Evidence map›Paper›PMID 40619425›Full record

ArticleMilitary Medical Research2025

Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.

Ming-Jun Shi, Zhi-Xiang Wang, Shuang-Kun Wang, Xuan-Hao Li, Yan-Lin Zhang, Ying Yan, Ran An, Li-Ning Dong, Lei Qiu, Tian Tian and 12 more

Abstract readMulticenter Study
In one paragraph

Article in Military Medical Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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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

22 authors.

Ming-Jun Shi *Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Zhi-Xiang Wang *Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Shuang-Kun Wang *Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
Xuan-Hao LiDepartment of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Yan-Lin ZhangDepartment of Pathology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Ying YanDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Ran AnDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Li-Ning DongDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Lei QiuDepartment of Urology, Peking University Third Hospital, Beijing, 100083, China.
Tian TianDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
Jia-Xin LiuDepartment of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Hong-Chen SongDepartment of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Ya-Fan WangDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
Che DengDepartment of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
Zi-Bing CaoDepartment of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Hong-Yin WangDepartment of Urology, Peking University Third Hospital, Beijing, 100083, China.
Zheng WangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Wei WeiDivision of Science and Technology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Jian SongDepartment of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China. songjian1974@aliyun.com.
Jian LuDepartment of Urology, Peking University Third Hospital, Beijing, 100083, China. lujian@bjmu.edu.cn.
Xuan WeiDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China. weixuan315@163.com.
Zhen-Chang WangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China. cjr.wzhch@vip.163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultiparametric magnetic resonance imaging (mpMRI) has significantly advanced prostate cancer (PCa) detection, yet decisions on invasive biopsy with moderate prostate imaging reporting and data system (PI-RADS) scores remain ambiguous.

methodsTo explore the decision-making capacity of Generative Pretrained Transformer-4 (GPT-4) for automated prostate biopsy recommendations, we included 2299 individuals who underwent prostate biopsy from 2018 to 2023 in 3 large medical centers, with available mpMRI before biopsy and documented clinical-histopathological records. GPT-4 generated structured reports with given prompts. The performance of GPT-4 was quantified using confusion matrices, and sensitivity, specificity, as well as area under the curve were calculated. Multiple artificial evaluation procedures were conducted. Wilcoxon's rank sum test, Fisher's exact test, and Kruskal-Wallis tests were used for comparisons.

resultsUtilizing the largest sample size in the Chinese population, patients with moderate PI-RADS scores (scores 3 and 4) accounted for 39.7% (912/2299), defined as the subset-of-interest (SOI). The detection rates of clinically significant PCa corresponding to PI-RADS scores 2-5 were 9.4, 27.3, 49.2, and 80.1%, respectively. Nearly 47.5% (433/912) of SOI patients were histopathologically proven to have undergone unnecessary prostate biopsies. With the assistance of GPT-4, 20.8% (190/912) of the SOI population could avoid unnecessary biopsies, and it performed even better [28.8% (118/410)] in the most heterogeneous subgroup of PI-RADS score 3. More than 90.0% of GPT-4 -generated reports were comprehensive and easy to understand, but less satisfied with the accuracy (82.8%). GPT-4 also demonstrated cognitive potential for handling complex problems. Additionally, the Chain of Thought method enabled us to better understand the decision-making logic behind GPT-4. Eventually, we developed a ProstAIGuide platform to facilitate accessibility for both doctors and patients.

conclusionsThis multi-center study highlights the clinical utility of GPT-4 for prostate biopsy decision-making and advances our understanding of the latest artificial intelligence implementation in various medical scenarios.

Indexed as

Decision MakingMultiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedBiopsyHumansMaleMiddle AgedProstateRetrospective StudiesDecision-makingGenerative Pretrained Transformer-4 (GPT-4)Multiparametric magnetic resonance imaging (mpMRI)Prostate biopsyProstate cancer

Identifiers

PMID40619425
PMCPMC12232764

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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.