American Medical Association Establishes Centre for Digital Health and AI to Place Physicians at the Forefront of Technological Innovation
Key Takeaways:
- The American Medical Association (AMA) has launched the Centre for Digital Health and AI to ensure physicians play a central role in shaping and integrating emerging technologies in medicine.
- The Centre will focus on policy leadership, clinical workflow integration, education, and collaboration to guide the responsible use of digital and AI tools in healthcare.
- The initiative aims to bridge enthusiasm for AI among physicians with practical strategies that safeguard data privacy, reliability, and patient-centred outcomes.
A new centre to shape the future of digital health
The American Medical Association (AMA) has announced the launch of its Centre for Digital Health and AI, an initiative designed to position physicians at the heart of digital transformation in healthcare. The Centre aims to guide the development, implementation, and regulation of technologies such as artificial intelligence (AI), ensuring they serve both clinicians and patients effectively.
While digital health tools and AI systems are progressing at an unprecedented pace, their potential can only be fully realised when developed with clinical insight. Without physician involvement, these technologies risk introducing new administrative burdens and failing to integrate meaningfully into healthcare practice.
By embedding physicians throughout the entire technology lifecycle – from concept to deployment – the AMA seeks to ensure innovations enhance clinical workflows, reduce friction in practice, and ultimately improve patient outcomes.
Physician leadership in the age of AI
“Augmented Intelligence will be a defining force in the future of health care, but right now we are barely scratching the surface of its potential. Digital health tools are everywhere and the technology has limitless opportunity, but if you don’t understand clinical practice or clinical workflow, even the best tools will never be fully implemented,” said John Whyte, MD, MPH, CEO and Executive Vice President of the AMA.
“By launching this Centre, the AMA is leading in this space so physicians have a say in the technology and clinical care of the future. Our goal is to harness innovation responsibly and effectively, so it improves patient care and reduces unnecessary burdens on physicians,” he added.
Dr Whyte’s comments highlight a key challenge within healthcare innovation: ensuring that technological progress aligns with the realities of clinical practice. The AMA’s new Centre seeks to act as both a bridge and a safeguard – connecting the rapid pace of technological development with the values and needs of medical professionals and their patients.
Key areas of focus
The Centre for Digital Health and AI will concentrate on four principal areas:
1. Policy and Regulatory Leadership
The Centre will collaborate with regulators, policymakers, and technology leaders to develop benchmarks and guidance for the safe and effective use of AI and digital health tools. This includes contributing to policy discussions around data protection, algorithmic transparency, and equitable access to digital innovation.
2. Clinical Workflow Integration
Recognising that even the most advanced tools can fail without proper clinical fit, the Centre will create opportunities for doctors to inform how AI and digital tools are designed. The goal is to ensure technologies enhance both clinician and patient experience by supporting efficiency and accuracy in clinical decision-making.
3. Education and Training
The AMA will equip physicians and health systems with the skills and knowledge needed to adopt AI responsibly. Training programmes will help clinicians understand how to interpret AI outputs, evaluate new technologies, and integrate them seamlessly into everyday practice.
4. Collaboration and Partnership
The Centre will foster partnerships across the technology, research, government, and healthcare sectors to encourage innovation that aligns with patient needs and ethical standards. This collaborative approach aims to ensure that AI applications in medicine remain grounded in clinical realities and societal priorities.
Balancing enthusiasm and caution
Recent AMA surveys reveal growing physician enthusiasm for AI’s potential in medicine. Approximately two-thirds of physicians have already incorporated AI-enabled tools into some aspect of their practice, demonstrating the rapid pace of adoption. However, the same surveys show that one in four physicians remains more concerned than excited, citing ongoing issues around data privacy, reliability, and patient safety.
The AMA’s Centre for Digital Health and AI intends to bridge this divide – helping physicians feel confident in leveraging AI while addressing legitimate concerns. By guiding ethical integration and promoting education, the AMA hopes to create a healthcare environment where digital innovation enhances both care quality and professional satisfaction.
A step towards responsible innovation
As digital transformation reshapes the healthcare landscape, the AMA’s initiative underscores the importance of responsible innovation led by clinical expertise. The Centre for Digital Health and AI represents a strategic step toward embedding physician insight into every stage of technological development – ensuring that the promise of AI and digital tools translates into real-world improvements for patients and practitioners alike.
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AI Model Could One Day Help Prevent Childhood Obesity by Counting Bites
Key Takeaways:
- Researchers at Penn State have developed an artificial intelligence (AI) system capable of counting how many bites a child takes during a meal, achieving around 70% accuracy compared to human observers.
- Eating too quickly increases the risk of obesity in children because the body has less time to register fullness, leading to overeating.
- The AI system, named ByteTrack, may in future help parents, clinicians, and researchers monitor and guide children’s eating habits in real-world environments.
AI and eating behaviours: A new frontier in obesity prevention
The faster a child eats, the greater their risk of developing obesity, according to researchers from the Penn State Department of Nutritional Sciences. However, accurately measuring bite rate—the number of bites taken during a meal—has long posed a challenge. Traditionally, this requires a researcher to watch and manually record each bite from hours of video footage, limiting most studies to small, controlled laboratory environments.
In a collaborative effort between Penn State’s Departments of Nutritional Sciences and Human Development and Family Studies, researchers have created an AI model designed to automate this process. Their pilot study, published in Frontiers in Nutrition, shows that the system is currently around 70% as effective as a human observer in counting bites. Although still under development, the researchers believe the technology could eventually help identify when a child needs to slow their eating rate or adjust their eating behaviour.
The link between eating speed and obesity
“When we eat quickly, we do not give our digestive tract time to sense the calories,” explained Professor Kathleen Keller, the Helen A. Guthrie Chair of Nutritional Sciences at Penn State and co-author of the study. “The faster you eat, the faster it goes through your stomach, and the body cannot release hormones in time to let you know you are full. Later, you may feel like you have overeaten, but when this behaviour repeats, faster eaters are at greater risk for developing obesity.”
Keller’s research group has previously demonstrated that a faster bite rate, especially when combined with larger bite size, correlates with a higher likelihood of obesity in children. Other studies have also linked larger bite size to an increased risk of choking.
“Bite rate is often the target behaviour for interventions aimed at slowing eating rate,” noted Dr Alaina Pearce, research data management librarian at Penn State and co-author of the study. “This is because bite rate is a stable characteristic of children’s eating style that can be targeted to reduce their eating rate, intake, and ultimately risk for obesity.”
Manually recording bite rate, however, is both labour-intensive and costly. As Keller pointed out, “Measuring bite rate is tedious, labour-intensive work, meaning it is expensive, which often limits the amount of data considered in bite rate studies.”
Using AI to support healthier habits
To overcome these limitations, Yashaswini Bhat, a doctoral candidate in nutritional sciences and lead author of the study, set out to develop the first AI-powered bite counter designed specifically for studying children’s eating behaviours.
“I have an interest in AI and data science, but I had never developed a system like this one,” Bhat explained.
She partnered with Associate Professor Timothy Brick, from Penn State’s Department of Human Development and Family Studies, to create a system capable of detecting children’s faces within videos and identifying when a child takes a bite.
“An experienced and knowledgeable collaborator like Dr Brick was invaluable to this project,” Bhat added.
The team trained the system using 1,440 minutes of video footage from Keller’s Food and Brain Study, funded by the National Institute of Diabetes and Digestive and Kidney Diseases. The footage featured 94 children aged seven to nine, each consuming four meals with identical foods on different occasions.
Researchers manually identified bites in 242 videos to train the AI. Once the system had been trained to recognise what a bite looks like, it was tested on an additional 51 videos. The AI’s results were then compared to those of human researchers.
Promising early results
“The system we developed was very successful at identifying the children’s faces,” Bhat said. “It also did an excellent job identifying bites when it had a clear, unobstructed view of a child’s face.”
While the AI was 97% as effective as a human observer at recognising faces, it achieved about 70% accuracy in counting bites. Bhat noted that challenges arose when children were partially obscured, turned away from the camera, or engaged in behaviours such as chewing on their spoons or playing with their food—actions common among younger participants.
“The system was less accurate when a child’s face was not in full view of the camera or when a child chewed on their spoon or played with their food, as often happens toward the end of a meal,” Bhat said. “Chewing on a utensil sometimes appeared to be a bite, and this complicated the task for the AI model.”
Next steps for the ByteTrack system
Although still in its early stages, the researchers view the pilot as an important step toward automating bite rate analysis. The system, called ByteTrack, will continue to be refined so it can distinguish between bites and similar movements such as sipping a drink.
“The eventual goal is to develop a robust system that can function in the real world,” Bhat said. “One day, we might be able to offer a smartphone app that warns children when they need to slow their eating so they can develop healthy habits that last a lifetime.”
The research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, the Penn State Institute for Computational and Data Sciences, and the Penn State Clinical and Translational Science Institute.
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Implementing AI in the NHS Proves More Complex than Anticipated, Study Finds
Key Takeaways:
- A UCL-led study revealed that introducing AI diagnostic tools across NHS hospitals faced major challenges with governance, contracts, IT integration and staff training.
- By June 2025, 18 months after contracting was meant to be completed, over one-third of trusts (23 out of 66) had not yet implemented the AI systems in clinical practice.
- Researchers recommend stronger project management, more staff education on AI, and realistic timelines to ensure successful integration.
Background to the study
A major study led by researchers at University College London (UCL) has found that implementing artificial intelligence (AI) in NHS hospitals is considerably more difficult than policymakers and healthcare leaders initially expected. The study, published in The Lancet eClinicalMedicine on 10 September 2025, examined a £21 million NHS England programme launched in 2023. The programme aimed to introduce AI technology for diagnosing chest conditions, including lung cancer, across 66 NHS hospital trusts.
The research team conducted in-depth interviews with hospital staff and AI suppliers to understand how the tools were procured, set up, and used, while also identifying both the challenges encountered and the strategies that proved helpful in supporting implementation.
Delays and barriers to implementation
The study revealed that contracting and procurement processes took between four and ten months longer than expected. By June 2025, 18 months after contracting had been scheduled for completion, one-third of the hospital trusts (23 out of 66) were still not using the AI diagnostic systems in clinical practice.
Dr Angus Ramsey, principal research fellow at the UCL Department of Behavioural Sciences and Health and first author of the study, explained:
“Our study provides important lessons that should help strengthen future approaches to implementing AI in the NHS.
We found it took longer to introduce the new AI tools in this programme than those leading the programme had expected.
A key problem was that clinical staff were already very busy – finding time to go through the selection process was a challenge, as was supporting integration of AI with local IT systems and obtaining local governance approvals.
Services that used dedicated project managers found their support very helpful in implementing changes, but only some services were able to do this.
Also, a common issue was the novelty of AI, suggesting a need for more guidance and education on AI and its implementation.”
Key challenges identified
Researchers highlighted a range of challenges that slowed or complicated implementation, including:
- Staff workload pressures – Clinicians were already under heavy demand, making engagement with selection and integration processes difficult.
- Technological integration – Many NHS hospitals operate on ageing or varied IT systems, which created barriers for embedding the new AI tools.
- Governance processes – Local approvals and oversight requirements delayed progress.
- Lack of understanding and scepticism – Many staff expressed uncertainty or caution about the reliability and usefulness of AI in clinical care.
The study found that trusts that employed dedicated project managers were more successful at managing implementation, underscoring the importance of structured leadership in rolling out new technology.
Lessons for the future
The authors of the study cautioned that although AI tools hold promise for enhancing diagnostic services, they may not resolve workforce and system pressures as easily or quickly as policymakers might hope. As they wrote:
“AI tools may offer valuable support for diagnostic services, they may not address current healthcare service pressures as straightforwardly as policymakers may hope.”
The researchers recommended that:
- NHS staff should receive training on how AI can be used safely and effectively.
- Dedicated project management should be built into large-scale AI implementation programmes.
- Timelines for introducing AI should realistically reflect the complexity of NHS structures and systems.
Professor Naomi Fulop, senior author and professor of health care organisation and management at UCL, emphasised the complexity of the task:
“The NHS is made up of hundreds of organisations with different clinical requirements and different IT systems and introducing any diagnostic tools that suit multiple hospitals is highly complex.”
Funding and next steps
The research was funded by the National Institute for Health and Care Research and conducted collaboratively by teams from UCL, the Nuffield Trust and the University of Cambridge. The group is now conducting further studies on how AI tools perform once they are more firmly embedded in NHS practice.The findings are expected to offer valuable insights for the government’s 10-year health plan, published on 3 July 2025, which identified AI as a central element in modernising and improving NHS services.
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Mayo Clinic Launches AI-Powered Nurse Virtual Assistant to Support Frontline Care
Key Takeaways:
- Mayo Clinic nurses have led the design and development of an in-house, AI-powered Nurse Virtual Assistant to streamline access to clinical information.
- The tool consolidates patient summaries, evidence-based guidelines, and clinical policies into one tab within the electronic health record, reducing administrative burden.
- More than 9,600 nurses across Mayo Clinic’s inpatient and emergency departments are now using the system, with ongoing feedback ensuring it continues to evolve with nursing practice.
Streamlining access to critical information
High-quality care depends on timely access to electronic health records, clinical policies, and evidence-based practice guidelines. However, navigating multiple systems to retrieve this information can be time-consuming for nurses, detracting from direct patient care.
To solve this challenge, Mayo Clinic’s Department of Nursing led a multidisciplinary initiative to create Nurse Virtual Assistant – a generative artificial intelligence (AI) tool developed entirely in-house by nurses for nurses. Integrated into Mayo Clinic’s electronic health record (EHR) system, the tool presents essential information within a single tab, making it easier to retrieve and act upon.
Nurses can view a curated, nurse-specific patient summary and access direct links to key evidence-based resources, including Lippincott procedures, IV administration guidelines, and Mayo Clinic’s clinical policy library – all in one place.
This streamlined interface allows nurses to spend less time searching and more time focusing on what matters most: the person receiving care.
Augmenting – not replacing – human connection
Mayo Clinic emphasises that Nurse Virtual Assistant is designed to support, rather than replace, the expertise and human presence that nurses bring to clinical practice.
“It is an amazing tool,” says Nick Flynn, a registered nurse in the Emergency Department at Mayo Clinic Hospital in Arizona. Flynn highlights the value of consolidated patient data from inpatient stays, outpatient visits, and phone calls:
“You have easy access to a history of their illness, and that is available just moments after they arrive.”
Nurse-driven innovation from concept to rollout
The project began in 2024 as part of Mayo Clinic’s strategy to ease administrative pressures in a rapidly digitising healthcare environment. Crucially, nurses were involved at every stage – from conceptualisation to design and testing – ensuring the tool meets real-world clinical needs.
Early-access users played an active role in shaping the system’s features, providing feedback that directly informed improvements.
Brendon Bloomfield, a registered nurse in Psychiatric Acute Care at Mayo Clinic Hospital – Rochester, explains:
“To see a concept I was passionate about, AI-enhanced communication, actually get built – and to be invited to help shape it – reinforces that frontline nurses’ voices matter and that we have the power to influence the future of care.”
The solution underwent a research study approved by an Institutional Review Board before scaling to more than 9,600 nurses across inpatient and emergency department units.
Evolving with nursing practice
Nurse Virtual Assistant has been released as a Minimum Lovable Product – a version that not only solves an immediate problem but is designed to be engaging and impactful for end users. Nurses can submit feedback directly through the tool, allowing the solution to continuously evolve with frontline input.
Enhancements already implemented include improved search result accuracy, refined content layouts, and new functionality based on user suggestions.
Privacy, security, and compliance at the core
Built to the highest standards of data privacy, the Nurse Virtual Assistant is a patent-pending solution developed in full compliance with HIPAA regulations and other applicable requirements. This ensures that patient information remains secure while enabling timely and efficient access for clinical teams.
Shaping the future of nursing care
Mayo Clinic’s Chief Nursing Officer, Ryannon Frederick, sees the innovation as a milestone in supporting nursing practice:
“Nurse Virtual Assistant is an example of how Mayo Clinic nurses are driving innovation and shaping the future of care. By reducing administrative burden, we allow nurses to focus on the most important part of their work: caring for patients with skill, compassion and presence.”
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Vanderbilt Researchers Use AI to Address Gaps in Long-Term Obesity Care
Key Takeaways:
- A $1 million Eli Lilly grant will fund a two-year Vanderbilt University Medical Center (VUMC) project using artificial intelligence (AI) to address gaps in obesity care.
- The initiative will analyse electronic health records (EHRs), survey patients and clinicians, and build a multi-agent AI system to develop evidence-based strategies for improving long-term engagement.
- A patient-facing mobile application will be designed and piloted in VUMC obesity clinics to support shared decision-making and sustained weight management.
Major investment in addressing gaps in care
Vanderbilt University Medical Center (VUMC) has secured a $1 million grant from Eli Lilly and Company to fund a two-year research project aimed at improving continuity of care for people living with obesity. The initiative seeks to understand why many individuals discontinue treatment and to create scalable solutions to help them stay engaged in long-term care.
“Obesity is a chronic, relapsing condition that requires ongoing management, yet too often it is treated episodically because of barriers like delayed medication access,” explained You Chen, PhD, Associate Professor of Biomedical Informatics and the project’s Principal Investigator for informatics and technology. “We’re combining data-driven insights, stakeholder input and multi-agent AI to understand where continuity breaks down and to design evidence-based interventions that keep patients engaged.”
Data-driven insights and stakeholder engagement
In its first year, the research team will analyse VUMC’s electronic health records to identify patterns distinguishing people who remain in continuous follow-up from those who disengage. Patient and clinician surveys will be conducted to capture real-world barriers to care, including logistical, financial and psychological challenges.
The findings will be integrated into a multi-agent AI system, featuring simulated physician, nurse and dietitian agents. This system will generate and prioritise strategies for maintaining engagement, which will then be reviewed by panels of clinicians, informaticians and patient representatives.
Patient-facing app to support engagement
The second year of the project will focus on designing and piloting a mobile application to be used in VUMC obesity clinics. This app is intended to help patients view and interpret their own health data, complete pre-visit tasks, and communicate more effectively with their care teams.
“By helping patients view and interpret their own data, complete previsit tasks, and communicate more effectively with care teams, the app will aim to strengthen shared decision-making and sustain engagement over time,” said Chen.
Clinical leadership and broader impact
The project’s clinical lead is Gitanjali Srivastava, MD, Professor of Medicine in the Division of Diabetes, Endocrinology and Metabolism.
“Medicine has evolved, and we need to adapt to new technological advances while catering to patient needs,” Srivastava stated. “It’s about designing practical tools and processes that fit naturally into patients’ lives and clinicians’ workflows, ultimately supporting healthier weight management over time.”
Chen emphasised that the project is intended to be scalable across health systems. The researchers believe that the human–AI collaborative approach developed through this project could serve as a reproducible framework for improving continuity of care for other chronic conditions that require long-term management.
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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.
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New AI Tool Pinpoints Genes and Drug Combinations That Restore Health in Diseased Cells
Key Takeaways:
- Harvard researchers have developed PDGrapher, an AI tool that identifies genes and drug combinations most likely to restore diseased cells to a healthy state.
- The model uses graph neural networks to map cellular relationships and predict effective single or combined drug targets, significantly reducing the need for exhaustive drug screening.
- PDGrapher demonstrated high accuracy and speed across multiple cancer datasets, offering promise for personalised medicine and drug discovery in complex diseases such as cancer, Parkinson’s, and Alzheimer’s.
Introduction: A new era in drug discovery
Researchers at Harvard Medical School (HMS) have unveiled a powerful artificial intelligence (AI) model capable of identifying therapies that can reverse disease at the cellular level. This innovation, published in Nature Biomedical Engineering on 9 September, could reshape how new drugs are discovered, designed, and personalised for patients.
Unlike traditional approaches that examine one protein target or drug candidate at a time, this new model — named PDGrapher and freely available to researchers — analyses multiple cellular drivers of disease. It identifies the genes most likely to return diseased cells to normal function and pinpoints the most promising single or combined drug targets to correct the underlying cellular dysfunction.
“Traditional drug discovery resembles tasting hundreds of prepared dishes to find one that happens to taste perfect,” explained senior study author Marinka Zitnik, Associate Professor of Biomedical Informatics at HMS’s Blavatnik Institute. “PDGrapher works like a master chef who understands what they want the dish to be and exactly how to combine ingredients to achieve the desired flavour.”
Limitations of traditional drug discovery
Historically, drug discovery has focused on activating or inhibiting a single protein target. This approach has produced successful therapies such as kinase inhibitors, which block proteins that drive cancer cell growth. However, Zitnik emphasised that such strategies often fall short for diseases driven by multiple interacting pathways and genes.
She noted that many recent therapeutic breakthroughs — including immune checkpoint inhibitors and CAR T-cell therapies — work by targeting broader disease processes rather than single molecules. PDGrapher aims to expand this concept by identifying drug targets that can reverse signs of disease, even when the precise molecular mechanisms are not yet fully understood.
How PDGrapher works: Mapping complex cellular networks
PDGrapher is based on a graph neural network — a form of AI that analyses not only individual data points but also the relationships and interactions between them. In biological research, this means mapping how genes, proteins, and signalling pathways influence one another inside a cell.
Instead of screening thousands of compounds blindly, PDGrapher simulates what would happen if certain genes or pathways were switched off, dialled down, or targeted with a drug. It then predicts whether these interventions would shift a diseased cell towards a healthier state.
“Instead of testing every possible recipe, PDGrapher asks: ‘Which mix of ingredients will turn this bland or overly salty dish into a perfectly balanced meal?’” said Zitnik.
Testing and validation: Proving its predictive power
To train the model, researchers fed PDGrapher a dataset of diseased cells both before and after treatment, allowing it to learn which gene changes led to recovery.
They then evaluated the tool using 19 independent datasets across 11 cancer types, combining both genetic and drug-based experiments. PDGrapher was asked to propose treatment options for samples and cancer types it had never seen before.
The model accurately predicted known drug targets that had been deliberately excluded during training and identified new candidates supported by emerging evidence. Notably, it highlighted KDR (VEGFR2) as a target for non-small cell lung cancer, consistent with clinical findings, and identified TOP2A, an enzyme already targeted by chemotherapy, as a promising target for preventing metastasis in certain tumours.
PDGrapher consistently outperformed comparable AI models — ranking correct therapeutic targets up to 35 percent higher and producing results up to 25 times faster.
Implications for future drug discovery
By focusing on targets that directly reverse disease traits, PDGrapher streamlines the drug discovery process. This allows researchers to prioritise fewer, more promising interventions and to design experiments that are faster and more cost-effective.
This capability is particularly valuable for complex diseases such as cancer, where tumours often evade therapies that strike only one target. Because PDGrapher identifies multiple disease drivers, it offers a way to design combination treatments that could prevent drug resistance.
In the future, with further validation, PDGrapher could be applied to individual patients’ cellular profiles to create personalised treatment strategies.
Broader applications and ongoing research
Beyond cancer, the research team is using PDGrapher to investigate neurological conditions such as Parkinson’s disease and Alzheimer’s disease, aiming to identify genetic drivers that could restore neuronal health.
They are also collaborating with Massachusetts General Hospital’s Center for X-linked Dystonia-Parkinsonism (XDP) to map potential drug targets for this rare, inherited neurodegenerative disorder.
“Our ultimate goal is to create a clear road map of possible ways to reverse disease at the cellular level,” Zitnik stated.
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