Prospective multi-centre evaluation of guideline−based artificial intelligence to streamline multidisciplinary tumour board
Abstract
Introduction Multidisciplinary tumour board (MTB) or tumour board (TB) is the “gold standard” for providing oncological patients’ diagnosis and treatment. Often, MTBs are time-intensive, capacity-constrained, and absent or inconsistent in many routine hospitals. Despite that, in MTB, a few experienced specialists in the oncological field, sometimes the final decision is mistaken (4-47%), the necessity of treatment plan changing (12-64%), etc. Using artificial intelligence (AI) for cancer patients’ treatment is a modern approach, but the main idea of our investigation should be to possibly replace MTB. On the other hand, the choice of correct treatment in oncological patients for some doctors, like surgeons, gynaecologists, and family medicine doctors, is a big trouble. They need to address the guidelines, which are so complicated quick changing. We have offered our AI algorithm, which connects doctors with patients’ data, guidelines (only official sources), and then uses LLM for decision-making. Main research question – assess the role of our AI algorithm in diagnosis and treatment for cancer patients, compare with or together with MTB. Methods Prospective, multicentre study at four specialised regional oncological hospitals. Trail period 2023-2025. Additionally, we included 37 doctors (DR) from general and rural hospitals (15 surgeons, 11 gynaecologists, 11 family medicine doctors). 728 cases in eight tumour groups were included in the study: breast 18.13% (132), colorectal…
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