Evidence map›Paper›PMID 40390563›Full record

ArticleMedical education2026

Tomorrow's doctors are robots: Redefining healthcare education and practice in the technological revolution.

Tim Fawns, Michelle D Lazarus

Abstract read
In one paragraph

Article in Medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Tim FawnsMonash Education Academy, Monash University, Clayton, Victoria, Australia.ORCID 0000-0001-5014-2662
Michelle D LazarusDepartment of Anatomy and Developmental Biology, Faculty of Medicine, Nursing, and Health Sciences, Biomedical Discovery Institute, Centre of Human Anatomy Education (CHAE), Monash University, Clayton, Victoria, Australia.ORCID 0000-0003-0996-4386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parody is an important form of imitation that can be used to critique problematic assumptions and discourses. We use parody in this article to warn against the overzealous infusion of artificial intelligence (AI) into healthcare and healthcare education. In doing so, our intention is not to discount some potential benefits of carefully considered AI integration into these contexts. Rather, we call readers' attention to important aspects that could also be lost (e.g., caring human relationships, human agency, or careful appreciation of diversity and context) by going too far in reorienting healthcare around AI- and data-powered technologies. As such, we have cited sources that contrast with, or critique, the ironic points made within our text. We encourage readers to take these sources and their arguments seriously while enjoying our joke at the expense of the carefully crafted and, hopefully, exaggerated AI systems. For parody to work, it must be distinguishable from intentional and straightforward accounts. We recognise that our own writing, while farcical to us as the authors, may not be so to all readers, particularly since the discourse around AI-fuelled by for profit companies and evangelists-is often characterised by great hype and promise of more efficient, effective and utopian futures.

Indexed as

Artificial IntelligenceEducation, MedicalRoboticsDelivery of Health CareHumans

Identifiers

PMID40390563
PMCPMC12717484

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.