TY - JOUR
T1 - Just how transformative will AI/ML be for immuno-oncology?
AU - Bottomly, Daniel
AU - McWeeney, Shannon
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2024.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - Immuno-oncology involves the study of approaches which harness the patient’s immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ ML applications in the clinic. We highlight four clinical/ biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.
AB - Immuno-oncology involves the study of approaches which harness the patient’s immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ ML applications in the clinic. We highlight four clinical/ biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.
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U2 - 10.1136/jitc-2023-007841
DO - 10.1136/jitc-2023-007841
M3 - Review article
C2 - 38531545
AN - SCOPUS:85189719114
SN - 2051-1426
VL - 12
JO - Journal for immunotherapy of cancer
JF - Journal for immunotherapy of cancer
IS - 3
M1 - e007841
ER -