Eye tracking technology in medical practice: a perspective on its diverse applications.

Eye tracking technology has emerged as a valuable tool in the field of medicine, offering a wide range of applications across various disciplines. This perspective article aims to provide a comprehensive overview of the diverse applications of eye tracking technology in medical practice. By summarizing the latest research findings, this article explores the potential of eye tracking technology in enhancing diagnostic accuracy, assessing and improving medical performance, as well as improving rehabilitation outcomes. Additionally, it highlights the role of eye tracking in neurology, cardiology, pathology, surgery, as well as rehabilitation, offering objective measures for various medical conditions. Furthermore, the article discusses the utility of eye tracking in autism spectrum disorders, attention-deficit/hyperactivity disorder (ADHD), and human-computer interaction in medical simulations and training. Ultimately, this perspective article underscores the transformative impact of eye tracking technology on medical practice and suggests future directions for its continued development and integration.

Humanizing AI in medical training: ethical framework for responsible design.

The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable healthcare outcomes for both medical practitioners trainees and patients. This perspective article provides an ethical framework for responsibly designing AI systems in medical training, drawing on our own past research in the fields of electrocardiogram interpretation training and e-health wearable devices. The article proposes five pillars of responsible design: transparency, fairness and justice, safety and wellbeing, accountability, and collaboration. The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. The safety and wellbeing pillar however, emphasizes on the need to prioritize patient safety and wellbeing in AI model design whether it is for training or simulation purposes, and the accountability pillar calls for establishing clear lines of responsibility and liability for AI-derived decisions. Finally, the collaboration pillar emphasizes interdisciplinary collaboration among stakeholders, including physicians, data scientists, patients, and educators. The proposed framework thus provides a practical guide for designing and deploying AI in medicine generally, and in medical training specifically in a responsible and ethical manner.