MID-LLM: Enhancing Medical Image Diagnostics With LLMs in a Blockchain AI Framework
Bibliographic record
Abstract
The rapid growth of medical imaging data presents significant challenges in diagnostic accuracy, data privacy, and computational efficiency. Traditional centralized AI models struggle with scalability and pose risks to patient confidentiality due to data aggregation. Moreover, heterogeneous medical data across institutions complicates the development of robust diagnostic tools. To address these issues, we propose MID-LLM, a novel framework that integrates Large Language Models (LLMs) with a blockchain-based federated learning system for medical image analysis. It also ensures the security and privacy of sensitive medical data across decentralized networks. MID-LLM uses verification mechanisms to ensure the global model’s integrity. It also employs aggregation techniques to reduce bias and improve training efficiency. Experiments on the BraTS 2020 dataset show that MID-LLM outperforms traditional federated learning, achieving higher Dice scores with improved computational efficiency. These results highlight MID-LLM’s potential to enhance diagnostic accuracy while offering a scalable, secure solution for AI in healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".