Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

数字化病理学 临床肿瘤学 医学物理学 医学 解剖病理学 病理 计算机科学 数据科学 内科学 癌症 免疫组织化学
作者
Kirthika Senthil Kumar,Vanja Mišković,Agata Blasiak,Raghav Sundar,Alessandra Pedrocchi,Alexander T. Pearson,Arsela Prelaj,Dean Ho
出处
期刊:American Society of Clinical Oncology educational book [American Society of Clinical Oncology]
卷期号: (43) 被引量:32
标识
DOI:10.1200/edbk_390084
摘要

Recently, a wide spectrum of artificial intelligence (AI)–based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.

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