This paper thoroughly examines how structured frameworks such as quality assurance (QA), quality control (QC), and acceptance testing (AT) can effectively minimize risks while enhancing the benefits of AI in healthcare settings. The authors underscore the critical need for continuous validation and performance monitoring throughout the AI lifecycle, from initial development to clinical deployment, to ensure reliability, accuracy, and patient safety. Their balanced perspective on the responsible use of AI is particularly commendable, offering a systematic evaluation of QA, QC, and AT with clearly defined criteria for mitigating common risks associated with medical AI tools. By emphasizing ongoing performance assessment and regulatory alignment, the authors provide valuable insights for both developers and healthcare institutions seeking to leverage AI's transformative potential without compromising patient care.