ReviewFrontiers in digital health2026
Human-centered AI in healthcare teams: integrating clinical oversight, EHR auditability, and reimbursement pathways for responsible adoption.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Survey on Perspectives Toward Artificial Intelligence Among Italian Interventional Cardiologists.Journal of clinical medicine · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: AI is being introduced into clinical workforces during a critical transition toward integrated, value-based models of care, where its greatest promise lies in augmenting clinician judgment and expanding the reach of already strained healthcare teams. Yet clinical adoption remains limited because most AI systems lack reimbursement pathways, impose substantial implementation costs, and lack standardized mechanisms for integration into electronic health records (EHRs). These gaps create misalignment between technological capability and clinical usability. This paper identifies financial, regulatory, and workflow structures required for AI to operate safely, predictably, and sustainably across key domains of healthcare. Methods: This narrative synthesis reviews clinical, economic, regulatory, and implementation-science literature from 2022 to 2025. Four domains were analyzed: (1) AI augmentation of clinical workflows; (2) reimbursement structures and CPT coding pathways; (3) EHR-based AI deployment and governance; and (4) economic and equity considerations for large-scale implementation. Sources included peer-reviewed reviews, white papers, consensus statements, and health policy analyses. Results: AI tools demonstrated benefits in diagnostic accuracy, decision support, and documentation efficiency, particularly in radiology, cardiology, and EHR-integrated workflows. Adoption was hindered by absent reimbursement for clinician-reviewed AI outputs. Implementation and monitoring costs fell heavily on health systems, risking widened disparities. Additional concerns included accuracy, bias, generalizability, and limited oversight. Enabling conditions included clinician-in-the-loop review, auditable outputs, equity-centered validation, and alignment with evolving payment models. Conclusions: AI can strengthen healthcare teams and advance value-based care, but scaling requires aligned incentives, rigorous evaluation standards, equity safeguards, and coordinated governance-echoing lessons from national EHR implementation.
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.