Study Finds Early Virtual Follow-Up Reduces Hospital Readmissions and Enhances Recovery
Key Takeaways:
- A UC San Diego Health telemedicine clinic reduced 30-day hospital readmissions from 20.1% to 14.9% among high-risk patients.
- The clinic provides rapid, virtual follow-up care after discharge, addressing medication access, care understanding, and specialist coordination.
- Findings suggest that virtual post-discharge care can improve health outcomes, cut costs, and enhance care equity.
Virtual care reduces readmissions in high-risk patients
A new study led by researchers at the University of California San Diego (UC San Diego) School of Medicine has found that a virtual transition of care clinic significantly reduces hospital readmissions among high-risk patients.
Published on 23 September 2025 in JMIR Medical Informatics, the study revealed that the 30-day readmission rate for patients seen in UC San Diego Health’s virtual transition of care clinic was 14.9%, compared with 20.1% in a benchmark group that received standard follow-up care.
“With our virtual transition of care clinic, we are providing patients with the right care, at the right place, at the right time,” said Dr Sarah Horman, lead author of the study and Professor of Medicine at UC San Diego School of Medicine. “With the convenience of meeting virtually, we’re able to reach patients much more efficiently.”
Tackling a national challenge
Hospital readmissions represent a major strain on healthcare systems across the United States, with an estimated annual cost of $17 billion. Recognising this challenge, UC San Diego Health clinicians and leadership launched the virtual clinic in 2021 to improve care coordination immediately following discharge.
The initiative supports clinical management and specialist referrals for people leaving hospital, aiming to reduce the likelihood of complications or unplanned readmissions.
How the virtual transition clinic works
The clinic operates with a team of 12 hospitalists, two medical assistants, one pharmacist, and an on-demand interpreter service. When necessary, visits were converted to telephone consultations to accommodate patients facing technical or connectivity barriers.
Each discharge triggers a standardised hand-off to the patient’s primary care provider and relevant specialists, summarising the reason for admission, ongoing care needs, and follow-up recommendations.
If a patient experienced issues post-discharge, the virtual care team expedited communication with the primary care provider to ensure timely in-person review.
Addressing barriers to follow-up care
“When telemedicine first began, there was concern it would further increase health disparities, especially in vulnerable patient groups,” said Dr Horman, who is also a hospitalist and affiliate faculty member at the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health. “However, through our research, we have found the opposite as the virtual clinic reaches patients more effectively.”
Many individuals, she noted, struggle to attend in-person follow-up appointments due to transport issues or mobility limitations. “For example, many patients do not have access to transportation for in-person follow-up visits, so they will often skip them altogether, resulting in an increased risk of hospital readmission. For patients who did not have access to video visits, we coordinated telephone calls instead. In total, the no-show rate for these follow-up visits was less than 5%.”
Strengthening the post-hospital care chain
According to Dr Horman, the clinic targets three critical aspects of post-discharge care:
- Ensuring access to and availability of prescribed medications.
- Supporting patient and caregiver understanding of the care plan.
- Facilitating navigation between primary and specialist care.
“Our goal is to hardwire this linkage in the care chain between the hospital team and primary care in order to help expedite support during that very sensitive, post-hospital period of time,” she explained. “As a result, patient outcomes are improving while they recover at home and hospitals have capacity to take care of the next patient in need of critical care.”
Study scope and findings
The study evaluated more than 25,000 patients discharged from UC San Diego Health between 1 September 2021 and 17 September 2024. Of these, 2,314 were seen in the virtual clinic, while 23,129 received standard care.
Typically, patients see their primary care provider two to four weeks after discharge. However, under this programme, individuals at moderate or high risk were seen within one week.
“Our clinic is a one-time, virtual visit with a patient immediately after their hospital stay to ensure we’re doing all we can to mitigate risk,” added Dr Horman.
Data-driven patient targeting with the LACE+ index
The team used the LACE+ index to identify patients at high risk of readmission or complications. LACE stands for Length of stay, Acuity of admission, Comorbidity, and Emergency department visits. The “+” extends the model to include factors such as age, sex, and previous hospitalisations.
“The use of LACE+ underscores the importance of data-driven and patient-centric strategies in enhancing patient outcomes,” said Dr Horman. “By using this tool, we were able to target follow-up care to those most likely to benefit. This approach helped improve care transitions and reduce avoidable hospital visits.”
Future of the programme
UC San Diego Health’s virtual transition of care clinic continues to operate across Hillcrest and Jacobs Medical Centers, with expansion plans to include East Campus Medical Center.
Dr Horman noted that these findings demonstrate how telemedicine can contribute to broader goals of improving population health, enhancing patient experience, reducing healthcare costs, and advancing care equity.
The study’s co-authors include Milla Kviatkovsky, Edward Castillo, Patricia S. Maysent, Chad VanDenBerg, John Bell, and Christopher A. Longhurst, all from UC San Diego Health.
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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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New Study Finds Wearables May Reshape Obesity Care
Key Takeaways:
- A new study from Northwestern University demonstrates how wearable devices can identify five distinct overeating patterns in people living with obesity, paving the way for more personalised interventions.
- The HabitSense body camera and NeckSense necklace provide unprecedented yet privacy-conscious insights into real-world eating behaviour.
- Researchers emphasise that overeating is not simply a matter of willpower but is shaped by complex emotional, environmental and behavioural factors.
Rethinking obesity treatment through technology
What if a smartwatch, necklace or discreet camera could sense when someone is about to overeat, and instead gently encourage healthier decisions?
Northwestern University scientists are exploring this idea through a pioneering lifestyle medicine programme that combines wearable technology with behavioural analysis. The approach uses three different devices – a necklace, a wristband and a body-mounted camera – to capture eating habits in natural settings, with privacy firmly safeguarded.
“Overeating is a major contributor to obesity, yet most treatments overlook the unconscious habits that drive it,” explained corresponding author Nabil Alshurafa, Associate Professor of Behavioural Medicine at Northwestern University Feinberg School of Medicine and, by courtesy, of Computer Science and Electrical and Computer Engineering at Northwestern’s McCormick School of Engineering.
Five distinct overeating patterns identified
In the study, published in npj Digital Medicine (part of the Nature Portfolio), 60 adults living with obesity wore the three sensors and logged contextual information – such as mood, activity and social setting – using a smartphone app over a two-week period. The project generated thousands of hours of data, revealing that overeating typically followed one of five recurring patterns:
- Take-out feasting – heavy consumption of delivered or takeaway meals.
- Evening restaurant revelry – social dining leading to excessive intake.
- Evening craving – compulsive late-night snacking.
- Uncontrolled pleasure eating – spontaneous binges driven by enjoyment.
- Stress-driven evening nibbling – grazing triggered by anxiety.
“These patterns reflect the complex dance between environment, emotion and habit,” said Alshurafa. “What’s amazing is now we have a roadmap for personalised interventions.”
A step towards personalised interventions
The findings create a foundation for future clinical practice, in which individuals may be profiled according to their dominant overeating pattern and then matched with tailored interventions.
Lead author Farzad Shahabi, a PhD student in Computer Science and member of Alshurafa’s laboratory, highlighted the significance:
“What struck me most was how overeating isn’t just about willpower. Using passive sensing, we were able to uncover hidden consumption patterns in people’s real-world behaviour that are emotional, behavioural and contextual. Seeing the patterns emerge from the data felt like turning on a light in a room we’ve all been stumbling through for decades. Our long-term vision is to move beyond one-size-fits-all solutions and toward a world in which health technology feels less like a prescription and more like a partnership.”
HabitSense – A body camera with built-in privacy
The project’s roots date back to when Alshurafa borrowed a police body camera from Northwestern’s campus police. He modified it to record only food-related actions, creating what is now called HabitSense.
HabitSense is the first patented Activity-Oriented Camera (AOC), which uses thermal sensors to activate recording solely when food enters the field of view. Unlike conventional egocentric cameras that capture everything from the wearer’s perspective, AOCs record actions rather than scenes. This innovation preserves bystander privacy while still collecting critical behavioural data.
NeckSense – Recording eating behaviours in real time
Participants also wore NeckSense, a necklace designed by Alshurafa and his team. NeckSense is the first technology able to passively and precisely monitor multiple eating behaviours. It can detect when someone is eating, how many bites they take, their chewing rate and the frequency with which their hand moves to their mouth. This provides researchers with highly detailed insight into real-world eating events.
A wrist-worn activity tracker – similar to a Fitbit or Apple Watch – completed the three-sensor system.
From personal struggles to scientific mission
Alshurafa’s scientific interest in obesity stems from his own personal journey. Throughout his younger life, his weight fluctuated by 40 to 50 pounds, with repeated attempts at dieting often undermined by late-night binge eating in front of the television.
“I tried to turn my personal struggle into a scientific mission that promises to reshape obesity treatment,” he reflected. “By merging computer science, behavioural medicine and a dash of Jane Goodall–style curiosity, we’re working to lead the way toward truly personalised, habit-based health care. This study marks only the beginning of a journey toward smarter and more compassionate interventions for millions grappling with overeating.”
Study team and support
The research team behind this project brought together a wide range of expertise from Northwestern and beyond. Contributors included PhD student in computer science Boyang Wei, HABits Lab research study coordinator Chris Romano, and undergraduate student Rowan McCloskey. They were joined by adjunct faculty members Annie Lin of the University of Minnesota and Mahdi Pedram of the University of North Texas, as well as former Northwestern faculty member Tammy Stump, now at the University of Utah. Jacob Schauer, Assistant Professor of Preventive Medicine, also played a role, alongside computer science PhD student Glenn Fernandes and senior engineer Tanmeet Butani (MS ’23).
The study was funded by the US National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases.
CCH insight:
This is a fascinating study, which shows how new technologies may be able to provide innovative digital solutions to health issues, in this case identifying behavioural patterns underpinning overeating. These results need to be verified in larger studies, and then interventions trialled to address the different eating patterns, so we are a long way from viable new interventions, but this is an intriguing addition to the development of precision treatments for obesity.
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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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