Evidence map›Paper›PMID 41343815›Full record

ArticleJMIR AI2025

The Perceived Roles of AI in Clinical Practice: National Survey of 941 Academic Physicians.

Anshul Ratnaparkhi, Simon Moore, Abhinav Suri, Bayard Wilson, Jacob Alderete, T J Florence, David Zarrin, David Berin, Rami Abuqubo, Kirstin Cook and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR AI, 2025. 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

15 authors.

Anshul RatnaparkhiDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-4337-3715
Simon MooreDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0009-0005-0310-0001
Abhinav SuriDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-7900-8763
Bayard WilsonDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-6782-0677
Jacob AldereteDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-9564-5386
T J FlorenceDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-6666-3032
David ZarrinDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-8666-4972
David BerinDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0009-0009-7546-003X
Rami AbuquboDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0009-0003-5178-7923
Kirstin CookDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-7317-156X
Matiar JafariDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-2224-4896
Joseph S BellDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-8709-115X
Luke MacyszynDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-3706-2796
Andrew C VivasDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-2276-4533
Joel BeckettDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-2392-0526

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) and machine learning models are frequently developed in medical research to optimize patient care, yet they remain rarely used in clinical practice.

objectiveThis study aims to understand the disconnect between model development and implementation by surveying physicians of all specialties across the United States.

methodsThe present survey was distributed to residency coordinators at Accreditation Council for Graduate Medical Education-accredited residency programs to disseminate among attending physicians and resident physicians affiliated with their academic institution. Respondents were asked to identify and quantify the extent of their training and specialization, as well as the type and location of their practice. Physicians were then asked follow-up questions regarding AI in their practice, including whether its use is permitted, whether they would use it if made available, primary reasons for using or not using AI, elements that would encourage its use, and ethical concerns.

resultsOf the 941 physicians who responded to the survey, 384 (40.8%) were attending physicians and 557 (59.2%) were resident physicians. The majority of the physicians (651/795, 81.9%) indicated that they would adopt AI in clinical practice if given the opportunity. The most cited intended uses for AI were risk stratification, image analysis or segmentation, and disease prognosis. The most common reservations were concerns about clinical errors made by AI and the potential to replicate human biases.

conclusionsTo date, this study comprises the largest and most diverse dataset of physician perspectives on AI. Our results emphasize that most academic physicians in the United States are open to adopting AI in their clinical practice. However, for AI to become clinically relevant, developers and physicians must work synergistically to design models that are accurate, accessible, and intuitive while thoroughly addressing ethical concerns associated with the implementation of AI in medicine.

Indexed as

AI adoptionartificial intelligence in medicinebarriers to AI adoptionclinical decision support systemshealth care technology acceptancemachine learning in health caremedical informaticsphysician attitudes

Identifiers

PMID41343815
PMCPMC12715463

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.