The Clinical Considerations for Trustworthy AI in Oncologic Imaging

Abstract

Abstract The integration of artificial intelligence (AI) into medical imaging has the potential to revolutionise diagnostics and patient care. However, ensuring trust in AI-driven solutions remains a critical challenge. This chapter, written from the healthcare provider’s perspective and by Artificial Intelligence for Health Imaging Network (AI4HI) experts, explores the key aspects of trustworthy AI in oncologic imaging and is structured around several fundamental themes. We begin with an introduction to AI decision support systems in routine clinical practice, outlining both their potential benefits and the concerns that must be addressed. A brief case study illustrates real-world applications and challenges encountered in deploying AI in medical settings. Next, we delve into trustworthy cancer imaging AI solutions, focusing on the role of trust in medicine. Several factors influence confidence in AI for cancer imaging, including stakeholder involvement (clinicians and patient representatives), technology development (design, data collection, algorithm training, and validation), and robust technology assessment. Clinical validity, user experience, robustness, explainability, generalisability, and adherence to AI4HI practices are crucial for ensuring reliable performance and user acceptance. The paper then addresses the transfer of AI solutions from development to clinical practice, examining the clinical gap AI seeks to fill. Regulatory approval, legal, and ethical aspects play a pivotal role in adoption, requiring compliance with established standards. Successful integration into clinical workflows necessitates evaluating individual AI solutions, utilising orchestrators, conducting local validation, adapting technology, and ensuring adequate training for healthcare professionals. Finally, we emphasise the importance of quality management, continuous monitoring, and improvement to maintain trust. Adapting to changing circumstances, implementing structured quality audits, and establishing update strategies are necessary to ensure AI solutions remain relevant and effective. Through interdisciplinary collaboration and adherence to regulatory, ethical, and technological best practices, AI in medical imaging can be developed and implemented in a way that fosters trust and improves patient outcomes.

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