ReviewVisceral medicine2020
Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption.
Review in Visceral medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 4 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
50 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence Supported Analysis of Anal Sphincter and Levator Ani Muscle Using Medical Imaging Techniques: A Systematic Review.International urogynecology journal · 2026Pooled it
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled it
- Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal.BMJ health & care informatics · 2023Pooled it
- Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Examining explainable artificial intelligence in TNM staging with PET-CT: a user-centred observation study.La Radiologia medica · 2026Observational
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Accuracy and Knowledge Base Evaluation of ChatGPT-4o, Gemini-2.0-Flash, and DeepSeek-V3 in Metabolic and Bariatric Surgery: an Expert-Rated Blinded Study.Obesity surgery · 2026Article
- Targeting the Unmet Need in Gastrointestinal Stromal Tumors: A Contemporary Review of Investigational Clinical Trials and Therapeutic Landscape.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture.ACS ES&T engineering · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Explainable Survival Modeling in Pediatric Hematopoietic Stem Cell Transplantation Using Landmark-Based Machine Learning.Scientifica · 2026Article
- Toward an operational definition of Artificial Intelligence for health care informatics: a Delphi survey.Health affairs scholar · 2026Article
- A Comprehensive Approach to Responsible AI Development and Deployment.Mayo Clinic proceedings. Digital health · 2025Review
- Barriers and enablers for generative artificial intelligence in clinical psychology: a qualitative study based on the COM-B and theoretical domains framework (TDF) models.BMC psychology · 2025Article
- Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.Scientific reports · 2025Article
- Artificial intelligence-driven transformative applications in disease diagnosis technology.Medical review (2021) · 2025Review
- A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.Scientific reports · 2025Article
- Article
- Implicit versus explicit Bayesian priors for epistemic uncertainty estimation in clinical decision support.PLOS digital health · 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
backgroundArtificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. SUMMARY: Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. KEY MESSAGES: There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.
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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.