PRESENTATION TITLE: 

Developing a Learning Health System for Melanoma Prognostication

PRESENTER(S): Yevgeniy R. Semenov, M.D., M.A., F.A.A.D.

LEARNING OBJECTIVES
Upon completion of this activity, participants should be able to:

• Evaluate the limitations of current melanoma staging systems and recognize the unmet need for improved prognostic tools, particularly for early-stage disease.
• Describe the integration of clinical, histopathologic, genomic, and spatial biology data into artificial intelligence–driven prognostic models for predicting melanoma recurrence and survival.
• Apply AI-derived, multi-modal prognostic insights to guide personalized surveillance strategies, optimize adjuvant therapy decisions, and reduce overtreatment in melanoma patients.

ATTENDANCE / CREDIT
Text the session code (provided only at the session) to 507-200-3010 within 48 hours of the live presentation to record attendance. All learners are encouraged to text attendance regardless of credit needs. This number is only used for receiving text messages related to tracking attendance. Additional tasks to obtain credit may be required based on the specific activity requirements and will be announced accordingly. Swiping your badge will not provide credit; that process is only applicable to meet GME requirements for Residents & Fellows.

TRANSCRIPT
Any credit or attendance awarded from this session will appear on your Transcript.

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