The article "The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare" by Ankush U. Patel, Qiangqiang Gu, Ronda Esper, Danielle Maeser, and Nicole Maeser highlights the importance of interdisciplinary collaboration in developing and implementing explainable AI (XAI) in healthcare. The authors emphasize that XAI is crucial for fostering trust and enabling effective use of AI in medical contexts, particularly in image-based specialties like pathology and radiology. They argue that interdisciplinary conferences play a vital role in promoting cross-disciplinary exchange, driving innovation, and influencing research directions. The article reviews key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare, emphasizing the need for clear communication, defined roles, and a shared commitment to ethical development. Specialized conferences, such as the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), are highlighted as essential for shaping the future of XAI and computational biology, contributing to improved patient outcomes and healthcare innovations. The authors also discuss the legal, ethical, and societal implications of AI in healthcare, emphasizing the importance of patient-centered care and the need for continuous attention to these issues. Finally, they outline future directions and innovations in XAI, including the integration of AI and computational biology into healthcare 5.0, a vision that focuses on real-time patient monitoring, ambient control, and privacy compliance. The article concludes by emphasizing the ongoing challenges and the need for continued interdisciplinary collaboration to advance XAI and computational biology in healthcare.The article "The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare" by Ankush U. Patel, Qiangqiang Gu, Ronda Esper, Danielle Maeser, and Nicole Maeser highlights the importance of interdisciplinary collaboration in developing and implementing explainable AI (XAI) in healthcare. The authors emphasize that XAI is crucial for fostering trust and enabling effective use of AI in medical contexts, particularly in image-based specialties like pathology and radiology. They argue that interdisciplinary conferences play a vital role in promoting cross-disciplinary exchange, driving innovation, and influencing research directions. The article reviews key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare, emphasizing the need for clear communication, defined roles, and a shared commitment to ethical development. Specialized conferences, such as the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), are highlighted as essential for shaping the future of XAI and computational biology, contributing to improved patient outcomes and healthcare innovations. The authors also discuss the legal, ethical, and societal implications of AI in healthcare, emphasizing the importance of patient-centered care and the need for continuous attention to these issues. Finally, they outline future directions and innovations in XAI, including the integration of AI and computational biology into healthcare 5.0, a vision that focuses on real-time patient monitoring, ambient control, and privacy compliance. The article concludes by emphasizing the ongoing challenges and the need for continued interdisciplinary collaboration to advance XAI and computational biology in healthcare.