Fransen Stefan J, Kwee T C, Rouw D, Roest C, van Lohuizen Q Y, Simonis F F J, van Leeuwen P J, Heijmink S, Ongena Y P, Haan M, Yakar D
University Medical Center Groningen, Groningen, Netherlands.
Martini Hospital, Groningen, Netherlands.
Eur Radiol. 2025 Feb;35(2):769-775. doi: 10.1007/s00330-024-11012-y. Epub 2024 Aug 14.
This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.
A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis.
A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis.
Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability.
Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone.
Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.
本研究调查了患者对人工智能(AI)用于磁共振成像(MRI)扫描诊断前列腺癌(PCa)的接受程度以及影响他们对AI诊断信任的因素。
2023年1月至11月进行了一项前瞻性多中心研究。对接受前列腺MRI检查的患者就其对MRI扫描的假设性AI评估的意见进行了调查。问卷包括九个项目:四个关于AI与放射科医生组合的假设情景,两个关于对诊断的信任,三个关于误诊的责任。使用多变量分析评估项目与自变量之间的关系。
共纳入212例接受前列腺MRI检查的PCa可疑患者。大多数人倾向于AI与放射科医生一起参与他们的PCa诊断,91%的人同意AI作为主要阅片者,79%的人同意AI作为次要阅片者。如果AI有高度确定性诊断,15%的受访者会接受其作为唯一决策者。自主AI表现优于放射科医生时,52%的人会接受。受教育程度较高的人在AI表现优于放射科医生时倾向于接受AI(p < 0.05)。受访者表示医院(76%)、放射科医生(%)和程序开发者(55%)应对误诊负责。
患者支持AI与放射科医生一起参与PCa诊断。对AI诊断的信任取决于患者的教育水平和AI的表现,在AI表现优于放射科医生的情况下,少数人会接受自主AI。受访者认为医院、放射科医生和程序开发者对误诊负有责任,责任程度依次递减。
患者对MRI上AI辅助前列腺癌诊断表现出高度接受度,无论是与放射科医生一起还是完全自主进行,特别是如果它表现出优于单独放射科医生的性能。
前列腺癌可疑患者可能根据性能接受自主AI。患者更喜欢AI与放射科医生一起参与前列腺癌诊断。患者表示AI的责任应由多个利益相关者分担。