ArticleJournal of clinical medicine2021
Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Algorithmic Choreography: Redefining Human and Non-Human Relations in Digital Health.Sociology of health & illness · 2026Article
- Evidence gap in predicting intracranial haemorrhage risk in people with glioma on anticoagulants: a scoping review.BMC neurology · 2025Article
- Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo.PLOS digital health · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Applying Implementation Science to Advance Electronic Health Record-Driven Learning Health Systems: Case Studies, Challenges, and Recommendations.Journal of medical Internet research · 2024Article
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.Implementation science : IS · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Roles and Competencies of Doctors in Artificial Intelligence Implementation: Qualitative Analysis Through Physician Interviews.JMIR formative research · 2023Article
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
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.
Funding
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.
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.