Evidence map›Paper›PMID 39926067›Full record

ReviewJMA journal2025

Medical AI and AI for Medical Sciences.

Kazuhiro Sakurada, Tetsuo Ishikawa, Junna Oba, Masahiro Kuno, Yuji Okano, Tomomi Sakamaki, Tomohiro Tamura

Abstract readReview
In one paragraph

Review in JMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

7 authors.

Kazuhiro SakuradaDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Tetsuo IshikawaDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Junna ObaDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Masahiro KunoDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Yuji OkanoDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Tomomi SakamakiDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.
Tomohiro TamuraDepartment of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine Graduate School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital transformation of healthcare is rapidly progressing. Digital transformation improves the quality of services and access to health information for users, reduces the workload and associated costs for healthcare providers, and supports clinical decision-making. Data and artificial intelligence (AI) play a key role in this process. The AI used for this purpose is called medical AI. Medical AI is currently undergoing a shift from task-specific to general-purpose models. Large language models have the potential to systematize existing medical knowledge in a standardized way. The usage of AI in medicine is not limited to digital transformation; it plays a pivotal role in fundamentally changing the state of medical science. This approach, known as "AI for Medical Science," focuses on pioneering a form of medical science that predicts the onset and progression of disease based on the underlying causes of disease. The key to such predictive medicine is the concept of "states," which can be sought through machine learning. Using states instead of symptoms not only dramatically improves the accuracy of identification (diagnosis) and prediction (prognosis) but also potentially pioneers P4 medicine by integrating it with empirical knowledge and theories based on natural principles.

Indexed as

AI for Medical ScienceArtificial intelligenceDeep learningLarge language modelMachine learningMedical AIP4 medicine

Identifiers

PMID39926067
PMCPMC11799684

What OpenQuestion holds

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LicenceCC BY
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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.