Evidence map›Paper›PMID 40240489›Full record

ArticleNPJ digital medicine2025

Artificial intelligence tools in supporting healthcare professionals for tailored patient care.

Jiyeong Kim, Michael L Chen, Shawheen J Rezaei, Tina Hernandez-Boussard, Jonathan H Chen, Fatima Rodriguez, Summer S Han, Rayhan A Lal, Sun H Kim, Chrysoula Dosiou and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

15 authors.

Jiyeong KimStanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA. jykim3@stanford.edu.
Michael L ChenStanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
Shawheen J RezaeiStanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.
Tina Hernandez-BoussardDepartment of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Jonathan H ChenStanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Palo Alto, CA, USA.
Fatima RodriguezDivision of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA.
Summer S HanStanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Palo Alto, CA, USA.
Rayhan A LalDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
Sun H KimDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
Chrysoula DosiouDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
Susan M SeavDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
Tugce AkcanDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University, Stanford, CA, USA.
Carolyn I RodriguezDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Palo Alto, CA, USA.
Steven M AschDivision of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.
Eleni LinosStanford Center for Digital Health, Department of Medicine, Stanford University, Palo Alto, CA, USA.

Funding

Patient Oriented Research in Vulnerable Populations with Skin DiseaseK24AR075060 · NIAMS · STANFORD UNIVERSITY · PI Eleni Linos · 2019 to 2026
$1.6M
Novel digital tools for home-based monitoring of skin diseaseR01AR082109 · NIAMS · STANFORD UNIVERSITY · PI Eleni Linos · 2023 to 2026
$629k
Leveraging Large Language Models and Machine Learning Algorithms to Assess Depression and Anxiety Symptoms and Risks for Patients with Cardiovascular Disease or Diabetes MellitusK01MH137386 · NIMH · STANFORD UNIVERSITY · PI Jiyeong Kim · 2024 to 2026
$406k
NIAMS NIH HHS K24 AR075060NIAMS NIH HHS R01 AR082109NIH HHS K01MH137386NIH HHS K24AR075060NIMH NIH HHS K01 MH137386
6 · The paper itself

Abstract

Artificial intelligence (AI) tools to support clinicians in providing patient-centered care can contribute to patient empowerment and care efficiency. We aimed to draft potential AI tools for tailored patient support corresponding to patients' needs and assess clinicians' perceptions about the usefulness of those AI tools. To define patients' issues, we analyzed 528,199 patient messages of 11,123 patients with diabetes by harnessing natural language processing and AI. Applying multiple prompt-engineering techniques, we drafted a series of AI tools, and five endocrinologists evaluated them for perceived usefulness and risk. Patient education and administrative support for timely and streamlined interaction were perceived as highly useful, yet deeper integration of AI tools into patient data was perceived as risky. This study proposes assorted AI applications as clinical assistance tailored to patients' needs substantiated by clinicians' evaluations. Findings could offer essential ramifications for developing potential AI tools for precision patient care for diabetes and beyond.

Identifiers

PMID40240489
PMCPMC12003912

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.