Machine learning (ML) based mortality prediction models are increasingly used in clinical settings to support prognostication, care planning, and resource allocation. However, many clinicians lack the foundational knowledge and applied skills needed to understand how these models are developed, interpret their outputs accurately, and implement them appropriately in patient care. This gap limits the effectiveness, safety, and ethical use of ML mortality models in routine clinical practice. Timeliness of serious illness conversation discussions requires both comfort with these difficult conversations and accurate prognostication on the part of the treating clinicians. Palliative medicine specialists can help, but need to be brought in earlier. Clinicians typically over-estimate prognosis leading to missed opportunities to either initiate discussion or consult palliative medicine to assist.

Current practice is characterized by several interrelated gaps. Clinicians frequently use tools powered by ML without a clear understanding of what ML is, how models are trained and validated, or how data quality and assumptions influence model outputs. Without this foundational knowledge, clinicians may overestimate model reliability or fail to recognize limitations, leading to inappropriate clinical reliance or underutilization. ML mortality prediction models are increasingly available, yet clinicians report uncertainty about when and how to apply these predictions to clinical decision-making. There is inadequate understanding of how mortality models are developed, what outcomes they predict, and how to contextualize risk predictions within individual patient circumstances, resulting in inconsistent use across clinical settings. Furthermore, clinicians often lack structured education addressing the clinical challenges and ethical considerations associated with ML mortality models, including bias, transparency, and impact on patient-provider communication. This gap contributes to clinician discomfort, mistrust, and concerns about equity and patient-centered care, hindering responsible implementation. 

National surveys indicate that while most physicians report current or anticipated use of AI/ML tools, fewer than 30% feel adequately trained, and many express limited confidence in understanding model performance, bias, and ethical implications. The rapid growth of ML-enabled medical devices has outpaced clinician education and implementation guidance. The U.S. Food and Drug Administration has authorized over 1,300 AI/ML-enabled medical devices, including prognostic and mortality risk models; however, many lack robust real-world validation and rely on clinicians to appropriately interpret and contextualize outputs in practice. 

ML mortality prediction models demonstrate strong performance but raise concerns about bias, transparency, and clinical integration. Systematic reviews show that ML mortality models achieve high predictive accuracy (AUCs often >0.80), yet frequently lack external validation, subgroup analyses, and clear guidance for clinical use, contributing to ethical concerns and inconsistent adoption, therefore, it is imperative to stay abreast of these advancements and guidelines.