Evidence mapPaperPMID 39557634Full record

ReviewCancer science2025

Current status and future direction of cancer research using artificial intelligence for clinical application.

Ryuji Hamamoto, Masaaki Komatsu, Masayoshi Yamada, Kazuma Kobayashi, Masamichi Takahashi, Mototaka Miyake, Shunichi Jinnai, Takafumi Koyama, Nobuji Kouno, Hidenori Machino and 4 more

Abstract readReview
In one paragraph

Review in Cancer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
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

14 authors.

Ryuji HamamotoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0002-2632-1334
Masaaki KomatsuDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0003-0421-8085
Masayoshi YamadaDepartment of Endoscopy, National Cancer Center Hospital, Tokyo, Japan.
Kazuma KobayashiDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Masamichi TakahashiDepartment of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan.ORCID https://orcid.org/0000-0001-8792-1993
Mototaka MiyakeDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan.
Shunichi JinnaiDepartment of Dermatologic Oncology, National Cancer Center Hospital East, Kashiwa, Japan.
Takafumi KoyamaDepartment of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan.ORCID https://orcid.org/0000-0001-5807-8458
Nobuji KounoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Hidenori MachinoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0001-6843-7696
Satoshi TakahashiDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Ken AsadaDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Naonori UedaDisaster Resilience Science Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
Syuzo KanekoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0003-4558-9800

Funding

Cabinet Office, Government of Japan BRIDGE
6 · The paper itself

Abstract

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI-equipped software as a medical device (AI-SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data-driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI-based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology.

Indexed as

Artificial IntelligenceBiomedical ResearchNeoplasmsBig DataDeep LearningHumansAIclinical applicationhallucinationLLMSaMD

Identifiers

PMID39557634
PMCPMC11786316

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.