ReviewJournal of the American Medical Informatics Association : JAMIA2024
Large language models and generative AI in telehealth: a responsible use lens.
Review in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effectiveness of Mobile Health Interventions in Pediatric Cancer: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR mHealth and uHealth · 2026Pooled it
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Views and conditional acceptance of telehealth and artificial intelligence for lifestyle modification among individuals with prediabetes: A qualitative descriptive study.International journal of nursing studies advances · 2026Article
- Hospital Human Resource Managers' Perspectives on Organizational Readiness for Generative AI Skills: Qualitative Descriptive Study.JMIR medical informatics · 2026Article
- Structured reasoning failures compromise LLM interpretation of clinical oncology notes.NPJ digital medicine · 2026Article
- Explainability and Human Oversight for AI-Generated Exercise Guidance in Digital Healthcare: A Governance-Oriented Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Spatial and telehealth accessibility to eating disorder treatment in the United States: evidence from registry and LLM-augmented data.International journal of health geographics · 2026Article
- Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review.Translational andrology and urology · 2026Review
- Scaling medical AI across clinical contexts.Nature medicine · 2026Review
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- An autonomous agentic workflow for clinical detection of cognitive concerns using large language models.NPJ digital medicine · 2026Article
- Real-world deployment and longitudinal evaluation of a human-in-the-loop telemedicine communication support system.Frontiers in digital health · 2026Article
- Developing Effective Frameworks for Large Language Model-Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT.JMIR cancer · 2025Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Telerehabilitation of Developmental Dyslexia: Critical Considerations on Intervention Methods and Their Effectiveness.Brain sciences · 2024Review
- Trustworthy AI in Telehealth: Navigating Challenges, Ethical Considerations, and Future Opportunities for Equitable Healthcare Delivery.Healthcare technology lettersReview
- The double-edged algorithm: A rapid review exploring the trustworthy and responsible use of generative AI in public health.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThis scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. MATERIALS AND
methodsFollowing the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions.
resultsTwenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration.
conclusionThe findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.