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Bridging Technology and Pretest Genetic Services: Quantitative Study of Chatbot Interaction Patterns, User Characteristics, and Genetic Testing Decisions

✇Journal of Medical Internet Research
By: Yang Yi · Lauren Kaiser-Jackson · Jemar R Bather · Melody S Goodman · Daniel Chavez-Yenter · Richard L Bradshaw · Rachelle Lorenz Chambers · Whitney F Espinel · Rachel Hess · Devin M Mann · Rachel Monahan · David W Wetter · Ophira Ginsburg · Meenakshi Sigireddi · Kensaku Kawamoto · Guilherme Del Fiol · Saundra S Buys · Kimberly A Kaphingst
Background: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. Objective: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. Methods: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Results: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Conclusions: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.
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Delegation to artificial intelligence can increase dishonest behaviour

✇Nature - Issue - nature.com science feeds
By: Nils Köbis · Zoe Rahwan · Raluca Rilla · Bramantyo Ibrahim Supriyatno · Clara Bersch · Tamer Ajaj · Jean-François Bonnefon · Iyad Rahwan

Nature, Published online: 17 September 2025; doi:10.1038/s41586-025-09505-x

People cheat more when they delegate tasks to artificial intelligence, and large language models are more likely than humans to comply with unethical instructions—a risk that can be minimized by introducing prohibitive, task-specific guardrails.
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New Doc on the Block: Scoping Review of AI Systems Delivering Motivational Interviewing for Health Behavior Change

✇Journal of Medical Internet Research
By: Zev Karve · Jacob Calpey · Christopher Machado · Michelle Knecht · Maria Carmenza Mejia
Background: Artificial intelligence (AI) is increasingly used in digital health, particularly through large language models (LLMs), to support patient engagement and behavior change. One novel application is the delivery of motivational interviewing (MI), an evidence-based, patient-centered counseling technique designed to enhance motivation and resolve ambivalence around health behaviors. AI tools, including chatbots, mobile apps, and web-based agents, are being developed to simulate MI techniques at scale. While these innovations are promising, important questions remain about how faithfully AI systems can replicate MI principles or achieve meaningful behavioral impact. Objective: This scoping review aimed to summarize existing empirical studies evaluating AI-driven systems that apply MI techniques to support health behavior change. Specifically, we examined the feasibility of these systems; their fidelity to MI principles; and their reported behavioral, psychological, or engagement outcomes. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and Cochrane Library for empirical studies published between January 1, 2018, and February 25, 2025. Eligible studies involved AI-driven systems using natural language generation, understanding, or computational logic to deliver MI techniques to users targeting a specific health behavior. We excluded studies using AI solely for training clinicians in MI. Three independent reviewers screened and extracted data on study design, AI modality and type, MI components, health behavior focus, MI fidelity assessment, and outcome domains. Results: Of the 1001 records identified, 15 (1.5%) met the inclusion criteria. Of these 15 studies, 6 (40%) were exploratory feasibility or pilot studies, and 3 (20%) were randomized controlled trials. AI modalities included rule-based chatbots (9/15, 60%), LLM-based systems (4/15, 27%), and virtual or mobile agents (2/15, 13%). Targeted behaviors included smoking cessation (6/15, 40%), substance use (3/15, 20%), COVID-19 vaccine hesitancy, type 2 diabetes self-management, stress, mental health service use, and opioid use during pregnancy. Of the 15 studies, 13 (87%) reported positive findings on feasibility or user acceptability, while 6 (40%) assessed MI fidelity using expert review or structured coding, with moderate to high alignment reported. Several studies found that users perceived the AI systems as judgment free, supportive, and easier to engage with than human counselors, particularly in stigmatized contexts. However, limitations in empathy, safety transparency, and emotional nuance were commonly noted. Only 3 (20%) of the 15 studies reported substantially significant behavioral changes. Conclusions: AI systems delivering MI show promise for enhancing patient engagement and scaling behavior change interventions. Early evidence supports their usability and partial fidelity to MI principles, especially in sensitive domains. However, most systems remain in early development, and few have been rigorously tested. Future research should prioritize randomized evaluations; standardized fidelity measures; and safeguards for LLM safety, empathy, and accuracy in health-related dialogue. Trial Registration: OSF Registries 10.17605/OSF.IO/G9N7E; https://osf.io/g9n7e
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Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study

✇Journal of Medical Internet Research
By: Mark McMahon · Sylvie Plate · Tobias Herz · Gabi Brenner · Michael Kleinknecht-Dolf · Michael Krauthammer
Background: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being. Objective: This methodological study aims to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; “documentation of nursing activities”), to predict future workload requirements using machine learning techniques. Methods: We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with three shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error (MAE) and mean absolute percentage error (MAPE), and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels. Results: Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model was the Lasso Regression model, which achieved an average improvement in accuracy of 25.0% compared to the baseline. When used to predict upcoming changes in workload levels, the model achieved strong classification performance, giving an average AUROC of 0.79 and precision values between 66.2% and 75.3%. Crucially, the model severely misclassified—predicting an upcoming increase as a decrease, and vice versa—in just 0.17% of cases, highlighting potential reliability for using the model in practice. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends. Conclusions: This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: 1) utilizing more diverse data, 2) more advanced machine learning architecture that perform time-series modelling, 3) addressing data quality concerns, and 4) conducting controlled trials for real-world evaluation.
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Behavior Change Strategies in Digital Exercise Interventions for Adolescent Idiopathic Scoliosis: Scoping Review

✇Journal of Medical Internet Research
By: Yufeng Li · Fangyuan Chang · Wen Zhang · Zihan Ren · Yawen Chen · Zhao Liu
Background: Adolescent idiopathic scoliosis is a common spinal deformity typically treated with exercise therapy. Despite the increasing use of digital technologies in interventions, there remains a gap in understanding how to effectively integrate behavior change techniques (BCTs) and behavior theories within these digital solutions. Objective: This review aims to identify the digital characteristics of interventions and the BCTs used, and to analyze potential theoretical mechanisms with the Theoretical Domains Framework and the capability, opportunity, motivation, and behavior model. Methods: We conducted a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A total of 5 databases, including PubMed, Web of Science, Embase, Cochrane Library, and CINAHL, were selected for screening eligible studies up to April 4, 2024. We included studies of any design type that involved patients with adolescent idiopathic scoliosis using digital interventions for exercise rehabilitation, including qualitative, quantitative, or mixed methods studies, and study protocols with detailed descriptions of digital interventions. Two researchers independently screened studies and extracted data into tables for descriptive analysis. The Mixed Methods Appraisal Tool was used to assess the quality of studies. Results: Out of the 3267 identified papers, 21 (0.64%) studies were included. The most frequently used technologies were videoconferencing (n=7) and instructional videos (n=5). The three most common BCT clusters were “Shaping Knowledge” (n=19), “Social Support” (n=16), and “Antecedents” (n=16). “Knowledge” was the most used mechanism of action (n=21), followed by “Skills” (n=16), “Environmental Context and Resources” (n=16), and “Social Influences” (n=16). The studies primarily addressed “Capability” and “Opportunity,” with less emphasis on “Motivation,” particularly “Automatic Motivation.” Conclusions: This review identified common digital technologies and their characteristics, analyzed potential mechanisms of behavior change in interventions, and provided recommendations for technology utilization. Future research should further evaluate the effectiveness of digital technologies while enhancing patient motivation and user experience. Trial Registration: PROSPERO CRD42024530851; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024530851
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Digital Health Technology Infrastructure Challenges to Support Health Equity in the United States: Scoping Review

✇Journal of Medical Internet Research
By: Shongkour Roy · Stella T Lartey · Polina Durneva · Niharika Jha · Michael Arthur Ofori · Zebunnesa Zeba · Stella Dockery · Nichole Saulsberry Scarboro · Michelle Taylor · Ashish Joshi
Background: Even though Digital Health Technology (DHT) is widely utilized in the United States (U.S.) at both hospital provider and individual levels, it is beset with several challenges that have contributed to inequities in the health service delivery. Previous studies have shown that health inequities observed may be amplified many by DHT requirements. Objective: The objectives of this scoping review are aimed at synthesizing information on DHT inequities by exploring evidence that describes DHT infrastructure needs focused on promoting health equity in the U.S. and identifying key challenges at both the individual/patient level and at the health service provider's level. Methods: We adapted Arksey and O'Malley's scoping review guidelines in our review. We searched PubMed, Web of Science, CINAHL, and PsycINFO were searched. We also conducted supplementary searches on Google Scholar. The inclusion criteria were peer-reviewed publications that broadly conceptualize or analyze DHT infrastructure from a health equity perspective and the challenges of DHT requirements between 2020 and 2024. Following a full-text screening using eligibility criteria such as studies were included if they examined DHT infrastructure in the U.S. from a health equity perspective, discussed health disparities resulting from DHT interventions, or investigated the variables influencing health inequities connected to DHT. Two researchers evaluated each citation’s individually at the title and abstract levels. Thematic approach and qualitative analysis determined this scoping review’s outcome. Results: Of the 628 research articles from the search, 27 were included in the analysis based on the inclusion criteria. In this review, we discussed factors such as elderly population, education, race, ethnicity, and socioeconomic status leading to health inequities in DHT. Patients and Service providers challenges that exist in health inequities related to DHT. The most common challenges for service providers were infrastructure and technical issues such as inadequate integration with existing workflows, user-unfriendly health information exchange (HIE) interfaces, and lack of skilled staff, while for individuals or patients, this included limited broadband internet access, cultural or linguistic appropriateness, and access to digital tools. Conclusions: The study identified that in the U.S., DHT is an essential part of the delivery of health services, yet it is saddled with key challenges leading to health inequities. Finding pragmatic solutions to these challenges can improve health equity in DHT.
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Prompt Engineering in Clinical Practice: Tutorial for Clinicians

✇Journal of Medical Internet Research
By: Jialin Liu · Fang Liu · Changyu Wang · Siru Liu
Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming healthcare by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, where small changes in wording or structure can greatly impact output quality. This poses a challenge for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot, few-shot, chain-of-thought, and meta-prompting. We examine four critical dimensions (accuracy, bias mitigation, privacy protection, and workflow integration) through clinical case studies grounded in real-world practice. We provide actionable guidance on defining objectives, applying core principles, iteratively refining prompts, and integrating them into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.
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Do startups still need Silicon Valley? Leaders at SignalFire, Lago, and Revolution debate at TechCrunch Disrupt 2025

✇TechCrunch
By: TechCrunch Events
Does Silicon Valley still give founders an edge? At TechCrunch Disrupt 2025, Anh-Tho Chuong (Lago), David Hall (Revolution), and Tawni Nazario-Cranz (SignalFire) debate whether location still drives startup success.
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Interventions Based on Biofeedback Systems to Improve Workers’ Psychological Well-Being, Mental Health, and Safety: Systematic Literature Review

✇Journal of Medical Internet Research
By: Simão Ferreira · Matilde A Rodrigues · Catarina Mateus · Pedro Pereira Rodrigues · Nuno Barbosa Rocha
Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees’ mental well-being. However, most studies on these interventions have been conducted in controlled laboratory settings. Objective: This review aimed to systematically identify and analyze studies that implemented biofeedback-based interventions in real-world occupational settings, focusing on their effectiveness in improving psychological well-being and mental health. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed and EBSCO databases for studies published between 2012 and 2024. Inclusion criteria were original peer-reviewed studies that focused on employees and used biofeedback interventions to improve mental health or prevent mental illness. Exclusion criteria included nonemployee samples, lack of a description of the intervention, and low methodological quality (assessed using the Physiotherapy Evidence Database [PEDro] checklist). Data were extracted on study characteristics, intervention type, physiological and self-reported outcomes, and follow-up measures. Risk of bias was assessed, and VOSviewer was used to visualize the distribution of research topics. Results: A total of 9 studies met the inclusion criteria. The interventions used a range of delivery methods, including traditional biofeedback, mobile apps, mindfulness techniques, virtual reality, and cerebral blood flow monitoring. Most studies focused on breathing techniques to regulate physiological responses (eg, heart rate variability and respiratory sinus arrhythmia) and showed reductions in stress, anxiety, and depressive symptoms. Mobile and app-directed interventions appeared particularly promising for improving resilience and facilitating recovery after stress. Of the 9 studies, 8 (89%) reported positive outcomes, with 1 (11%) study showing initial increases in stress due to logistical limitations in biofeedback access. Sample sizes were generally small, and long-term follow-up data were limited. Conclusions: Biofeedback interventions in workplace settings show promising short-term results in reducing stress and improving mental health, particularly when incorporating breathing techniques and user-friendly delivery methods such as mobile apps. However, the field remains underexplored in occupational contexts. Future research should address adherence challenges, scalability, cost-effectiveness, and long-term outcomes to support broader implementation of biofeedback as a sustainable workplace mental health strategy.
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Fluctuating DNA methylation tracks cancer evolution at clinical scale

✇Nature - Issue - nature.com science feeds
By: Calum Gabbutt · Martí Duran-Ferrer · Heather E. Grant · Diego Mallo · Ferran Nadeu · Jacob Househam · Neus Villamor · Madlen Müller · Simon Heath · Emanuele Raineri · Olga Krali · Jessica Nordlund · Thorsten Zenz · Ivo G. Gut · Elias Campo · Armando Lopez-Guillermo · Jude Fitzgibbon · Chris P. Barnes · Darryl Shibata · José I. Martin-Subero · Trevor A. Graham

Nature, Published online: 10 September 2025; doi:10.1038/s41586-025-09374-4

Cancer evolutionary dynamics are quantitatively inferred using a method, EVOFLUx, applied to fluctuating DNA methylation.
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Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression

✇(Multiomics OR Omics) AND (Pancreatic)
By: Rebecca Melton · Sara Jimenez · Weston Elison · Luca Tucciarone · Abigail Howell · Gaowei Wang · Denise Berti · Elisha Beebe · Michael Miller · Chun Zeng · Carolyn McGrail · Kennedy VanderStel · Katha Korgaonkar · Ruth Elgamal · Hannah Mummey · Joshua Chiou · Emily Griffin · Irina Kusmartseva · Mark Atkinson · Sebastian Preissl · Fabian J Theis · Maike Sander · Kyle J Gaulton

Sci Adv. 2025 Sep 12;11(37):eady0080. doi: 10.1126/sciadv.ady0080. Epub 2025 Sep 10.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here, we performed single-nucleus multiomics and spatial transcriptomics in up to 32 nondiabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine subtypes. β, Acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in β cells. Last, single-cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting β cell regulation. Overall, these results revealed drivers of T1D in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40929272 | PMC:PMC12422192 | DOI:10.1126/sciadv.ady0080

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Organoids in Genetic Disorders: from Disease Modeling to Translational Applications

✇Omics In Lung
By: Yuanhang Zhu · Nanshan Lin · Juan Li · Haoqian Zhang · Ping Zhang · Xin Cheng · Qian Yang · Ling Liu

Stem Cell Rev Rep. 2025 Sep 11. doi: 10.1007/s12015-025-10973-x. Online ahead of print.

ABSTRACT

The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g., brain, eye, kidney, lung, and heart), highlighting their pivotal roles in identifying novel pathogenic genes, elucidating disease mechanisms, and advancing therapeutic strategies such as drug screening platforms, gene-editing therapies, and organ transplantation strategies. Furthermore, we critically address current limitations-including challenges in recapitulating complex pathologies and scaling production-while underscoring their potential for personalized medicine through multi-omics integration and bioengineering innovations. Although the scope of "genetic diseases" is broad, this synthesis focuses on disorders with well-defined inheritance patterns, such as monogenic disorders, copy number variations (CNVs), and aneuploidies. Despite covering only a subset of these conditions, this review aims to provide researchers with a comprehensive overview of the field, emphasizing how organoid-based approaches could accelerate both mechanistic discoveries and clinical translation in genetic disease research.

PMID:40931310 | DOI:10.1007/s12015-025-10973-x

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Liquid biopsy- A pivotal test to help navigate clinical decisions at a precision center in India!

✇MRD
By: Sewanti Limaye · Aditya Shreenivas · Darshana Patil · Janani Sambath · Irene A George · Anjali Parab · Aakriti Datta · Punita Jain · Shambhavi Singh · Chetan Madre · Pritam Kataria · Darshit Shah · Niyati Shah · Ruturaj Deshpande · Rajan Datar · Prashant Kumar

J Liq Biopsy. 2025 Aug 9;9:100323. doi: 10.1016/j.jlb.2025.100323. eCollection 2025 Sep.

ABSTRACT

Liquid biopsy, specifically circulating tumor DNA (ctDNA) analysis, has emerged as a transformative tool in precision oncology, providing real-time, minimally invasive characterizations of the tumor and tumor dynamics. While tissue biopsy is a critical tool for baseline diagnosis of malignancy, it is often limited by sampling constraints and an inability to capture tumor heterogeneity. In this study, we explored the clinical utility of serial ctDNA testing in guiding therapeutic decisions across a cohort of 30 patients with diverse solid tumors. Our real-world analysis demonstrates that ctDNA profiling meaningfully influenced treatment escalation, de-escalation, disease monitoring, and early relapse prediction. Cases where ctDNA positivity indicated minimal residual disease prompted timely escalation of therapy, while ctDNA clearance allowed safe treatment de-intensification, minimizing toxicity without compromising outcomes. Longitudinal ctDNA monitoring provided a dynamic, non-invasive method for assessing treatment response and detecting recurrence months before radiological progression. Our study highlights the potential of integrating liquid biopsy into routine clinical practice to enable dynamic treatment monitoring, early detection of therapeutic resistance, and more informed, personalized decision-making across various cancer types.

PMID:40919127 | PMC:PMC12409318 | DOI:10.1016/j.jlb.2025.100323

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Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

✇Journal of Medical Internet Research
By: David Guo · Kim-Kwang Raymond Choo
Background: Adverse drug reactions (ADRs) pose significant challenges in healthcare, where early prevention is vital for effective treatment and patient safety. Objective: Traditional supervised learning methods are limited in addressing healthcare data, which is often unstructured, heavily regulated, and involves restricted access to sensitive personal information. Methods: The integration of Federated Learning (FL) and Large Language Model (LLM) offers a promising solution to these challenges since FL supports the distributed training on edge device with limited resources and the capability of LLM to deal with unstructured healthcare data. Additionally, client models trained on the edge device can be merged into a global model on the server, preserving data privacy. Results: Natural Language Processing (NLP) technologies underpinning LLM provide a full set of tools that can readily be used to process unstructured ADR as input, enabling LLM to predict ADR outcome effectively. The ADR output space can be discrete labels, unstructured texts, or both. Conclusions: This review presents a scoping review following the PRISMA protocol on the applications of Federated Large Language Model (FedLLM) in ADR prediction, aiming to explore future research venue on ADR applications
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The WHO global landscape of cancer clinical trials

✇Nature Medicine
By: Raffaella Casolino · Lambed Tatah · Sarah Charnaud · Marilina Santero · Andrè Ilbawi · Anna Laura Ross

Nature Medicine, Published online: 09 September 2025; doi:10.1038/s41591-025-03926-x

This Review of the WHO’s International Clinical Trials Registry Platform presents a snapshot of the global cancer trial landscape and provides critical empirical evidence to inform policy, practice and investment.
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