ArticleJMIR AI2024
The "Magical Theory" of AI in Medicine: Thematic Narrative Analysis.
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.
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.
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.
Generating Personalized Stories With Artificial Intelligence for Pediatric Kidney Recipients
Who cites it
3 citing papers in PubMed.
- The last human touch: why AI doesn't belong in life's defining moments.BMC medical ethics · 2026Article
- Article
- From Stories to Science: Mapping Global Trends in Narrative Medicine Research (2004-2024).Cureus · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
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.