ArticleJournal of healthcare leadership2022
Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare.
Article in Journal of healthcare leadership, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Analysis of the use of digital technologies in the preliminary diagnosis of dermatological diseases: a systematic review.Archives of dermatological research · 2024Pooled it
- From Prediction to Horizon: Clinicians' Negotiations of AI-Driven Personal Prognoses in Clinical Practice.Sociology of health & illness · 2026Article
- Experiences and Perceptions of Clinical and Graduate Medical Students Regarding AI in Syria: Cross-Sectional Study.JMIR medical education · 2026Article
- Intelligent Decision Support for Transcatheter Aortic Valve Replacement: Machine Learning Spans From Anatomical Assessment to Dynamic Risk Modeling.Reviews in cardiovascular medicine · 2026Review
- Health Care Professionals' Concerns About Medical AI and Psychological Barriers and Strategies for Successful Implementation: Scoping Review.Journal of medical Internet research · 2025Article
- Ethics of AI in healthcare: a scoping review demonstrating applicability of a foundational framework.Frontiers in digital health · 2025Review
- Review
- Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians.Current oncology (Toronto, Ont.) · 2024Review
- The effect of psychological factors on pain outcomes: lessons learned for the next generation of research.Pain reports · 2023Article
- Augmented and virtual reality in spine surgery.Journal of orthopaedics · 2023Article
- Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Review
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review.Frontiers in medicine · 2023Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) promise to transform all facets of medicine. Expected changes include more effective clinical triage, enhanced accuracy of diagnostic interpretations, improved therapeutic interventions, augmented workflow algorithms, streamlined data collection and processing, more precise disease prognostication, newer pharmacotherapies, and ameliorated genome interpretation. However, many caveats remain. Reliability of input data, interpretation of output data, data proprietorship, consumer privacy, and liability issues due to potential for data breaches will all have to be addressed. Of equal concern will be decreased human interaction in clinical care, patient satisfaction, affordability, and skepticism regarding cost-benefit. This descriptive literature-based treatise expounds on the promise and provisos associated with the anticipated import of AI and ML into all domains of medicine and healthcare in the very near future.
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.