AI辅助肿瘤治疗方案制定，多学科评估显示准确且安全。
Clinical Reasoning AI for Oncology Treatment Planning: A Multi-Specialty Case-Based Evaluation
- 用大模型结合癌症知识图谱与安全检测层生成治疗方案
- 专家评分显示方案与指南一致度达4.7分（满分5分）
- 适合社区医院医生快速生成合规治疗计划
超过80%的美国癌症治疗在社区医疗机构进行，但生存率仍低于学术中心。临床医生需整合基因组学、分期、影像、病理及不断更新的指南，面临巨大认知负担。我们评估了OncoBrain——一个用于肿瘤治疗计划生成的AI临床推理平台，作为OGI的早期步骤。该平台结合通用大模型与癌症特异性图谱检索增强生成层、标准治疗方案语料库作为长期记忆，以及独立于模型的安全层（CHECK）以检测和抑制幻觉。在妇科、泌尿生殖、神经肿瘤、消化道/肝胆及血液系统恶性肿瘤的173个病例上，由三类临床医生（亚专科肿瘤学家50例，医师评审员78例，高级执业提供者45例）使用16项指标完成结构化评估。结果显示，科学准确性、证据支持和安全性得分最高，流程整合与时间节省评分较低但仍可接受。平均对齐指南与证据得分为4.60、4.56、4.70；无安全或错误信息担忧的平均分为4.80、4.40、4.60；流程整合均值为4.50、3.94、4.00；感知时间节省均值为5.00、3.89、3.60。结论：在多学科案例评估中，OncoBrain生成的治疗方案被评价为符合指南、临床可接受且易于监管，表明精心设计的AI推理平台有潜力辅助肿瘤治疗规划，并支持在社区环境中开展前瞻性真实世界研究。
原文摘要 · Abstract (English)
Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians must integrate genomics, staging, radiology, pathology, and changing guidelines, creating cognitive burden. We evaluated OncoBrain, an AI clinical reasoning platform for oncology treatment-plan generation, as an early step toward OGI. Methods: OncoBrain combines general-purpose LLMs with a cancer-specific graph retrieval-augmented generation layer, a gold-standard treatment-plan corpus as long-term memory, and a model-agnostic safety layer (CHECK) for hallucination detection and suppression. We evaluated clinician-enriched case summaries across gynecologic, genitourinary, neuro-oncology, gastrointestinal/hepatobiliary, and hematologic malignancies. Three clinician groups completed structured evaluations of 173 cases using a common 16-item instrument: subspecialist oncologists reviewed 50 cases, physician reviewers 78, and advanced practice providers 45. Results: Ratings were highest for scientific accuracy, evidence support, and safety, with lower but favorable scores for workflow integration and time savings. On a 5-point scale, mean alignment with evidence and guidelines was 4.60, 4.56, and 4.70 across subspecialists, physician reviewers, and advanced practice providers. Mean scores for absence of safety or misinformation concerns were 4.80, 4.40, and 4.60. Workflow integration averaged 4.50, 3.94, and 4.00; perceived time savings averaged 5.00, 3.89, and 3.60. Conclusions: In this multi-specialty vignette-based evaluation, OncoBrain generated oncology treatment plans judged guideline-concordant, clinically acceptable, and easy to supervise. These findings support the potential of a carefully engineered AI reasoning platform to assist oncology treatment planning and justify prospective real-world evaluation in community settings.
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