ReviewBMJ quality & safety2024
Generative artificial intelligence, patient safety and healthcare quality: a review.
Review in BMJ quality & safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed.
- Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.European radiology · 2026Article
- Performance of large language models as a source of clinical information on bacteriophage therapy.Npj viruses · 2026Article
- Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study.JMIR medical education · 2026Article
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Publicly Accessible Large Language Model Responses to Frequently Asked Questions About Spondylodiscitis: Preliminary Expert Evaluation.Journal of medical Internet research · 2026Article
- Evaluating the ability of AI chatbots to provide informed consent information for common oncological surgeries.Annals of the Royal College of Surgeons of England · 2026Article
- Biomedical multimodal large language models: From model-centric development to clinically grounded evaluation and integration.Journal of biomedical informatics · 2026Article
- Ontology-driven generation of parameters for health technology assessment models: a prompt engineering study.International journal of technology assessment in health care · 2026Article
- Comparative Quality Assessment of Artificial Intelligence in Patient Education on Platelet-Rich Plasma (PRP) Therapy.Journal of personalized medicine · 2026Article
- Clinical Use of Non-Certified Generative AI in Healthcare: Governing the Regulatory Grey Zone from Convenience to Legal Accountability.Journal of medical systems · 2026Article
- Application and efficacy of artificial intelligence in patient education on spinal cord injuries.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
- Experts V/S AI´s 2.0: Comparative evaluation of AI models and expert consensus in obstructive sleep apnea assessment.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Benchmarking public large language model responses to patient-facing varicose veins questions: informational quality, verifiability indicators, and readability.Frontiers in public health · 2026Article
- ChatGPT-5.4 in health education: inter-generation stability and persistent readability challenges.Frontiers in public health · 2026Article
- Large language models in hepatology: A systematic review.Hepatology forum · 2026Review
- Toward a National Health Digital and Data Architecture: Laying the Foundation for Digital Transformation: Commission on Investment Imperatives for a Healthy Nation.NAM perspectives · 2026Review
- The ethics of using virtual assistants to help people in vulnerable positions access care.Journal of medical ethics · 2025Article
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Solicitude toward artificial intelligence among health care providers and its relation to their patient's safety culture in Saudi Arabia.BMC health services research · 2025Article
- A Knowledge-Enhanced Platform (MetaSepsisKnowHub) for Retrieval Augmented Generation-Based Sepsis Heterogeneity and Personalized Management: Development Study.Journal of medical Internet research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models' underpinnings, upsides, risks and unknowns-and how these new capabilities may help improve healthcare quality and patient safety.
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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.