Capital Medical University

The work presents NeuroSymb-MRG, a framework that maps image features to probabilistic clinical concepts, composes multi-hop abductive reasoning chains through a differentiable logic layer (product t-norm AND, probabilistic sum OR, learnable gating α), decodes those chains into roughly 120 clause templates augmented with retrieved evidence and constrained LLM paraphrasing, and drives clinician-in-the-loop review with active sampling based on rule-level predictive entropy (5 MC-dropout passes) plus k-center diversity (k=16 per round); on IU X-ray and MIMIC-CXR it improves BLEU-1 through BLEU-4, ROUGE-L and METEOR over the listed representative baselines, for example IU X-ray BLEU-1 of 0.602 and MIMIC-CXR BLEU-1 of 0.487.

The work presents NeuroSymb-MRG, a framework that maps image features to probabilistic clinical concepts, composes multi-hop abductive reasoning chains through a differentiable logic layer (product t-norm AND, probabilistic sum OR, learnable gating α), decodes those chains into roughly 120 clause templates augmented with retrieved evidence and constrained LLM paraphrasing, and drives clinician-in-the-loop review with active sampling based on rule-level predictive entropy (5 MC-dropout passes) plus k-center diversity (k=16 per round); on IU X-ray and MIMIC-CXR it improves BLEU-1 through BLEU-4, ROUGE-L and METEOR over the listed representative baselines, for example IU X-ray BLEU-1 of 0.602 and MIMIC-CXR BLEU-1 of 0.487.

From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.

From a clinical-expert perspective, this article reviews the progress and value of artificial intelligence, especially deep learning, in core links of spinal deformity surgery such as imaging parameter measurement, surgical risk prediction, and individualized plan formulation, analyzes bottlenecks in technology promotion, and looks ahead to a "human-machine co-intelligence" direction, arguing that integrating AI with clinical experience can move the discipline from "standardized treatment" toward "individualized precision treatment" and build an intelligent diagnosis and treatment system.

Using data from the TRANQUIL multicenter randomized controlled trial, this study estimated minimal clinically important differences (MCIDs) for the modified activities of daily living (mADL) and mADL11 scales of TETRAS in essential tremor via anchor-based approaches (CGI-I and PGI-I) and ROC analyses, recommending patient-report-based MCIDs of -6.2 for mADL and -4.1 for mADL11.

Using data from the TRANQUIL multicenter randomized controlled trial, this study estimated minimal clinically important differences (MCIDs) for the modified activities of daily living (mADL) and mADL11 scales of TETRAS in essential tremor via anchor-based approaches (CGI-I and PGI-I) and ROC analyses, recommending patient-report-based MCIDs of -6.2 for mADL and -4.1 for mADL11.