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患者对基于人工智能的局部前列腺癌决策的信任:一项前瞻性试验的结果。

Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial.

机构信息

Department of Urology, LMU University Hospital, Munich, Germany; USC Institute of Urology and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Department of Urology, LMU University Hospital, Munich, Germany.

出版信息

Eur Urol Focus. 2024 Jul;10(4):654-661. doi: 10.1016/j.euf.2023.10.020. Epub 2023 Nov 1.


DOI:10.1016/j.euf.2023.10.020
PMID:37923632
Abstract

BACKGROUND: Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear. OBJECTIVE: To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients. DESIGN, SETTING, AND PARTICIPANTS: A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC). INTERVENTION: Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists. RESULTS AND LIMITATIONS: Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario. CONCLUSIONS: Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities. PATIENT SUMMARY: Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.

摘要

背景:人工智能(AI)有可能提高诊断准确性并改善治疗效果。然而,AI 如何融入临床工作流程以及患者的看法尚不清楚。

目的:确定患者对 AI 的信任度以及他们对依赖 AI 的泌尿科医生的看法,以及患者对未来诊断和治疗 AI 应用的看法。

设计、设置和参与者:前瞻性试验涉及接受前列腺癌(PC)诊断或治疗干预的患者。

干预:患者在接受磁共振成像、前列腺活检或根治性前列腺切除术之前完成了一项调查。

结果测量和统计分析:主要结果是患者对 AI 的信任度。次要结果是 AI 在治疗环境中的选择以及归因于 AI 和泌尿科医生的特质。

结果和局限性:对 466 名患者的数据进行了分析。对技术的累计亲和力与对 AI 的信任呈正相关(相关系数 0.094;p=0.04),而患者年龄、教育水平和主观疾病感知则没有(p>0.05)。在以个性化的方式沟通诊断时,医生对 AI 的能力的信任评分(4.51±0.76 与 3.38±1.07;平均差值[MD]1.130,95%置信区间[CI]1.010-1.250;t=18.52,p<0.001;Cohen's d=1.040)和解释易懂信息方面的信任评分(4.57±与 3.18±1.09;MD 1.392,95%CI 1.275-1.509;t=27.27,p<0.001;Cohen's d=1.216)高于 AI。患者表示,他们对由医生控制的 AI 做出的诊断比不由医生控制的 AI(4.31±0.88 与 1.75±0.93;MD 2.561,95%CI 2.444-2.678;t=42.89,p<0.001;Cohen's d=2.818)更信任。在当前的临床情况下,AI 辅助医生(66.74%)比单独的医生(29.61%)、由 AI 控制的医生(2.36%)和单独的 AI(0.64%)更受患者青睐。

结论:对未来基于 AI 的诊断和治疗 AI 的信任依赖于与泌尿科医生的最佳整合,作为人机界面,以利用人类和 AI 的能力。

患者总结:人工智能(AI)将在未来的前列腺癌诊断决策中发挥作用。目前,患者更倾向于 AI 辅助的泌尿科医生,而不是单独的泌尿科医生、单独的 AI 和由 AI 控制的泌尿科医生。AI 和泌尿科医生的具体特质可用于优化前列腺癌患者的诊断和治疗。

相似文献

[1]
Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial.

Eur Urol Focus. 2024-7

[2]
Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

Eur Radiol. 2025-2

[3]
Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI.

Radiology. 2024-11

[4]
Artificial Intelligence Improves the Ability of Physicians to Identify Prostate Cancer Extent.

J Urol. 2024-7

[5]
External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

BJU Int. 2025-1

[6]
Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.

JMIR Form Res. 2024-11-27

[7]
Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021?

Eur Urol Focus. 2022-3

[8]
Investigating Whether AI Will Replace Human Physicians and Understanding the Interplay of the Source of Consultation, Health-Related Stigma, and Explanations of Diagnoses on Patients' Evaluations of Medical Consultations: Randomized Factorial Experiment.

J Med Internet Res. 2025-3-5

[9]
Comparison of MRI artificial intelligence-guided cognitive fusion-targeted biopsy versus routine cognitive fusion-targeted prostate biopsy in prostate cancer diagnosis: a randomized controlled trial.

BMC Med. 2024-11-13

[10]
The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study.

J Med Internet Res. 2024-5-28

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[1]
Patient Attitudes Toward Artificial Intelligence in Cancer Care: Scoping Review.

JMIR Cancer. 2025-8-22

[2]
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BMJ Oncol. 2025-5-15

[3]
Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.

EClinicalMedicine. 2025-5-12

[4]
GPT-4 generates accurate and readable patient education materials aligned with current oncological guidelines: A randomized assessment.

PLoS One. 2025-6-4

[5]
The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.

J Clin Med. 2025-4-15

[6]
Evaluating interactions of patients with large language models for medical information.

BJU Int. 2025-6

[7]
Physician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians.

World J Urol. 2024-12-27

[8]
Comparing Patient's Confidence in Clinical Capabilities in Urology: Large Language Models Versus Urologists.

Eur Urol Open Sci. 2024-10-23

[9]
Large language model use in clinical oncology.

NPJ Precis Oncol. 2024-10-23

[10]
Re: letter to the editor for the article "Health-related quality of life following salvage radical prostatectomy for recurrent prostate cancer after radiotherapy or focal therapy".

World J Urol. 2024-9-27

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