Evidence map›Paper›PMID 40416675›Full record

Observational studyFrontiers in public health2025

Generative AI's healthcare professional role creep: a cross-sectional evaluation of publicly accessible, customised health-related GPTs.

Bianca Chu, Natansh D Modi, Bradley D Menz, Stephen Bacchi, Ganessan Kichenadasse, Catherine Paterson, Joshua G Kovoor, Imogen Ramsey, Jessica M Logan, Michael D Wiese and 4 more

Abstract readObservational Study
In one paragraph

Observational study in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
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

14 authors.

Bianca Chu *Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Natansh D Modi *Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Bradley D Menz *Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Stephen BacchiDepartment of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Ganessan KichenadasseFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Catherine PatersonCaring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia.
Joshua G KovoorFaculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia.
Imogen RamseyCaring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia.
Jessica M LoganClinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
Michael D WieseClinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
Ross A McKinnonFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Andrew RowlandFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Michael J SorichFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Ashley M HopkinsFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Generative artificial intelligence (AI) is advancing rapidly; an important consideration is the public's increasing ability to customise foundational AI models to create publicly accessible applications tailored for specific tasks. This study aims to evaluate the accessibility and functionality descriptions of customised GPTs on the OpenAI GPT store that provide health-related information or assistance to patients and healthcare professionals. Methods: We conducted a cross-sectional observational study of the OpenAI GPT store from September 2 to 6, 2024, to identify publicly accessible customised GPTs with health-related functions. We searched across general medicine, psychology, oncology, cardiology, and immunology applications. Identified GPTs were assessed for their name, description, intended audience, and usage. Regulatory status was checked across the U.S. Food and Drug Administration (FDA), European Union Medical Device Regulation (EU MDR), and Australian Therapeutic Goods Administration (TGA) databases. Results: A total of 1,055 customised, health-related GPTs targeting patients and healthcare professionals were identified, which had collectively been used in over 360,000 conversations. Of these, 587 were psychology-related, 247 were in general medicine, 105 in oncology, 52 in cardiology, 30 in immunology, and 34 in other health specialties. Notably, 624 of the identified GPTs included healthcare professional titles (e.g., doctor, nurse, psychiatrist, oncologist) in their names and/or descriptions, suggesting they were taking on such roles. None of the customised GPTs identified were FDA, EU MDR, or TGA-approved. Discussion: This study highlights the rapid emergence of publicly accessible, customised, health-related GPTs. The findings raise important questions about whether current AI medical device regulations are keeping pace with rapid technological advancements. The results also highlight the potential "role creep" in AI chatbots, where publicly accessible applications begin to perform - or claim to perform - functions traditionally reserved for licensed professionals, underscoring potential safety concerns.

Indexed as

Generative Artificial IntelligenceHealth PersonnelProfessional RoleCross-Sectional StudiesHumansAI health applicationsAI regulationcustomised GPTsGenerative AI in healthcaremedical chatbotsOpenAI GPT store

Identifiers

PMID40416675
PMCPMC12098394

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.