Presentation
Beyond Biomedical Simulations in Supercomputing: Ethical Challenges and Regulatory Obstacles with Boundary Conditions in Healthcare
DescriptionAchieving trustworthy AI systems with easy usability for all stakeholders in the healthcare sector is challenging, as trustworthiness has many facets. It has been shown that even when physicians lack knowledge or understanding, patients are usually willing to use drugs that are demonstrably safe and efficient (Boddington, 2017). Reducing the opacity of black-box AI systems is crucial for healthcare AI applications because of the moral and professional responsibility of physicians to provide reasons and explanations for their decisions (Holzinger et al., 2019).
However, black-box models are common in AI and are generally thought to pose a problem for trustworthiness. Despite the fact that robotic surgical systems are as efficient as physicians, many patients still trust a surgeon more than a robotic system (Longoni, 2019).
This paper explores the challenges in healthcare simulations, emphasizing the need for ethical frameworks and adaptive regulatory mechanisms to address data requirements and privacy concerns.
However, black-box models are common in AI and are generally thought to pose a problem for trustworthiness. Despite the fact that robotic surgical systems are as efficient as physicians, many patients still trust a surgeon more than a robotic system (Longoni, 2019).
This paper explores the challenges in healthcare simulations, emphasizing the need for ethical frameworks and adaptive regulatory mechanisms to address data requirements and privacy concerns.