual ward initiative that will integrate smart monitoring devices and collaborations with other service providers to link artificial intelligence (AI)-enabled virtual care platforms. This will facilitate real-time health data capture and evaluation of patient conditions in care homes, community nursing, and virtual wards.
“BT excels at implementing technology on a grand scale and we possess a significant privilege to contribute,” said Professor Sultan Mahmud, BT’s Healthcare Business Director.
Having previously served as the chief innovation, integration, and research officer at Royal Wolverhampton Hospitals NHS Trust (RWT), Mahmud joined BT in 2021. He added, “BT is adept at bridging the translational gap. This is fundamentally about achieving technical interoperability and interoperation.” A crucial objective of interoperability, he emphasised, is to ensure that technology procurement avoids “closed systems or vendor lock-in.”
Mahmud further pointed out that the company’s strategy is reflective of its commitment to assist the NHS in addressing staffing shortages and managing waiting lists. Employed effectively, remote technology can function as a tool for staff retention and aid in easing the demand for hospital beds.
Read MoreUC San Diego health trial suggests promising role of ChatGPT in easing physician workloads
A recent study conducted by UC San Diego Health suggests that ChatGPT, a powerful AI model, may offer more empathetic responses to patients’ queries. This finding has spurred the launch of a pilot program wherein Epic and Microsoft’s generative AI technology autonomously generate responses to messages.
Dr. Christopher Longhurst, Chief Medical Officer and Chief Digital Officer at UC San Diego Health, expressed enthusiasm about the pilot programme after discussing the study results with one of Epic’s leaders. “Given our preliminary experience with ChatGPT, we decided to participate in the pilot,” he said.
The pilot program, initiated by UC San Diego Health, UW Health based in Madison, Wisconsin, and Stanford Health Care located in Palo Alto, California, marks the first instance of health systems using AI, courtesy of Microsoft and Epic, to aid physicians in addressing patients’ questions on online portals.
This nascent project is designed to alleviate the burden of documentation on physicians. Dr. Longhurst expressed concern over the increasing volume of messages doctors have to manage nationwide, underscoring the pressing need to address this issue. Excessive EHR documentation, a factor contributing to physician burnout, is reported by 57% of providers. However, generative AI and ChatGPT are indicating potential to ease this problem. “Our existing research already affirms that ChatGPT can be beneficial,” said Dr. Longhurst. “The recent integration of this AI into our clinical workflow and electronic health record system is noteworthy.”
However, while the AI’s integration is encouraging, Dr. Longhurst added that it would be deployed cautiously with clinicians reviewing all AI-generated responses before they are relayed to patients. “In the pilot, ChatGPT drafts a response to a patient’s query, which a doctor can choose to use as a starting point, modify, or opt to craft their own response,” explained Dr. Longhurst. “Every auto-generated message is accompanied by a disclaimer stating that the message was created in a secure environment and was reviewed and edited by the patient’s care team.”
Given the apprehension patients may harbour towards AI, Dr. Longhurst emphasised the importance of transparency in AI usage by UC San Diego Health. He said, “While the technology holds potential for various applications, we plan to study and pilot each application thoughtfully to ensure that it is beneficial and does not inadvertently cause harm.”
The UC San Diego Health team is scrutinising the AI for potential bias and the risk of exacerbating health inequities. They are also gathering data to determine if the tool enhances clinicians’ efficiency and if patients find ChatGPT’s responses useful. While preliminary feedback from physicians and patients has been favourable, Dr. Longhurst stated that an additional two to three months would be necessary to evaluate whether the tool truly delivers on its promises.
Read MoreThe Healthcare Industry Goes Digital: The Impact of AI and Digital Health
The healthcare industry has undergone significant changes over the past century, with the proliferation of smartphones and digital media revolutionising the way patients access medical information. In recent years, applications of Artificial Intelligence (AI) and Machine Learning (ML) have further transformed the industry, enhancing the consumer experience, healthcare delivery, and healthcare in general.
The traditional doctor-patient connection is being replaced by a wave of digital health technologies, including telehealth services, AI, and ML. These advancements are reshaping an entirely new ecosystem for technology-driven global healthcare. The Indian digital healthcare industry, for example, was worth INR 524.97 Bn in 2021, and is anticipated to grow at a CAGR of 28.50%, or INR 2528.69 Bn by 2027, according to the research “Digital Healthcare Market in India 2022-2027.”
AI and digital health are rapidly transforming the global healthcare industry in several ways, including:
Improved diagnosis and treatment: By evaluating patient data including medical histories, test findings, and imaging scans, AI-powered systems can assist doctors in arriving at more accurate diagnoses and creating more effective treatment regimens. Additionally, the AI program can recognize trends and forecast which treatments will be more efficient and appropriate for patients, ultimately improving patient outcomes.
Remote patient monitoring: Digital health technology allows medical professionals to monitor their patients’ vital signs and health status remotely, lowering the risk of readmission to the hospital. Moreover, it broadens opportunities for pharmaceutical marketers to promote prescription drugs online.
Personalised medicine: AI algorithms can analyse a patient’s genetic and clinical data to create personalised treatment plans that take into account individual responses to medications and other treatments. The availability of AI-collected patient data can significantly enhance personalised diagnosis and outcomes.
Drug discovery: AI can accelerate drug discovery by analysing vast amounts of data to identify potential drug targets and predict how various compounds will interact with the human body. The Global Artificial Intelligence for Drug Discovery Market is expected to reach around US$ 8,149 Mn by 2026 and register a CAGR of above 42% over the forecast period 2019 to 2026 as the technology holds great potential.
Healthcare operations and management: By automating administrative processes, lowering expenses, and enhancing patient access to care, digital health technology can enhance healthcare operations. The adoption of AI and ML technologies offers significant potential for automating administrative tasks and lowering operating costs as a whole.
Overall, AI and digital health are improving patient outcomes, cost, and efficiency, rapidly changing the global healthcare sector. Moreover, the recent introduction of ChatGPT has generated buzz in the industry because it allows medical professionals to provide quicker, more accurate diagnoses and treatment plans, improving patient outcomes. It also enables medical professionals to access patient data easily, allowing the creation of customised treatment plans and the delivery of better medical services.
Read MoreHealth Education England publishes roadmap for use of AI in the NHS
Health Education England (HEE) has released the first-ever roadmap outlining the use of artificial intelligence (AI) in the UK’s National Health Service (NHS) and its impact on the healthcare workforce.
The report analyses the implementation of AI and data-driven technologies in the NHS, their adoption rate, and the impact on staff. The roadmap aims to provide insight to healthcare leaders into AI policy, education, regulation, innovation, digital transformation, and workforce strategy. The report examines the timeframe for AI projects’ implementation, the distribution of AI technology in clinical areas and the workforce, the different uses of AI in healthcare, and the effects on staff and patients.
Dr Hatim Abdulhussein, clinical lead for the Digital, Artificial Intelligence and Robotics Technologies in Education (DART-Ed) programme at HEE, states that the AI roadmap is a valuable asset in understanding the AI and data-driven healthcare landscape and its implications on staff and learners. The report is a collaborative effort between Unity Insights, NICE, NHS AI Lab, and the NHS Accelerated Access Collaborative (AAC). The report also builds on the Topol review, providing a framework to identify and classify AI technology in healthcare.
The report finds that diagnostic technology, such as those used in imaging, pathology, and endoscopy, accounts for the most significant proportion of AI use in healthcare (34% share), followed by automation/service efficiency, P4 medicine, remote monitoring, and therapeutic. Of the 56 technologies estimated for large-scale deployment within a year, 77% are for use in secondary care, 23% for use in primary care, and 7% for use in community care. A total of 155 workforce groups, across 67 clinical areas, use AI tech identified by HEE, with medics in clinical radiology and general practice being the most affected, as well as non-clinical admin staff.
Abdulhussein highlights the importance of achieving transformation through emerging technology to improve patient care and scalability throughout the country, and the roadmap’s insights will focus efforts on education and training. The UK government recently unveiled its ten-year national plan to tackle cancer, including an increased use of AI and machine learning in NHS healthcare. England is also trialling a new approach to the ethical adoption of AI in healthcare.
Read MoreAI could help predict medical conditions earlier thanks to unique health data sets
Nightingale Open Science, a project launched by Ziad Obermeyer, a physician and machine learning scientist at the University of California, Berkeley, offers a unique and free medical dataset aimed at solving medical mysteries through the use of artificial intelligence (AI).
The project, which was funded with $2 million from former Google CEO Eric Schmidt, consists of 40 terabytes of medical imagery such as X-rays, electrocardiogram waveforms, and pathology specimens from patients with various conditions, including Covid-19, sudden cardiac arrest, fractures, and high-risk breast cancer. Each image is labelled with the patient’s medical outcomes, such as the stage of breast cancer, whether it resulted in death, or whether a Covid-19 patient required a ventilator.
The datasets were curated with the help of hospitals in the US and Taiwan over a period of two years, and Obermeyer plans to expand this to include more medical diversity in Kenya and Lebanon in the coming months. The key differentiating factor between these datasets and those that are available online is that they are labelled with “ground truth,” meaning that they are based on what actually happened to the patient rather than a doctor’s opinion.
The AI community has recently shifted its focus from collecting “big data” to more meaningful data, which is relevant to a specific problem and can be used to address issues such as inherent biases in healthcare, image recognition, or natural language processing. In healthcare, algorithms have been shown to amplify existing health disparities. For example, an AI system used by hospitals to allocate additional medical support for patients with chronic illnesses was found to be prioritising healthier white patients over sicker black patients who needed help, due to the use of healthcare costs as a proxy for healthcare needs.
The Nightingale Open Science project is aimed at producing high-quality datasets that are more representative of the population and can be used to root out underlying biases that are discriminatory towards underserved and underrepresented groups in healthcare systems, such as women and minorities. These diverse datasets can also help to study the spread of diseases in different cultures and locations, where people may react differently to illnesses.
The availability of high-quality, diverse medical datasets can enable AI to predict medical conditions earlier, triage better, and ultimately save lives. The Nightingale Open Science project, which provides unique and curated datasets based on actual patient outcomes, has the potential to drive progress in healthcare and reduce the impact of biases on healthcare systems.
Read MoreAI could save the healthcare industry up to $360B a year
The ChatGPT AI model is generating a buzz in healthcare as it has passed the U.S. medical licensing exam, authored scientific papers, and is being used to appeal insurance denials. However, despite research indicating the benefits of AI in healthcare, actual adoption of AI-based tools remains low.
In a new paper, researchers estimate that broader adoption of AI in healthcare could result in savings of between 5% and 10%, or roughly $200 billion to $360 billion a year. These savings could be achieved by using AI for use cases that employ current technologies attainable within the next five years, without sacrificing quality or access.
For hospitals, AI could bring cost savings by improving clinical operations, quality, and safety, such as optimising operating rooms or detecting adverse events. Physician groups could use AI for continuity of care, like referral management. Health insurers would see savings from use cases that improve claims management, like automating prior authorisation, along with healthcare and provider relationship management, including preventing readmissions and provider directory management.
Based on AI-driven use cases, private payers could save around 7% to 9% of their total costs, amounting to $80 billion to $110 billion in annual savings within the next five years. Physician groups could save 3% to 8% of costs, amounting to between $20 billion and $60 billion in savings. Hospitals could see savings between 4% to 11%, or between $60 billion and $120 billion each year, according to the report’s estimates.
While the potential benefits of AI in healthcare are significant, and the Food and Drug Administration has accelerated approvals of medical AI tools, actual adoption remains hit-or-miss. A recent study published in JAMA found a “paucity of robust evidence” to support claims that AI could enhance clinical outcomes. However, experts believe 2023 could be a turning point for adoption as more evidence around AI’s efficacy in real-world settings emerges.
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