ReviewAnnual review of biomedical data science2025
Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support.
Review in Annual review of biomedical data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Based Clinical Decision Support Systems in Primary Care: Systematic Review of Clinical Implementation.Journal of medical Internet research · 2026Pooled it
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- AI-Enabled Interpretation Guidance for Hemostasis Testing with TEGDiagnostics (Basel, Switzerland) · 2026Article
- Seven Pillars for a Community-Led AI-POCUS Future - A WINFOCUS Manifesto.The ultrasound journal · 2026Article
- PROACTIVE-AI: An Intelligent Dashboard for Assessing and Predicting of FDA-approved AI Software Performance in Real-World Clinical Scenarios.Research square · 2026Article
- Landscape of regulatory clinical investigations status of traditional and digital medical devices in the Republic of Korea (2003-mid 2024).NPJ digital medicine · 2026Article
- The role of large language models in the writing of surgical reviews: fact or fantasy?Langenbeck's archives of surgery · 2026Article
- Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.Therapeutic innovation & regulatory science · 2026Article
- AI in respiratory care: findings from the GOLD report.Journal of translational medicine · 2026Article
- From reviews to real-time: dynamic evidence in dentistry.Evidence-based dentistry · 2026Article
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Globalization in the Healthcare Industry: Drivers, Risks, and Adaptation.Healthcare (Basel, Switzerland) · 2026Review
- Data-driven medical devices and the EU MDR: mapping gaps in standards for regulatory compliance.npj health systems · 2026Review
- Artificial intelligence in endocrine practice: comparing ChatGPT, Gemini, and Claude for adrenal incidentaloma care.Journal of endocrinological investigation · 2026Article
- The Current Status and Evaluation of Artificial Intelligence in Bladder Cancer: Clinical Applications and Research.Computational and structural biotechnology journal · 2026Review
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
- Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice.BMJ digital health & AI · 2025Article
- Review
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
Abstract
Artificial intelligence (AI) methods were first developed nearly seven decades ago. Only in recent years have they demonstrated their potential to improve clinical care at the bedside. AI systems are now capable of interpreting, predicting, and even generating important medical information. AI medical devices share many similarities with traditional medical devices but also diverge from them in important ways. Despite widespread optimism and enthusiasm surrounding the use of such devices to improve care processes, patient outcomes, and the healthcare experience for patients, caregivers, and clinicians alike, little evidence exists so far for their effectiveness in practice. Even less is known about the safety or equity of AI medical devices. As with any new technology, this exciting time is accompanied by appropriate questions regarding if, how much, when, and who such AI systems really help. Different stakeholders, ranging from patients to clinicians to industry device developers, may have divergent preferences or assessments of risk and benefits, warranting an informed public discussion to guide emerging regulatory efforts. This review summarizes the rapidly evolving recent efforts and evidence related to the regulation and evaluation of AI medical devices and highlights opportunities for future work to ensure their effectiveness, safety, and equity.
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.