Explore strategies for clinical deployment of AI models in medicine beyond testset performance in this insightful conference talk. Delve into the challenges of translating academic research success to real-world clinical practice, examining various studies and papers that highlight risks and obstacles. Learn about developing robust evaluation frameworks and monitoring systems to enhance model reliability for deployment. Gain valuable insights on label-efficient models, observational supervision, self-supervision, and methods to assess and improve model trust and robustness to distribution shifts. Engage in a critical discussion on key topics in AI and medicine, generating fresh ideas for their intersection. Benefit from the speaker's expertise in machine learning methodology for medical applications and her research focus on building reliable models for clinical use.