Ai-driven system and methods for 3d medical image disease detection

Abstract

The disclosure provides a unified system and method for analyzing 3D medical images through probabilistic masking, multimodal embedding generation, anomaly detection, and reinforcement learning-based refinement. The methods comprise applying ailment-aware probabilistic masking to 3D medical scans using prior distributions of known abnormalities to retain diagnostically relevant voxel regions. The masked scans are encoded into latent vectors using a vision transformer encoder, while associated textual medical reports are encoded using a language model; both are mapped into a shared embedding space. Anomaly detection is performed through (1) a temporal comparison method based on differences in patient embeddings across time, and (2) a similarity-based method comparing current embeddings to a database of known abnormal cases using cosine similarity. Reinforcement learning is applied to refine embeddings using human expert feedback, including corrected annotations and textual clarifications, allowing dynamic adjustment of thresholds and encoder parameters. This adaptive framework supports efficient, scalable, and accurate medical diagnosis across a variety of imaging modalities.