Clinical Validation, Bias, and Ethical Deployment of Artificial Intelligence in Imaging.

Authors

Abstract

The integration of artificial intelligence (AI) into radiology practice has the potential to transform the health care sector in the setting of ongoing radiologist shortages. AI extends powerful tools to radiologists that enhance both diagnostic precision and reporting efficiency. A key requirement is that an AI model must display reliable performance across a diversity of radiologic data differing broadly across institutions, with varying imaging equipment and patient demographics. Thus, clinical validation by the radiologist is critical. Another important consideration is the smooth and seamless integration of AI into the radiology workflow without impeding clinical flow. It is also important, when deploying AI, to pay attention to its regulatory and ethical aspects. AI algorithms utilized for image analysis fall within the regulatory umbrella of medical devices and need to address corresponding safety and quality standards. Data privacy protections are paramount to ensure patient confidentiality. Decisions for the deployment of AI should be governed by ethical principles. Overall, for all entities, a governance framework is necessary, providing protocols underlining roles and responsibilities in AI deployment and utilization. Governance committees and regular performance audits are crucial. Initial training followed by periodic follow-up training of radiologists and administrators on functions and pitfalls of AI algorithms should be the norm. By emphasizing regulatory compliance, combined with initial and periodic clinical validation, radiologists and health care institutions as well as teleradiology centers can efficiently utilize AI to enhance diagnostic quality and proficiency, while maintaining the critical element of trust with clinical departments and patients.

Tags

Topics