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Cited by 6Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareMachine Learning in Healthcare

Medical AI and AI for Medical Sciences

Open access
Kazuhiro Sakurada, Tetsuo Ishikawa, Junna Oba, Masahiro Kuno, Yuji Okano, Tomomi Sakamaki, Tomohiro Tamura·2025·JMA Journal
DOI 10.31662/jmaj.2024-0185PMC PMC11799684
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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.

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  • Deep phenotyping integrates genomic sequencing, blood-based omics, microbiome analysis, and wearable device data to capture each patient's unique biological state over time.

    “This strategy integrates genomic sequencing, blood-based omics (e.g., metabolomics and proteomics), microbiome analysis, and wearable device data to capture each patient’s unique biological state over time.”

    From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [98]

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Authors
Sakurada, K., Ishikawa, T., Oba, J., Kuno, M., Okano, Y., Sakamaki, T., & Tamura, T.
Affiliations
Keio University (JP)
ORCID
Venue
JMA Journal
Publisher
Japan Medical Association
Published
2025
Volume
8
Issue
1
Pages
26-37
ISSN
  • 2433-328X (ISSN-L)
  • 2433-3298
DOI
10.31662/jmaj.2024-0185
PMC
PMC11799684
OpenAlex
W4406186736
Cited by
6
References
43
Citations by year
2026: 1 · 2025: 5
FWCI
1.43
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMA Journal (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Keio University
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
artificial intelligence in healthcare, digitalization of healthcare, large language models, predictive medicine, disease progression prediction, clinical decision-making, P4 medicine, diagnosis and prognosis

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