ArticlePLOS digital health2024
Addressing 6 challenges in generative AI for digital health: A scoping review.
Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis 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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Breaking through the radiation dilemma: development and clinical translation of anti-radiation drugs.Pharmaceutical science advances · 2026Review
- Patient-Facing AI Chatbot Treatment-Direction Advice in Orthodontic Health Communication: A Scenario-Based Comparison with Expert Consensus.Healthcare (Basel, Switzerland) · 2026Article
- Mapping National Governance of AI for Health: Protocol for a Global Scoping Review.JMIR research protocols · 2026Article
- DataXflowGen for GenAI-driven model generation.Scientific reports · 2026Article
- Clinical artificial intelligence applications of vision-language foundation models.PLOS digital health · 2026Review
- Cloudy or Clear? Readability and Content Analysis of Patient Education Materials in Myasthenia Gravis.Muscle & nerve · 2026Article
- Generative Artificial Intelligence-driven orthodontic education practices.BMC medical education · 2026Review
- Comparative performance of recent and prior large language models and pediatric residents on pediatric in-training examination questions.Scientific reports · 2026Article
- Review
- Segment anything small for ultrasound: Enhancing segmentation with non-generative augmentation.PLOS digital health · 2026Article
- AI in patient education: a comparative analysis of the quality and readability of AI generated patient education material on spinal fusion.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Participatory-informed preference optimization (PiPrO): A reinforcement learning simulation study.PLOS digital health · 2026Article
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review.BMJ open · 2026Article
- Predicting the Intention to Use Generative Artificial Intelligence for Health Information: Comparative Survey Study.Journal of medical Internet research · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Hybrid artificial intelligence framework for patient emotion recognition in dentistry.Frontiers in oral health · 2026Article
- The integration of communicable and non-communicable disease (CD-NCD) health services in Africa: A scoping review.PLOS global public health · 2026Article
- Readiness for Hospital Discharge and 30‑Day Unplanned Readmission in Older Adult Sepsis Survivors from Internal Medicine Wards: A Cross‑Sectional Study.Patient preference and adherence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Generative artificial intelligence (AI) can exhibit biases, compromise data privacy, misinterpret prompts that are adversarial attacks, and produce hallucinations. Despite the potential of generative AI for many applications in digital health, practitioners must understand these tools and their limitations. This scoping review pays particular attention to the challenges with generative AI technologies in medical settings and surveys potential solutions. Using PubMed, we identified a total of 120 articles published by March 2024, which reference and evaluate generative AI in medicine, from which we synthesized themes and suggestions for future work. After first discussing general background on generative AI, we focus on collecting and presenting 6 challenges key for digital health practitioners and specific measures that can be taken to mitigate these challenges. Overall, bias, privacy, hallucination, and regulatory compliance were frequently considered, while other concerns around generative AI, such as overreliance on text models, adversarial misprompting, and jailbreaking, are not commonly evaluated in the current literature.
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.