Core Viewpoint - The Medical World Model (MeWM) has been developed to enhance personalized treatment in oncology by simulating tumor evolution and optimizing clinical decision-making through AI technology [1][2]. Group 1: Overview of MeWM - MeWM introduces the concept of a world model, creating a closed-loop process of "observe-simulate-evaluate-optimize" [3]. - The model uses imaging observations as input, generating initial states and predicting future states based on various interventions [4]. Group 2: Core Functions of MeWM - MeWM consists of three main components: a strategy model that generates treatment combinations, a dynamic model that simulates tumor morphology post-treatment, and an inverse dynamics model that scores survival risks for each candidate tumor image [5][6][7]. - The strategy model generates multiple treatment combinations (protocol beams) covering different strategy spaces, while the dynamic model simulates the tumor's response to each treatment [6][11]. Group 3: Clinical Decision-Making Process - The process involves generating treatment combinations, simulating tumor evolution, and evaluating survival risks to select the optimal intervention path [9][13]. - MeWM's approach allows for a data-driven, personalized treatment decision-making process in real liver cancer scenarios [13]. Group 4: Validation and Performance - MeWM has been validated through systematic experiments on both private and public datasets, demonstrating its effectiveness in optimizing treatment decisions [17]. - In visual Turing tests, MeWM's generated images were misidentified as real images at a rate of 79%, indicating high specificity compared to existing methods [16][19]. Group 5: Risk Assessment and Comparison - MeWM's heuristic model shows higher accuracy in survival risk assessment compared to traditional Cox proportional hazards models, with a mean squared error (MSE) of 0.2142 versus 0.3550 for Cox models [21][22]. - Kaplan-Meier analysis indicates that MeWM has superior risk stratification capabilities, achieving a C-Index of 0.752 [23]. Group 6: Clinical Application and Impact - In TACE treatment exploration, MeWM achieved an F1-score of 52.38% on private datasets, outperforming other multimodal models by over 10% [29]. - The integration of MeWM into clinical workflows can enhance pre-treatment outcome predictions by an average of 13% in F1-score, aligning closely with expert recommendations [30].