This invention discloses a method and
system for automatically generating medical voice
case records based on a cloud-based large-
scale model. It collects doctor'
s voice data from a medical
microphone and performs semantic analysis to identify terminology density. A terminology calibration mapping is constructed through context matching evaluation and
ambiguity resolution. Case structure response data from the cloud-based large-
scale model is obtained, and priority factors for completion are generated based on structural missingness. Content allocation coefficients are generated based on the cumulative rate of change. Diagnostic conflicts are identified and sorted through the terminology calibration mapping, and key nodes for case generation are established by combining sorting consistency detection. Finally, draft case documents are output based on these key nodes. Standardized case documents are generated by
statistical analysis of doctor modification frequency and allocation of key
verification points. This effectively solves problems such as insufficient handling of terminology
ambiguity, lack of dynamic case structure completion capability, and imperfect error-prone area
verification mechanisms in medical voice transcription.