Evidence map›Paper›PMID 40140592›Full record

ReviewNature2025

Multimodal generative AI for medical image interpretation.

Vishwanatha M Rao, Michael Hla, Michael Moor, Subathra Adithan, Stephen Kwak, Eric J Topol, Pranav Rajpurkar

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 12 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

57 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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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

7 authors.

Vishwanatha M RaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-0080-7299
Michael HlaDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Michael MoorDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Subathra AdithanDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-1464-9911
Stephen KwakDepartment of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Eric J Topol *Scripps Research, La Jolla, CA, USA. etopol@scripps.edu.ORCID 0000-0002-1478-4729
Pranav Rajpurkar *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. pranav_rajpurkar@hms.harvard.edu.ORCID 0000-0002-8030-3727

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process. In this Perspective, we synthesize progress and challenges in developing AI systems for generation of medical reports from images. We focus extensively on radiology as a domain with enormous reporting needs and research efforts. In addition to analysing the strengths and applications of new models for medical report generation, we advocate for a novel paradigm to deploy GenMI in a manner that empowers clinicians and their patients. Initial research suggests that GenMI could one day match human expert performance in generating reports across disciplines, such as radiology, pathology and dermatology. However, formidable obstacles remain in validating model accuracy, ensuring transparency and eliciting nuanced impressions. If carefully implemented, GenMI could meaningfully assist clinicians in improving quality of care, enhancing medical education, reducing workloads, expanding specialty access and providing real-time expertise. Overall, we highlight opportunities alongside key challenges for developing multimodal generative AI that complements human experts for reliable medical report writing.

Indexed as

Artificial IntelligenceDiagnostic ImagingImage Interpretation, Computer-AssistedHumans

Identifiers

PMID40140592

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.