ArticleJMIR medical informatics2022
Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Technological Integration in Aesthetic Practice: A Systematic Review of Artificial Intelligence, Augmented Reality and Robotics in Cosmetic Procedures.Aesthetic plastic surgery · 2026Pooled it
- Economic evaluation of AI-assisted technologies in healthcare: A systematic review.Journal of food and drug analysis · 2025Pooled it
- AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.Journal of medical Internet research · 2025Pooled it
- Closing Editorial: Toward Safe, Equitable, and Intelligent Healthcare.Healthcare (Basel, Switzerland) · 2026Article
- Theoretical applications of artificial intelligence in smart infusion pump technology: Expert panel insights.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2026Article
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- Perception of artificial intelligence and machine learning applications in the Nigerian healthcare sector: A cross-sectional study.PLOS global public health · 2026Article
- Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership Common Data Model Queries: Validation and Evaluation Study.JMIR medical informatics · 2025Article
- Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women.BMC oral health · 2025Article
- Quantitative evaluation and optimization of AI policy and regulatory texts for smart healthcare.Frontiers in public health · 2025Article
- Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade.npj health systems · 2025Review
- Surgical outcomes and patient-centred perioperative programs.Journal of clinical monitoring and computing · 2023Article
- Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany.Langenbeck's archives of surgery · 2023Article
- The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems.Clinics and practice · 2023Review
- AI in Pathology: What could possibly go wrong?Seminars in diagnostic pathology · 2023Review
- Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging.Dento maxillo facial radiology · 2023Review
- Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare.Frontiers in artificial intelligence · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundCost-effectiveness analysis of artificial intelligence (AI) in medicine demands consideration of clinical, technical, and economic aspects to generate impactful research of a novel and highly versatile technology.
objectiveWe aimed to systematically scope existing literature on the cost-effectiveness of AI and to extract and summarize clinical, technical, and economic dimensions required for a comprehensive assessment.
methodsA scoping literature review was conducted to map medical, technical, and economic aspects considered in studies on the cost-effectiveness of medical AI. Based on these, a framework for health policy analysis was developed.
resultsAmong 4820 eligible studies, 13 met the inclusion criteria for our review. Internal medicine and emergency medicine were the clinical disciplines most frequently analyzed. Most of the studies included were from the United States (5/13, 39%), assessed solutions requiring market access (9/13, 69%), and proposed optimization of direct resources as the most frequent value proposition (7/13, 53%). On the other hand, technical aspects were not uniformly disclosed in the studies we analyzed. A minority of articles explicitly stated the payment mechanism assumed (5/13, 38%), while it remained unspecified in the majority (8/13, 62%) of studies.
conclusionsCurrent studies on the cost-effectiveness of AI do not allow to determine if the investigated AI solutions are clinically, technically, and economically viable. Further research and improved reporting on these dimensions seem relevant to recommend and assess potential use cases for this technology.
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.