ReviewHealthcare (Basel, Switzerland)2023
Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.
Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
92 citing papers in PubMed, 2 syntheses or guidelines pooled it, 219 citations in OpenAlex.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey.Frontiers in digital health · 2025Pooled it
- The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026Article
- Misaligned by design: evaluating and deploying generative AI for the real-world conditions of primary care: a Nordic and European perspective.Scandinavian journal of primary health care · 2026Article
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
- Representing Chronic Pain Experiences Through Manual and AI-Driven Vignettes From Video Blog Data With Member Checking: Participatory Design Study.JMIR formative research · 2026Article
- Comparative evaluation of AI-driven large language models for periodontal-orthodontic patient questions: accuracy, comprehensiveness, readability, and temporal consistency.BMC oral health · 2026Article
- Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.Journal of medical Internet research · 2026Article
- Quality Evaluation of Large Language Model-Assisted Generation of Initial Senior Physician Ward Round Records for Patients With Acute Poisoning: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Multi-model large-scale AI framework for avian influenza surveillance and preparedness: Harnessing large language models to enhance risk communication, real-time decision support, and public health response strategies.One health (Amsterdam, Netherlands) · 2026Article
- The role of large language models in the diagnosis and management of obstetric patients: a pilot feasibility study using simulated obstetric cases.Archives of gynecology and obstetrics · 2026Article
- Using data science to identify climate change and health adverse impacts and solutions in Africa: a scoping review.npj health systems · 2026Article
- Large language model-assisted research question development in public health: a case study in the Special Supplemental Nutrition Program for Women, Infants, and Children.Public health nutrition · 2026Article
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- Article
- ChatGPT-4o in delirium recognition: a pilot study.Revista da Associacao Medica Brasileira (1992) · 2026Article
- A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge.Learning health systems · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Exploring the potential of large language models in nutrition behavior prediction: evidence from college students.Frontiers in nutrition · 2026Article
- Hierarchical Molecular Language Models (HMLMs).ArXiv · 2025Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.
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.