Clinician and patient perspectives on digital technology platforms for healthcare delivery.

Lillian C Chen, Mahsa Yarmohammadi, Peter P Zandi, Melvin Ortega, Shari M Lawson, Ore Afon, Mark Dredze, Leslie Miller

Abstract read

In one paragraph

Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lillian C ChenDepartment of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Mahsa YarmohammadiCenter for Language and Speech Processing, Johns Hopkins University, Baltimore, MD, United States.

Peter P ZandiDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Melvin OrtegaDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Shari M LawsonDepartment of Obstetrics and Gynecology, Howard University College of Medicine, Washington, DC, United States.

Ore AfonDepartment of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Mark DredzeDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, United States.

Leslie MillerDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines clinician and patient experiences and concerns regarding the use of digital technology platforms, particularly artificial intelligence (AI) chatbots, for clinical care. We conducted focus groups with clinicians and patients across two disciplines - young adult mental health and prenatal care - to explore these themes. While clinicians and patients both recognized the benefits of AI chatbots, they also noted the potential for misuse and inability to consider the clinical context among other concerns. We call for AI chatbots to provide clear, culturally sensitive communication, transparent attribution of sources, disclaimers for follow-up with health care professionals, and a form of regulation to avoid bias and misuse.

Indexed as

AIartificial intelligencechatbothealthcareLLM

Identifiers

PMID42416817

DOI10.3389/fdgth.2026.1851786

PMCPMC13337701

What OpenQuestion holds

Textmetadata

LicenceCC BY

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Registered trials

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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.