*Result*: Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.
Title:
Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.
Authors:
Hamamoto R; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. rhamamot@ncc.go.jp.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan. rhamamot@ncc.go.jp., Koyama T; Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Takahashi S; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Yasuda T; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan., Kobayashi K; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan.; Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Akagi Y; Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8655, Japan., Kouno N; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan.; Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan., Sudo K; Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan.; Department of Medical Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Hirata M; Department of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Sunami K; Department of Laboratory Medicine, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Kubo T; Department of Laboratory Medicine, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Katayama H; Clinical Research Support Office, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Takashima A; Department of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Taniguchi T; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Matsumoto H; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Shibaki R; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Asada K; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Komatsu M; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Kaneko S; Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.; AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi,, Chuo-ku, Tokyo, 103-0027, Japan., Yamada M; Endoscopy Division, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Horinouchi H; Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Tanaka K; IT Integrating and Support Center, National Cancer Center, Tokyo, 104-0045, Japan., Goto Y; Department of Thoracic Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Kato K; Department of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Saito Y; Endoscopy Division, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Nakamura K; Clinical Research Support Office, National Cancer Center Hospital, Tokyo, 104-0045, Japan., Yamamoto N; Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Source:
Journal of hematology & oncology [J Hematol Oncol] 2026 Feb 09; Vol. 19 (1), pp. 14. Date of Electronic Publication: 2026 Feb 09.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Biomed Central Country of Publication: England NLM ID: 101468937 Publication Model: Electronic Cited Medium: Internet ISSN: 1756-8722 (Electronic) Linking ISSN: 17568722 NLM ISO Abbreviation: J Hematol Oncol Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : Biomed Central, 2008-
MeSH Terms:
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Grant Information:
BRIDGE Cabinet Office, Government of Japan; the MEXT subsidy for the Advanced Integrated Intelligence Platform Ministry of Education, Culture, Sports, Science and Technology
Contributed Indexing:
Keywords: Generative artificial intelligence; Large language model; Precision oncology; Retrieval-augmented generation; Transformer; Vision language model
Entry Date(s):
Date Created: 20260210 Date Completed: 20260212 Latest Revision: 20260214
Update Code:
20260214
PubMed Central ID:
PMC12896320
DOI:
10.1186/s13045-026-01781-y
PMID:
41664164
Database:
MEDLINE
*Further Information*
*The paramount challenge in precision oncology lies in further improving quality of life and response rates for individual patients. Efforts toward these goals are steadily expanding the scope of clinical implementation, despite ongoing challenges such as standardization, cost-effectiveness, and data harmonization. Building upon this maturing foundation, generative AI-which has evolved dramatically in recent years-is particularly valuable at this stage of advancing efficiency and adoption as an auxiliary technology linking literature, guidelines, trial protocols, and patient data. Specifically, through mutation interpretation, trial eligibility matching, and tumor board support, it is expected to contribute to advancing standardization, improving cost-effectiveness, accelerating data harmonization, and further accelerating human-centered decision-making. Accordingly, this review surveys the development history of generative AI and its current healthcare applications, organizing its implementation potential for precision oncology along three axes: (1) generative AI-based interpretation of genetic mutations and estimation of their pathological significance; (2) generative AI-driven verification of clinical trial eligibility; and (3) multimodal foundation models for imaging and pathology that compute "tumor phenotypes" using real-world data, contributing to report drafting and molecular surrogate estimation. In response, we propose a strategy centered on retrieval-augmented generation (RAG) and human-in-the-loop (HITL) workflows, encompassing data preparation based on OMOP, mCODE, and FHIR; multicenter prospective evaluation; auditable logs and governance aligned with Good Manufacturing Practice (GMP) and the EU AI Act; and a synthetic data strategy including differential privacy. Ultimately, this approach validates value through real-world outcomes and charts a path toward "learning oncology," accelerating patient-centered decision-making and clinical trial development.
(© 2026. The Author(s).)*
*Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: All authors read and approved the final manuscript for publication. Competing interests: The authors declare no competing interests.*