Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper.

Autor: Marti-Bonmati L; Radiology Department and Biomedical Imaging Research Group (GIBI230), La Fe Polytechnics and University Hospital and Health Research Institute, Valencia, Spain. luis_marti@iislafe.es., Koh DM; Department of Radiology, Royal Marsden Hospital and Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK.; Department of Radiology, The Royal Marsden NHS Trust, London, UK., Riklund K; Department of Radiation Sciences, Diagnostic Radiology, Umeå University, 901 85, Umeå, Sweden., Bobowicz M; 2nd Department of Radiology, Medical University of Gdansk, 17 Smoluchowskiego Str, 80-214, Gdansk, Poland., Roussakis Y; Department of Medical Physics, German Oncology Center, 4108, Limassol, Cyprus., Vilanova JC; Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging (IDI)-Girona, Faculty of Medicine, University of Girona, Girona, Spain., Fütterer JJ; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands., Rimola J; CIBERehd, Barcelona Clinic Liver Cancer (BCLC) Group, Department of Radiology, Hospital Clínic, University of Barcelona, Barcelona, Spain., Mallol P; Radiology Department and Biomedical Imaging Research Group (GIBI230), La Fe Polytechnics and University Hospital and Health Research Institute, Valencia, Spain., Ribas G; Radiology Department and Biomedical Imaging Research Group (GIBI230), La Fe Polytechnics and University Hospital and Health Research Institute, Valencia, Spain., Miguel A; Radiology Department and Biomedical Imaging Research Group (GIBI230), La Fe Polytechnics and University Hospital and Health Research Institute, Valencia, Spain., Tsiknakis M; Foundation for Research and Technology Hellas, Institute of Computer Science, Computational Biomedicine Lab (CBML), FORTH-ICS Heraklion, Crete, Greece., Lekadir K; Departament de Matemàtiques and Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain., Tsakou G; Maggioli S.P.A., Research and Development Lab, Athens, Greece.
Jazyk: angličtina
Zdroj: Insights into imaging [Insights Imaging] 2022 May 10; Vol. 13 (1), pp. 89. Date of Electronic Publication: 2022 May 10.
DOI: 10.1186/s13244-022-01220-9
Abstrakt: To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
(© 2022. The Author(s).)
Databáze: MEDLINE
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