001032187 001__ 1032187 001032187 005__ 20250203133217.0 001032187 0247_ $$2doi$$a10.1016/S1470-2045(24)00315-2 001032187 0247_ $$2ISSN$$a1470-2045 001032187 0247_ $$2ISSN$$a1474-5488 001032187 0247_ $$2pmid$$a39481415 001032187 0247_ $$2WOS$$aWOS:001348280600001 001032187 037__ $$aFZJ-2024-06056 001032187 082__ $$a610 001032187 1001_ $$0P:(DE-HGF)0$$aBakas, Spyridon$$b0$$eCorresponding author 001032187 245__ $$aArtificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice 001032187 260__ $$aLondon$$bThe Lancet Publ. Group$$c2024 001032187 3367_ $$2DRIVER$$aarticle 001032187 3367_ $$2DataCite$$aOutput Types/Journal article 001032187 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1738146790_7914 001032187 3367_ $$2BibTeX$$aARTICLE 001032187 3367_ $$2ORCID$$aJOURNAL_ARTICLE 001032187 3367_ $$00$$2EndNote$$aJournal Article 001032187 520__ $$aTechnological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. 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