ArticleMilitary Medical Research2025
Prioritizing human-AI collaboration in healthcare: the TRIAD framework for trustworthy governance, real-world, and integrated adaptive deployment.
Article in Military Medical Research, 2025. 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
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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
9 citing papers in PubMed.
- Deep learning-based identification of causative genes in lissencephaly using 3D-MRI volumetric datasets.Neuroimage. Reports · 2026Article
- A Novel ConvNeXtV2-MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology.Journal of imaging informatics in medicine · 2026Article
- Large Language Models in Clinical Trial Recruitment: Sociotechnical and Economic Framework Development Study.JMIR AI · 2026Article
- A Machine Learning and Traditional Chinese Medicine Constitution-Based Prediction Model for Mild Cognitive Impairment in Community-Dwelling Older Adults.Neuropsychiatric disease and treatment · 2026Article
- Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.Frontiers in digital health · 2026Article
- How Implementation Context Shapes Clinician Acceptance and Engagement with Guideline-Based Clinical Decision Support Systems in Primary Care: A Qualitative Study.Journal of healthcare leadership · 2026Article
- Continuous assurance for AI-driven clinical decision support systems.Frontiers in artificial intelligence · 2026Review
- Development and expert radiologist validation of a custom pipeline for simplification of oncology radiology reports using large language model.Frontiers in oncology · 2026Article
- From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.Frontiers in artificial intelligence · 2026Article
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
Artificial intelligence (AI) and big data are reshaping the healthcare landscape. However, clinical value depends on how well systems augment clinicians and fit into routine workflows. To this end, we introduce the TRIAD framework: trustworthy governance, real-world clinical value, and integrated adaptive deployment, to guide the development, validation, and deployment of clinical AI. TRIAD requires explicit data provenance and intended use, fairness auditing, and calibrated uncertainty. This framework evaluates the human-AI team in real workflows using team-level metrics, including accuracy, safety, workload, and patterns of acceptance, editing, and overriding. Deployment proceeds via staged rollouts with preregistered guardrails and continuous monitoring of performance and subgroup impact. TRIAD views intelligence as a property of the human-AI team rather than the AI model alone. Aligning governance, evaluation, and deployment around clinicians and patients enables durable gains in safety, equity, efficiency, and experience, thereby elevating clinical value.
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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.