ArticleJournal of medical Internet research2026
Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Impact of Responsibility Allocation Structures on Diagnostic Quality in AI-Assisted Diagnosis: Randomized Controlled Experiment.Journal of medical Internet research · 2026Trial
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlabelled: There is growing interest in understanding how generative artificial intelligence (GenAI) can support patients and caregivers in making informed health care decisions, known as patient-centered clinical decision support (PC CDS). In this viewpoint, we present example applications for GenAI-supported PC CDS for patients, caregivers, clinicians, and patient-clinician interactions and examine the opportunities, challenges, and potential solutions associated with these applications. We conducted a targeted document review of our work in the Agency for Healthcare Research and Quality's Clinical Decision Support Innovation Collaborative focusing on GenAI-enabled PC CDS, supplemented by snowball sampling and targeted searches to identify additional applications. Findings were refined and validated through solicited feedback from a 20-member multidisciplinary expert panel. Through our work, we highlight six critical needs that must be addressed to fully realize GenAI's potential in PC CDS: (1) engage and ensure representation of patients and caregivers in design and development; (2) build the science of effective PC CDS implementation to support patient engagement; (3) develop risk-based policies for when to use GenAI; (4) establish independent testing and vetting criteria; (5) periodically reassess to identify and address algorithmic drift and verify performance; and (6) establish policies to promote transparency and patient consent in the use of GenAI. Understanding the applications and their potential implications for health care quality is essential to further the beneficial, ethical, and safe development of GenAI-supported PC CDS.
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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.