From Years of Uncertainty to Answers: How AI Is Shortening the Rare Disease Diagnostic Odyssey
Key Takeaways:
- ThinkRare, an algorithm at the Children’s Hospital of Eastern Ontario, scans health records to flag children who may need a rare disease referral. It has led to 21 new diagnoses and a 70% success rate so far.
- At Boston Children’s Hospital, a reasoning model built by OpenAI solved all 20 known test cases and contributed to 18 diagnoses among 376 previously unresolved ones.
- Neither tool works alone. Data bias, hallucinations, and clinicians’ tendency to defer to AI mean a human in the loop remains essential.
Why a rare disease diagnosis can take half a decade
For people living with a rare disease, the route to a diagnosis can be long, circuitous, and occasionally never completed at all. Reaching an answer often demands specialist training and technical resources that many clinics simply do not have. In a News and Perspectives article for JMIR, correspondent Simon Spichak reports on how AI initiatives at one children’s hospital in the United States and another in Canada are working to close that gap, and how they could fundamentally reshape the diagnostic experience for children and families living with rare conditions.
For some people, the benefits are already tangible. AI algorithms have helped their doctors reach a diagnosis, and that diagnosis has in turn opened the door to accessibility accommodations, treatments, clinical trials, and further research into their condition. If these tools can be scaled, they could meaningfully shorten what is often described as the diagnostic odyssey – the almost five years, on average, of appointments, medical visits, and frustration that it typically takes to diagnose a rare disease.
Finding the children who are lost in the system
CHEO is helping to lead the way with ThinkRare, an algorithm that scans electronic health records and flags children who may need a referral to a rare disease specialist for further testing.
“The purpose of ThinkRare is to find the patients that aren’t coming to us, that are bouncing around, that are lost in the system,” says Ivan Terekhov, BCom, director of research informatics, AI, and technology at CHEO Research Institute.
The approach targets the very beginning of the diagnostic journey, where children are cycling through appointments without anyone joining the dots. ThinkRare has already prompted genetic sequencing and follow-up in a handful of children, leading to 21 new rare disease diagnoses and a 70% success rate to date. The researchers behind the algorithm are now working on rolling the model out to other hospitals.
Tackling the cases that leave clinicians stuck
BCH is addressing the opposite end of the odyssey, using a reasoning model built by OpenAI to help resolve cases that have defeated clinicians.
Catherine Brownstein, PhD, scientific director of the genetic investigations arm of the Manton Center for Orphan Disease Research at BCH, says she was sceptical at first. When the team fed the reasoning model rare disease cases for which the diagnosis was already known, it initially appeared to falter. Eventually it diagnosed 19 out of 20 cases correctly, and ultimately all 20.
“When we looked at the one that it got wrong,” she says, “we were wrong.” The AI had flagged a second diagnosis that clinicians had missed.
At that point, Brownstein and her team judged the model ready for the real test: helping to solve cases that remained undiagnosed. It proved remarkably successful, with its output leading to 18 diagnoses among 376 previously unresolved cases.
What does AI do that clinicians cannot?
According to a recent report, insufficient physician training may help to explain why people with rare diseases go undiagnosed for so long. ThinkRare addresses this gap by incorporating expert-curated criteria to flag rare diseases that the average doctor might miss, so that those children can be considered for genetic testing.
Even after sequencing, rare disease specialists can take a long time to arrive at a diagnosis, and complicated cases sometimes remain unsolved. Specialists have only a limited amount of time to spend with each family, and may be carrying many cases they are simply unable to revisit.
During her PhD, Brownstein spent six years working on a single family. “There’s just simply not enough geneticists in this world or scientists in this world to do the thorough, in-depth investigations that are necessary to make all the insights possible,” she says.
The reasoning model used at BCH, designed to reason carefully and show its work, was developed using o3 Deep Research, a publicly accessible model. When it identifies a potential genetic mutation that might be causing a child’s symptoms, it presents its chain of reasoning alongside the suggestion. Geneticists then take over, running the tests needed to confirm or rule out the diagnosis.
“But instead of having to go to the sequence and find that variant yourself, it’s presented to you in a way that’s much, much quicker,” says Brownstein.
The insights the model produces may also lead to discoveries that clinicians would not otherwise make. In one instance it surfaced a new genetic variant that might be implicated in vitiligo, an autoimmune condition in which the body attacks the pigment-producing cells in the skin.
“I was speechless,” Brownstein recalls. Her team now has research under way to investigate whether the mutation is causative. If that work bears out, she hopes it could point towards a new treatment.
As tools like these move closer to everyday practice, the demand for clinicians who understand both their promise and their failure modes is rising. Short, focused CPD training such as the College of Contemporary Health’s AI Essentials for Primary Care course is designed for exactly that: helping practitioners get to grips with how these systems work, where they add value, and where human judgement has to stay firmly in charge.
Why there needs to be a human in the loop
Neither model removes the clinician from the process.
ThinkRare’s algorithm is passive, flagging a small number of cases out of several hundred thousand children attending its clinics. Those children still need testing and assessment by a specialist, because the algorithm does not actually predict which condition a child may have.
And because the algorithm is built on clinical expertise and trained on electronic medical records, bias is baked in. “I think we’d be naive to say that there’s not data quality issues or there’s worse data quality for those who are not White and those who do come from rural locations,” notes Alexandre White-Brown, MSc, a genetic counsellor and project manager of ThinkRare. Canada’s diversity is nonetheless well represented within the algorithm’s training dataset, which helps to offset some of these potential issues.
While Brownstein says the reasoning model used at BCH improved rapidly, errors and hallucinations are common with any large language model (LLM). OpenAI’s reasoning model still needs to be used in collaboration with a clinician rather than as a stand-alone tool.
The risk of trusting the machine too readily
Research into human-AI interaction has repeatedly shown that experts may defer to the judgement of an AI system, even when it is wrong.
“Previous studies show that learning from evidence is especially difficult when the evidence is poorer and uncertainty is higher, as in the case of patients with rare diseases,” says Aranzazu Viñas, PhD, an assistant professor at the University of the Basque Country. “Therefore, AI’s errors might be still more difficult to detect in the case of rare diseases.”
Her recent study suggests that doctors will trust an AI’s judgement even when they receive new clinical information showing it to be wrong. Viñas emphasises the importance of conducting “ecological research” to establish whether these errors also appear in real-world settings. Beyond that, she says it is “important to develop strategies and protocols that increase human critical thinking and detection of AI errors.”
The future of AI for rare diseases
BCH is now looking towards further study and model validation, measuring time and cost savings, clinician effort, false positives, and whether using the model actually changes the care people receive. The team is working with OpenAI to democratise access for other researchers and clinicians.
At CHEO, ThinkRare is currently able to spot the low-hanging fruit, but Terekhov points to the data silos created by information locked away in unstructured reports and doctors’ notes. The next iteration of ThinkRare will incorporate LLMs that can access and interpret this material, allowing the system to flag more cases. Scaling the model countrywide will require regulators to sign it off.
What a diagnosis changes for a family
White-Brown explains the impact ThinkRare is already having. Antony was diagnosed with an ultrarare condition called Chung-Jansen Syndrome at 10 years old, after being flagged by the algorithm. The diagnosis provided an explanation for his symptoms, and testing revealed that the same condition affected his brothers and his mother.
“The biggest thing for this family was actually being able to access resources in school that were previously unattainable,” says White-Brown.
CCH insight
AI-assisted triage, diagnostic support, and documentation tools are arriving in clinical settings faster than most training has kept pace with. The College of Contemporary Health’s AI Essentials for Primary Care CPD short course gives healthcare professionals a practical grounding in how these technologies work, how to appraise their output critically, and how to use them safely alongside clinical judgement.
Explore AI Essentials for Primary Care →
Source: JMIR Publications
Read More
When the Diagnosis Arrives by App: Why Most Patients Still Want a Human Voice
Key Takeaways:
- In a UT Southwestern survey of more than 2,400 people diagnosed with cancer, 75% said they would prefer to receive the news directly from their physician – in person or by telemedicine – rather than through an electronic patient portal.
- More than half of those who first learned of their diagnosis via the portal were alone at the time, often without support from a clinician or family member.
- Researchers are calling for a more personalised approach, including better portal notification settings, tiered or delayed release of sensitive findings, and plain-language summaries of radiology and pathology reports.
A digital convenience with an emotional cost
Electronic patient portals have transformed how quickly people can see their own test results. For routine bloodwork or a clear scan, near-instant access is a welcome convenience. But the same speed raises a difficult question for clinicians: how should a new cancer diagnosis be communicated when a patient can open the result on a phone before anyone has had the chance to talk it through?
That tension has grown sharper since 2021, when a provision of the 21st Century Cures Act came into force in the United States. The regulation requires that patients have timely, unrestricted access to their electronic health information – which, in practice, means a growing number of people are discovering a new or recurrent cancer diagnosis through their portal, sometimes with no clinician present to interpret it or answer the questions that immediately follow.
What the UT Southwestern survey found
A new survey carried out at UT Southwestern Medical Center suggests that, for most people facing a cancer diagnosis, faster is not better. The findings, published in JAMA Network Open, show that 75% of respondents would prefer to learn about a cancer diagnosis directly from their physician, whether in person or through a telemedicine appointment.
The 2025 survey gathered responses from more than 2,400 people who were diagnosed with cancer at the Harold C. Simmons Comprehensive Cancer Center between 2019 and 2023, giving the researchers a substantial real-world picture of how patients want sensitive results delivered.
According to the study’s lead author, Sheena Bhalla, M.D., Assistant Professor of Internal Medicine in the Division of Hematology and Oncology and a medical oncologist at the Simmons Cancer Center, the broad enthusiasm for digital access does not extend neatly to oncology. “While most patients in the general population appreciate rapid electronic access to test results, the situation for patients with cancer is much more nuanced,” she said. “Learning about a cancer diagnosis without the ability to immediately ask questions or discuss next steps with a trusted clinician can add to the significant stress, uncertainty, and fear that patients experience.”
Preferences are not one-size-fits-all
The survey also revealed that there is no single right way to share a result. Preferences varied according to people’s prior experiences, how frequently they used their portal, and their demographic characteristics. Men, for instance, were more likely than women to prefer learning of a diagnosis through the portal.
For senior author David Gerber, M.D., Professor of Internal Medicine in the Division of Hematology and Oncology and of Epidemiology in the Peter O’Donnell Jr. School of Public Health, and co-Director of the Simmons Cancer Center Office of Education and Training, that variation is precisely the point. “These findings highlight the need for a more personalized, tailored approach to communicating sensitive and life-changing results,” he said. “Moving beyond a one-size-fits-all approach can help clinicians provide a more thoughtful, compassionate patient experience.”
The hidden consequence: facing the news alone
Perhaps the most striking insight concerns the circumstances in which people are receiving these results. Among those who learned of their diagnosis through the portal, more than half reported that they were alone when they read it.
Dr Bhalla described this as one of the most troubling side effects of real-time access. “That’s one of the most unintended consequences of real-time access,” she said. “Patients are often alone without support from their physician or family at one of their most vulnerable moments.”
Possible solutions for clinicians and health systems
The researchers are clear that the answer is not to roll back access, but to design around it more thoughtfully. They point to several potential measures, including raising awareness among both clinicians and patients of the portal notification settings already available; developing tiered or delayed-release approaches for particularly sensitive findings; and integrating supportive digital tools such as plain-language summaries for radiology and pathology reports.
Policy is beginning to catch up. Since the Cures Act took effect, three states – including Texas – have enacted laws permitting the delayed portal release of cancer-related and other sensitive results, giving care teams a window to reach out before a patient is left to interpret difficult news on their own.
Looking ahead
For the study’s authors, the work is a starting point rather than a conclusion. “Further study and increased interdisciplinary collaboration among oncology clinicians, health services researchers, and digital health experts can help us better understand how patients receive and react to cancer diagnoses,” Dr Bhalla said. “Our goal is to increase awareness of this issue and help drive innovative approaches to patient-centered communication.”
Source: UT Southwestern Medical Center
Read More
Italy Officially Recognises Obesity as a Chronic Disease under New Law
Key Takeaways:
- Italy becomes the first country to officially recognise obesity as a chronic, progressive, and relapsing disease under national law.
- The new legislation ensures access to prevention, early diagnosis, and treatment through the National Health Service, supported by dedicated funding and professional training.
- The law marks a cultural and clinical milestone, combating stigma and embedding obesity care into multidisciplinary health and social policies.
A landmark in public health policy
Italy has taken a historic step forward in the recognition and treatment of obesity. Following final approval in the Senate, a new law formally recognises obesity as a chronic, progressive, and relapsing disease, fully incorporated within the National Health Service (Servizio Sanitario Nazionale, SSN). This recognition places obesity among the conditions requiring prevention, early diagnosis, and integrated care, marking a fundamental shift in how the nation addresses one of its most pervasive health challenges.
Until now, obesity—despite its well-documented health, social, and economic burden—was often perceived as an “individual problem” tied to lifestyle choices and personal responsibility. This new law redefines that narrative. It positions obesity as a medical condition requiring structured clinical and social support, thereby recognising the biological, psychological, and environmental factors that contribute to it.
Leadership and legislative consensus
The legislation was spearheaded by Hon. Roberto Pella, President of the Interparliamentary Group on Obesity, Diabetes and Non-Communicable Diseases (NCDs), who has long advocated for stronger prevention policies, enhanced patient rights, and healthier urban environments.
The final version of the bill approved by the Senate mirrors that previously passed by the Chamber of Deputies, reflecting broad cross-party agreement on the urgent need to address obesity as a national health priority.
Key provisions of the law
The new law introduces a comprehensive framework of measures that address obesity through prevention, treatment, research, and social inclusion.
- Access to Essential Levels of Care (LEA): People living with obesity will now have guaranteed access to diagnostic and therapeutic services under the National Health Service, ensuring equitable treatment and continuity of care.
- National Programme for Prevention and Care: Dedicated funding will progressively increase from €700,000 in 2025, to €800,000 in 2026, and €1.2 million annually from 2027 onwards. These resources will support prevention campaigns, public awareness efforts, anti-stigma initiatives, and projects promoting social inclusion.
- Health Promotion: The law prioritises preventive health measures, including nutrition education in schools, support for breastfeeding, and programmes encouraging regular physical activity across all age groups.
- Social Inclusion: New provisions promote the full participation of people living with obesity in society—whether in the workplace, education, or recreation—by addressing structural barriers and discrimination.
- Training for Health Professionals: From 2025, €400,000 annually will be invested in training doctors, paediatricians, psychologists, and other healthcare professionals to improve clinical understanding of obesity and strengthen multidisciplinary care.
- National Observatory for the Study of Obesity (OSO): A new Observatory will be established within the Ministry of Health to oversee the implementation of the law. It will monitor outcomes, coordinate research, and present an annual report to Parliament, ensuring transparency and accountability.
- Awareness Campaigns: A permanent fund of €100,000 per year will support public initiatives promoting balanced nutrition and physical activity. Schools, pharmacies, physicians, and local authorities will play key roles in these campaigns.
Cultural and clinical significance
The adoption of this law represents a dual victory—both cultural and medical. Culturally, it challenges the long-standing stereotypes and stigma that have surrounded obesity, reframing it as a health condition rather than a personal failing. It acknowledges the dignity and rights of people living with obesity and underscores the need for inclusion and respect in both healthcare and everyday life.
Clinically, the law ensures equal access to prevention and treatment and formally integrates obesity into structured healthcare pathways. This institutional recognition paves the way for better coordination between medical professionals, community services, and social care systems.
A multidisciplinary and cross-sectoral approach
The reform promotes a multidisciplinary model of care, encompassing prevention, education, inclusion, and research. It also aligns obesity management with broader public health objectives, connecting healthcare policy with urban planning, education, and sport.
By mandating ongoing monitoring through the National Observatory and annual parliamentary reporting, the law ensures a strong framework for governance and sustained progress.
Towards broader change
As Hon. Roberto Pella’s leadership demonstrates, this legislation is not merely a symbolic step—it establishes a concrete foundation for long-term change. The next phase will involve implementing its provisions effectively, strengthening local prevention programmes, supporting patient associations, and fostering partnerships between healthcare providers, schools, and community organisations.
Obesity is not only a disease but also a reflection of how societies live, eat, and structure their environments. Italy’s new law acknowledges this complexity. It is a decisive move toward compassion, inclusion, and evidence-based care, ensuring that people living with obesity receive the attention, respect, and medical support they deserve.
Read More
Precision Medicine Poised to Redefine Obesity Prevention and Treatment
Key Takeaways:
- Researchers at the Pennington Biomedical Research Center highlight how precision medicine could revolutionise the prevention, diagnosis, and treatment of obesity by tailoring interventions to an individual’s biology and environment.
- Significant barriers remain, including limited large-scale clinical trials, underrepresentation of diverse populations, and challenges in integrating personalised tools into clinical practice.
- Experts call for robust biomarkers, inclusive research, and policy support to make precision obesity care accessible and evidence-based.
A blueprint for personalised obesity care
A new report led by researchers at the Pennington Biomedical Research Center underscores the rapidly growing potential of precision medicine to transform how obesity is prevented, diagnosed, and treated. Published in Obesity in September, the paper titled “Precision Prevention, Diagnostics and Treatment of Obesity” brings together insights from the recent Pennington–Louisiana Nutrition Obesity Research Center (NORC) scientific workshop.
The workshop, held in April 2024, convened experts to review evidence on tailoring obesity interventions to a person’s unique biological, behavioural, environmental, and social characteristics. The resulting report presents both the opportunities and obstacles in implementing precision-based strategies in obesity care.
Understanding the multifactorial nature of obesity
The authors emphasise that obesity is not a one-size-fits-all condition. Instead, it is shaped by a complex interplay of factors including genetics, epigenetics, metabolic phenotypes, microbiome composition, and environmental exposures. These elements influence why individuals gain or lose weight differently and why some respond better to certain interventions than others.
The review highlights how understanding these factors could enable clinicians to identify subgroups of people with obesity who would benefit from specific preventive or therapeutic strategies. This approach marks a shift from broad public health recommendations towards tailored, data-driven care.
Diagnostic innovation: Towards greater precision
The report calls for improved diagnostic tools—including the development of reliable biomarkers, imaging technologies, and phenotypic classifications—to better characterise the different subtypes of obesity and related risk profiles.
By accurately identifying an individual’s obesity phenotype, clinicians may be able to predict treatment response more effectively and target interventions that align with a person’s unique biology and lifestyle. Such advances could help move beyond the current trial-and-error approach in weight management.
Treatment personalisation and the path ahead
Emerging research indicates that personalising diet, physical activity, pharmacotherapy, and behavioural interventions according to an individual’s biological and psychosocial characteristics may improve both efficacy and long-term sustainability of outcomes.
However, the authors caution that while enthusiasm for precision-based treatment is growing, more robust clinical evidence is essential before these approaches can be fully integrated into standard care.
“Despite tremendous interest in precision-based treatment, the field is still relatively young,” said Dr Corby Martin, Co-Chair of the symposium and Director of the NORC Human Phenotyping Core. “We need rigorous clinical trials to empirically determine if precision treatment is indeed better than current practices. Unfortunately, few such trials exist, and those that do are not always supportive.”
Persistent gaps and barriers
The report identifies several key challenges hindering progress in precision obesity medicine:
- Limited large-scale clinical trials validating precision approaches.
- Insufficient diversity in study populations, leading to reduced generalisability of findings.
- Inadequate cost-effectiveness data, making implementation difficult within healthcare systems.
- Integration challenges when introducing precision tools into routine clinical settings.
Addressing these barriers will be essential for translating the promise of precision medicine into meaningful clinical and public health outcomes.
Recommendations for future research and policy
To advance the field, the authors recommend:
- Conducting diverse and inclusive research to ensure results are representative across ethnicities, genders, and socioeconomic groups.
- Developing and validating robust biomarkers and imaging tools for more accurate diagnosis and monitoring.
- Running comparative effectiveness trials to determine whether precision interventions outperform current standard treatments.
- Implementing programmes and policies that make precision obesity care both accessible and affordable.
The report suggests that precision-based approaches could enhance obesity prevention by identifying people at risk earlier and tailoring lifestyle or environmental interventions to reduce progression. Moreover, by customising treatment to a person’s biological and behavioural profile, clinicians could minimise side effects, avoid ineffective treatments, and improve outcomes.
A continuing commitment to obesity research
For more than 25 years, the Pennington–Louisiana Nutrition Obesity Research Center (NORC) has convened over 100 scientists annually to explore emerging topics in obesity and nutrition science.
“Supporting 1.5-day workshops such as the ‘Precision Prevention, Diagnostics, and Treatment of Obesity’ brings top scientists and clinicians from around the world to Pennington Biomedical,” said Dr Leanne Redman, NORC Director, LPFA Endowed Chair in Nutrition, and Associate Executive Director for Scientific Education. “These reports provide a blueprint for the current state of the science and avenues for future research.”
Building a collaborative future
Dr John Kirwan, Executive Director of Pennington Biomedical, commended the team’s contribution:
“This team’s efforts in advancing precision medicine to diagnose, prevent, and treat obesity are truly commendable. At Pennington Biomedical, our work is built on strong partnerships across Louisiana and throughout the United States, strengthened through centres and institutes like the Pennington–Louisiana NORC. We are proud to collaborate with leading research institutions, universities, and healthcare systems nationwide to advance obesity research.”
As the science of precision medicine matures, the report provides a clear framework for how personalised approaches may one day redefine obesity prevention and treatment, improving outcomes for individuals and populations alike.
CCH insight:
Precision approaches to obesity prevention and treatments could massively improve outcomes for people with, or at risk of, obesity. The complex nature of the condition, with its broad range of biological, behavioural, psychological, social and environmental determinants and risk factors, means every patient is unique and requires a personalised intervention. However, this complexity of obesity also makes it difficult to characterise an individual’s obesity phenotype and predict responses to interventions – so there is still a long way to go, a lot more research is needed.
Read More
AI Model Forecasts Risk of More Than 1,000 Diseases Decades in Advance
Key Takeaways:
- Delphi-2M, a generative AI model, can predict susceptibility to over 1,000 diseases using anonymised medical records.
- The system has been tested successfully across large-scale UK and Danish datasets, showing remarkable accuracy and transferability.
- Experts say the model could transform population health forecasting within years and may eventually be adapted for personalised clinical use.
European scientists build AI model to predict long-term disease risk
European researchers have unveiled a powerful new artificial intelligence model that can predict a person’s susceptibility to more than 1,000 diseases decades before symptoms arise.
The system, known as Delphi-2M, was developed by scientists at the European Molecular Biology Laboratory (EMBL) in Cambridge. “Delphi uses a similar architecture to large language models but with key innovations to work with healthcare data,” explained Tom Fitzgerald of EMBL.
Delphi was trained on anonymised health records from 400,000 participants in the UK Biobank, a major long-term biomedical study. The researchers then validated its performance using records from 1.9 million patients in the Danish National Patient Registry.
Matching and exceeding existing prediction tools
The predictions made by Delphi-2M spanned more than 1,000 diseases and were generally comparable in accuracy to existing clinical tools that focus on specific conditions, such as the QRisk score used for cardiovascular disease risk. Results from the study were published in Nature on Wednesday.
“Our model is a proof of concept, showing that it’s possible for AI to learn many of our long-term health patterns and use this information to generate meaningful predictions,” said Ewan Birney, EMBL’s interim executive director. “We were surprised at how well the model transferred from the UK to Denmark though it had never seen a single bit of Danish data.”
Birney emphasised that turning Delphi into a clinically deployable forecasting tool could take five to ten years, but he noted that it could be used much sooner to inform public health strategies.
Population-level insights and healthcare planning
While Delphi generates predictions at the level of individual patients, its most immediate value may be in population health planning. “Although it makes predictions for each individual, it can be very useful at the population level to forecast collective healthcare needs, how many people will suffer from particular diseases such heart attacks, cancers or diabetes and what sort of treatment they need,” said Moritz Gerstung, head of AI at the German Cancer Research Center in Heidelberg and a member of the Delphi team.
The model performed best for diseases with well-understood and consistent progression patterns, such as cardiovascular disease, diabetes and sepsis (blood poisoning). It was less effective for conditions triggered by unpredictable environmental factors or for very rare congenital disorders.
Expanding to genomics and biological data
Researchers are now working to enhance Delphi by incorporating biological information, such as genomic and proteomic data. Despite this, Birney said they were “very pleasantly surprised” at how well the model performed using healthcare records alone, achieving results comparable to or better than some models that rely on genetic and protein-level data.
“I want to stress the power of the straightforward medical record,” Birney added.
The team has patented key aspects of Delphi’s approach to predicting disease risk and timing. “We are exploring whether there are commercialisation possibilities and how to do that with our respective institutions,” Birney confirmed.
Towards ethical and scalable predictive medicine
Independent experts have praised the work as an important step forward for responsible AI in medicine. “This research looks to be a significant step towards scalable, interpretable, and — most importantly — ethically responsible form of predictive modelling in medicine,” said Gustavo Sudre, professor of genomic neuroimaging and AI at King’s College London, who was not involved in the study.
He added that while the current model relies solely on anonymised health records, its architecture has been designed to handle richer data types in the future, including biomarkers, imaging and genomics.
Read More