Evidence map›Paper›PMID 40483669›Full record

ArticleMonash bioethics review2026

The new narrative medicine: ethical implications of artificial intelligence on healthcare narratives.

David Schwartz, Elizabeth Lanphier

Abstract read
In one paragraph

Article in Monash bioethics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
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.

David SchwartzUniversity of Central Florida, Orlando, USA.
Elizabeth LanphierCincinnati Children's Hospital Medical Center, Cincinnati, USA. lanphieh@ucmail.uc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While on the surface the rapid adoption of generative artificial intelligence (AI) in healthcare signals a new technological innovation that may sideline the social sciences, arts, and literature comprising the medical humanities, we argue that one way to understand the applications of AI based on large language models (LLM) in healthcare is as a deeply narrative project. LLM-based AI endorses narrative and humanistic value insofar as it is trained on vast amounts of narrative data as inputs and generates outputs in narrative formats. We contend that the medical humanities, rather than being replaced by AI in healthcare, are all the more essential to understand the practical and ethical opportunities and constraints for responsible use and integration of such tools. By analyzing two case studies of generative AI in healthcare reported in literature, we show that narrative medicine and the medical humanities provide crucial theoretical frameworks and disciplinary skills to assess, integrate, and critique the roles and impacts of such technologies in healthcare. Rather than offsetting narrative practice via AI, responsible use of LLM-based AI in healthcare requires attention to the functions of narrative in medicine and the importance of the medical humanities.

Indexed as

Artificial IntelligenceDelivery of Health CareNarrationNarrative MedicineGenerative Artificial IntelligenceHumanitiesHumansLarge Language Models

Identifiers

PMID40483669
PMCPMC13526085

What OpenQuestion holds

Textmetadata
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.