Interdisciplinary conferences play a crucial role in advancing explainable AI (XAI) in healthcare by fostering collaboration among experts from diverse fields. As AI integrates into healthcare and computational biology, developing interpretable models is challenging. XAI is essential for building trust and enabling effective AI use in healthcare, particularly in image-based specialties like pathology and radiology. Overcoming these challenges requires interdisciplinary collaboration, which interdisciplinary conferences facilitate. A literature review identified key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. Specialized conferences contribute to fostering dialogue, driving innovation, and influencing research directions. Best practices for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships. Thoughtful structuring of these events, including sessions on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine. Keywords: explainable AI (XAI); interdisciplinary conferences; GLBIO ISCB; healthcare informatics; computational biology; AI ethics in healthcare; algorithmic transparency; AI in medical decision making; patient-centered AI; regulatory aspects of healthcare AI.Interdisciplinary conferences play a crucial role in advancing explainable AI (XAI) in healthcare by fostering collaboration among experts from diverse fields. As AI integrates into healthcare and computational biology, developing interpretable models is challenging. XAI is essential for building trust and enabling effective AI use in healthcare, particularly in image-based specialties like pathology and radiology. Overcoming these challenges requires interdisciplinary collaboration, which interdisciplinary conferences facilitate. A literature review identified key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. Specialized conferences contribute to fostering dialogue, driving innovation, and influencing research directions. Best practices for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships. Thoughtful structuring of these events, including sessions on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine. Keywords: explainable AI (XAI); interdisciplinary conferences; GLBIO ISCB; healthcare informatics; computational biology; AI ethics in healthcare; algorithmic transparency; AI in medical decision making; patient-centered AI; regulatory aspects of healthcare AI.