Evidence map›Paper›PMID 39158953›Full record

ArticleJMIR AI2024

The "Magical Theory" of AI in Medicine: Thematic Narrative Analysis.

Giorgia Lorenzini, Laura Arbelaez Ossa, Stephen Milford, Bernice Simone Elger, David Martin Shaw, Eva De Clercq

Registry-linked trialAbstract read
In one paragraph

Article in JMIR AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07532382 (Generating Personalized Stories With Artificial Intelligence for Pediatric Kidney Recipients), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

NCT07532382 recruitingnot on this mapstarted 2026, after this paper: background citation

Generating Personalized Stories With Artificial Intelligence for Pediatric Kidney Recipients

TypeobservationalSponsorParis Translational Research Center for Organ TransplantationRan2026 to 2026Enrolled100ConditionsKidney TransplantArmsAI-generated story, AI generated story
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Giorgia LorenziniInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-9155-4724
Laura Arbelaez OssaInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-8303-8789
Stephen MilfordInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-7325-9940
Bernice Simone ElgerInstitute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-4249-7399
David Martin Shaw *Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0001-8180-6927
Eva De Clercq *Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-7835-9553

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe discourse surrounding medical artificial intelligence (AI) often focuses on narratives that either hype the technology's potential or predict dystopian futures. AI narratives have a significant influence on the direction of research, funding, and public opinion and thus shape the future of medicine.

objectiveThe paper aims to offer critical reflections on AI narratives, with a specific focus on medical AI, and to raise awareness as to how people working with medical AI talk about AI and discharge their "narrative responsibility."

methodsQualitative semistructured interviews were conducted with 41 participants from different disciplines who were exposed to medical AI in their profession. The research represents a secondary analysis of data using a thematic narrative approach. The analysis resulted in 2 main themes, each with 2 other subthemes.

resultsStories about the AI-physician interaction depicted either a competitive or collaborative relationship. Some participants argued that AI might replace physicians, as it performs better than physicians. However, others believed that physicians should not be replaced and that AI should rather assist and support physicians. The idea of excessive technological deferral and automation bias was discussed, highlighting the risk of "losing" decisional power. The possibility that AI could relieve physicians from burnout and allow them to spend more time with patients was also considered. Finally, a few participants reported an extremely optimistic account of medical AI, while the majority criticized this type of story. The latter lamented the existence of a "magical theory" of medical AI, identified with techno-solutionist positions.

conclusionsMost of the participants reported a nuanced view of technology, recognizing both its benefits and challenges and avoiding polarized narratives. However, some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace clinicians. The study concludes that a balanced narrative (that focuses on the technology's present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype.

Indexed as

artificial intelligencehypemedicinenarrativesphysiciansqualitative research

Identifiers

PMID39158953
PMCPMC11369530

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Registered trials

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.