ArticleJournal of medical Internet research2024
Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Implementing a Commercial AI Fracture Detection Tool in Health Care Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability Framework: A Formative Evaluation Study.JMIR formative research · 2026Article
- Moving beyond the benchmarks: Five foundational principles for meaningful AI evaluation in healthcare.PLOS digital health · 2026Article
- Qualitative Framework for Evaluating Clinical Data Science Systems: Beyond Technical Validation.Healthcare informatics research · 2026Article
- Ten Healthcare Delivery Trends and Their Measurement and Methodological Implications for Cancer Health Services Research.Health services research · 2026Article
- Structured taxonomy and framework for developing medical benchmark in large language models derived from scoping review.NPJ digital medicine · 2026Article
- How artificial intelligence is disrupting the field of medical knowledge and evaluation: a narrative review.BMJ digital health & AI · 2026Review
- ChatGPT provides high-quality answers to FAQs about high tibial osteotomy despite low inter-rater agreement.Journal of experimental orthopaedics · 2025Article
- Human performance evaluation of a pediatric artificial intelligence sepsis model.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- AI in Qualitative Health Research Appraisal: Comparative Study.JMIR formative 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
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.
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.