ReviewHealth and technology2024
The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.
Review in Health and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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.
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Who cites it
20 citing papers in PubMed.
- CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.Journal of big data · 2026Article
- Generative AI and the changing meaning of survey responses in digital health research.EXCLI journal · 2026Article
- A multi-agent GraphRAG framework for pharmacotherapy safety verification in clinical decision support systems.Frontiers in medicine · 2026Article
- Using tree-based ensemble methods to produce a population-based mortality risk score in Ontario, Canada.PloS one · 2026Article
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
- MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study.Journal of medical Internet research · 2025Article
- Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.Computers in biology and medicine · 2025Article
- Microscope Upcycling: Transforming legacy microscopes into automated cloud-integrated imaging systems.HardwareX · 2025Article
- Neonatal nurses' experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus.BMC nursing · 2025Article
- Large Language Models lack essential metacognition for reliable medical reasoning.Nature communications · 2025Article
- Artificial intelligence in prehospital emergency care systems in low- and middle-income countries: cure or curiosity? Insights from a qualitative study.Frontiers in public health · 2025Article
- The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.Current pharmaceutical design · 2025Review
- Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop.Scientific reports · 2024Article
- Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis.JMIR medical informatics · 2024Article
- Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study.JMIR formative research · 2024Article
- Review
- Artificial Intelligence Applied to Electrical and Non-Invasive Hemodynamic Markers in Elderly Decompensated Chronic Heart Failure Patients.Biomedicines · 2024Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon. Methods: The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health. Results: Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem. Conclusions: The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening. Graphical Abstract:
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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.