Observational studyJMIR human factors2024
Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.
Observational study in JMIR human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04049500 (An Observational Study to Adapt a Digital Diabetes Prevention Program), which is not on this map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
An Observational Study to Adapt a Digital Diabetes Prevention Program (dDPP) and Incorporate it Into the Clinical Workflows.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GenAI-Supported Virtual Patients in Health Care Education: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Virtual patients in medical education: a bibliometric analysis based on the wos core collection and scopus databases.Frontiers in medicine · 2026Pooled it
- A generative AI cybersecurity risks mitigation model for code generation: using ANN-ISM hybrid approach.Scientific reports · 2026Article
- Applications and potential of ChatGPT in dentistry: Scoping review of research perspectives.Journal of dental sciences · 2026Review
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- The impact of positive attitude towards artificial intelligence and multiple mediation functions of satisfying basic psychological needs.Scientific reports · 2025Article
- Artificial intelligence as a ploy to delve into the intricate link between genetics and mitochondria in patients with MASLD.JHEP reports : innovation in hepatology · 2025Article
- Challenges and standardisation strategies for sensor-based data collection for digital phenotyping.Communications medicine · 2025Review
- Peer perceptions of clinicians using generative AI in medical decision-making.NPJ digital medicine · 2025Article
- Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners.JMIR medical education · 2025Article
- Article
- Is Generative AI Increasing the Risk for Technology-Mediated Trauma Among Vulnerable Populations?Nursing inquiry · 2025Article
- Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study.MedEdPublish (2016) · 2025Article
- User-centric AI: evaluating the usability of generative AI applications through user reviews on app stores.PeerJ. Computer science · 2024Article
- Exploring ChatGPT's potential in the clinical stream of neurorehabilitation.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundGenerative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting.
objectiveThis paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program.
methodsWe examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency.
resultsMost metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies.
conclusionsChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation.
trial registrationClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.