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评估ChatGPT在放射瘤学方面的能力:跨临床场景的综合评估

Sherif Ramadan¹, Adam Mutsaers¹, Po-Hsuan Cameron Chen²

¹Department of Radiation Oncology, London Health Sciences Centre, London, ON, Canada.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|November 21, 2024

概括

人工智能 (AI) 在辐射瘤学中展示了潜力,在12个临床病例中获得了84%的得分. 虽然在许多领域都准确,但人工智能在复杂的临床管理和治疗规划方面存在局限性.

科学领域:

在瘤学瘤学.
人工智能的人工智能
机器学习机器学习

背景情况:

人工智能 (AI) 和机器学习 (ML) 为推进辐射瘤学的临床决策提供了有希望的途径.
在专业医疗领域评估AI语言模型的能力对于理解它们的潜在应用至关重要.

研究的目的:

评估一个著名的AI语言模型ChatGPT在解释复杂的辐射瘤学临床场景方面的能力.
评估ChatGPT的瘤学知识库的深度和准确性.

主要方法:

开发了一套全面的12个临床病例,涵盖各种疾病部位.
问题涵盖了各种领域,包括流行病学,分期,临床管理,治疗计划,癌症生物学,物理和监测.
放射瘤学家根据简洁性,完整性和正确性评估了ChatGPT的反应,使用标准化标签 (每个标准0-5分).

主要成果:

在182个问题中,ChatGPT获得了84% (2317/2730) 的总分.
绩效因标准而异:正确性 (81%),完整性 (79%) 和简洁性 (92%).
人工智能在流行病学 (93%) 和癌症生物学 (93%) 中表现出色,在分期,物理和监测方面表现出色,但在治疗计划 (78%) 和临床管理 (81%) 中表现较差.

结论:

作为放射性瘤学的辅助工具,ChatGPT具有显著的潜力,在几个关键知识领域表现出高精度.
确定了局限性,特别是关于复杂的临床场景中反应的完整性和正确性,突出了未来人工智能开发的领域.

关键词:

人工智能的人工智能是人工智能.大型语言模型.辐射瘤学辐射瘤学

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