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Implementing Generative Artificial Intelligence in Precision Oncology: Safety, Governance, and Significance

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Hamamoto, Ryuji, Koyama, Takafumi, Takahashi, Satoshi, Yasuda, Tomohiro, Kobayashi, Kazuma, Akagi, Yu, Kouno, Nobuji, Sudo, Kazuki +19 more·2026·Journal of Hematology & Oncology
DOI 10.1186/s13045-026-01781-yPMC PMC12896320
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Abstract

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.

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  • Generative AI platforms have demonstrated growing capacity for treatment recommendation generation, mutation interpretation, and clinical trial matching in oncology.

    “Against this benchmark, generative AI platforms have demonstrated growing capacity for treatment recommendation generation, mutation interpretation, and clinical trial matching in oncology [9].”

    From Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study (2026) · cites this paper as [9]

    The sections on genetic-mutation interpretation and clinical-trial eligibility describe developing AI applications and report evaluated trial-matching performance (page not provided). The paper also describes support for treatment selection and tumor-board deliberations, but the passages do not establish that generative AI platforms have demonstrated the capacity to generate treatment recommendations as a distinct capability. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Authors
Hamamoto, R., Koyama, T., Takahashi, S., Yasuda, T., Kobayashi, K., Akagi, Y., Kouno, N., Sudo, K., Hirata, M., Sunami, K., Kubo, T., Katayama, H., Takashima, A., Taniguchi, T., Matsumoto, H., Shibaki, R., Asada, K., Komatsu, M., Kaneko, S., Yamada, M., Horinouchi, H., Tanaka, K., Goto, Y., Kato, K., Saito, Y., Nakamura, K., & Yamamoto, N.
Affiliations
  • National Cancer Centre Japan (JP)
  • National Cancer Center Hospital (JP)
  • RIKEN Center for Advanced Intelligence Project (JP)
  • National Cancer Center Research Institute (JP)
  • The University of Tokyo (JP)
  • Kyoto University (JP)
Corresponding author
Hamamoto, Ryuji
ORCID
Venue
Journal of Hematology & Oncology
Publisher
Springer Science and Business Media LLC
Published
2026-02-09
Volume
19
Issue
1
Pages
14-14
ISSN
1756-8722 (ISSN-L)
DOI
10.1186/s13045-026-01781-y
PMC
PMC12896320
OpenAlex
W7128421024
Cited by
8
References
32
Citations by year
2026: 8
FWCI
4.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Hematology & Oncology (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Cabinet Office, Government of Japan (BRIDGE)
  • Ministry of Education, Culture, Sports, Science and Technology (the MEXT subsidy for the Advanced Integrated Intelligence Platform)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
generative AI, precision oncology, retrieval-augmented generation, human-in-the-loop, multimodal foundation models, tumor phenotype, data harmonization, AI governance, differential privacy, synthetic data, clinical decision support
MeSH terms
Generative Artificial Intelligence, Artificial Intelligence, Humans, Medical Oncology, Neoplasms, Precision Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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