Session: The Opportunities of Explainable AI in Healthcare
In healthcare, AI has deep penetration to improve the analytics and prediction models and identify anomalies and diagnosis patterns. Thus, AI use-cases in healthcare image classification, segmentation, and disease predictions [verma et al. 2022]. However, AI decisions in healthcare are critical, and EXAI plays an essential role in healthcare. With the use of aided technologies including artificial intelligence (AI), the Internet of Things (IoT), big data, and assisted networking channels, healthcare 5.0 focuses on real-time patient monitoring, ambient control and well-being, and privacy compliance. Healthcare 5.0 may, however, be hampered by operational procedures in the healthcare industry, the verifiability of prediction models, resilience, and a lack of ethical and legal frameworks. Explainable AI (EXAI), a recent revolutionary movement in AI, focuses on the applicability of conventional AI models by employing the systems' judgment and forecasting outcomes. The modeling and analysis interpretation of real characters’ new potential gives healthcare stakeholders the confidence to comprehend deep learning (ML) and deep learning (DL) models. I will talk about the Opportunities AI has brought to the healthcare sector and the challenges ahead.
- Opportunities AI has brought to healthcare sector
- Challenges ahead for AI application in healthcare
Dr. Aftab is a researcher, author, and academician. She is an author of the book "The Innovation Shift in Higher Education" by Palgrave Macmillan.
She has a Ph.D. in Human Resource Management from Ravenshaw University. Her research interests focus on Human Resource Management, Knowledge management, innovation, Sustainability, and Data Science.
A leader in strategic learning and development, teaching, and workplace training, she is experienced in the application of teaching techniques, mentoring, and team leadership. She explores innovative ways to bridge research, education, and practice!