Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy.
Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
Crit Rev Oncog. 2024;29(2):29-35. doi: 10.1615/CritRevOncog.2023050584.
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.
人工智能 (AI) 算法在肿瘤影像学中展现出巨大的潜力，在回顾性研究中优于或与放射科医生相媲美，这表明它们具有先进的筛查能力。这些 AI 工具为放射科医生提供了有价值的支持，帮助他们完成关键任务，如优先报告、早期癌症检测和精确测量，从而增强临床决策能力。随着医疗保健领域影像学请求的激增和可用放射科医生的减少，人工智能的整合变得越来越有吸引力。通过简化工作流程效率和提高患者护理水平，人工智能为肿瘤影像学实践面临的挑战提供了变革性的解决方案。然而，成功整合 AI 需要应对各种伦理、监管和医疗法律挑战。本综述旨在全面概述这些障碍，旨在促进人工智能在肿瘤影像学中的负责任和有效实施。
