Marco Alì, Arianna Fantesini, Marco Tullio Morcella, Simona Ibba, Gennaro D'Anna, Deborah Fazzini, Sergio Papa
{"title":"Adoption of AI in oncological imaging: ethical, regulatory, and medical-legal thorough","authors":"Marco Alì, Arianna Fantesini, Marco Tullio Morcella, Simona Ibba, Gennaro D'Anna, Deborah Fazzini, Sergio Papa","doi":"10.1615/critrevoncog.2023050584","DOIUrl":null,"url":null,"abstract":"Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.","PeriodicalId":35617,"journal":{"name":"Critical Reviews in Oncogenesis","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Reviews in Oncogenesis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1615/critrevoncog.2023050584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial Intelligence (AI) algorithms have shown great promise in oncological imaging, outperforming or matching radiologists in retrospective studies, signifying their potential for advanced screening capabilities. These AI tools offer valuable support to radiologists, assisting them in critical tasks such as prioritizing reporting, early cancer detection, and precise measurements, thereby bolstering clinical decision-making. With the healthcare landscape witnessing a surge in imaging requests and a decline in available radiologists, the integration of AI has become increasingly appealing. By streamlining workflow efficiency and enhancing patient care, AI presents a transformative solution to the challenges faced by oncological imaging practices. Nevertheless, successful AI integration necessitates navigating various ethical, regulatory, and medical-legal challenges. This review endeavors to provide a comprehensive overview of these obstacles, aiming to foster a responsible and effective implementation of AI in oncological imaging.
期刊介绍:
The journal is dedicated to extensive reviews, minireviews, and special theme issues on topics of current interest in basic and patient-oriented cancer research. The study of systems biology of cancer with its potential for molecular level diagnostics and treatment implies competence across the sciences and an increasing necessity for cancer researchers to understand both the technology and medicine. The journal allows readers to adapt a better understanding of various fields of molecular oncology. We welcome articles on basic biological mechanisms relevant to cancer such as DNA repair, cell cycle, apoptosis, angiogenesis, tumor immunology, etc.