ArticleNPJ digital medicine2026
A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.
Article in NPJ digital medicine, 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.
- Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: Qualitative Study.Journal of medical Internet research · 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
Abstract
With the widespread use of Artificial Intelligence (AI) in healthcare, how human physicians work with AI, a new colleague, has become an issue of increasing concern. This review systematically explores the collaboration between humans and AI in the medical field, focusing on the prevalent human-AI collaboration patterns, the task division mechanisms, and the evaluation metrics. Following the PRISMA guidelines for scoping reviews, we screened the relevant literature and finally included 85 journal papers. Our analysis reveals that human-AI collaboration patterns in healthcare tend to be associated with the inherent risk and automation level of medical tasks, and the collaboration process demonstrates a clear division of labor between human physicians and AI. Furthermore, the evaluation of the human-AI collaboration effect is shifting from single technical metrics (e.g., diagnostic accuracy) to multidimensional aspects incorporating clinical efficiency and user experience. Overall, this review presents collaboration patterns in various medical tasks and serves as a theoretical basis for optimizing the clinical workflows for human-AI collaboration.
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.