PrismPrompt: Layering Prompt-Enhanced Cloud-Edge Collaborative Language Model Toward Healthcare
Bibliographic record
Abstract
The rapid evolution of large language models (LLMs) has opened new avenues for enhancing healthcare delivery, particularly through cloud-edge collaborative frameworks. This paper introduces PrismPrompt, a novel system that leverages prompt-based engineering to optimize cloud-edge collaboration in medical applications. By integrating cloud-based LLMs with edge devices, PrismPrompt addresses the challenges of computational limitations and data privacy in healthcare environments. The system utilizes a hierarchical prompt strategy and an incremental expert decision-making process to enhance the retrieval and application of medical knowledge. Key innovations include a retriever module that accurately extracts and retrieves relevant information from cloud models and a decision maker that synthesizes expert opinions to ensure accurate and context-aware medical advice. Experimental results demonstrate that PrismPrompt outperforms existing models in terms of accuracy, highlighting its potential to improve real-time medical decision-making while preserving the computational feasibility on edge devices. This work provides a promising step towards the broader adoption of cloud-edge collaborative LLMs in healthcare, offering scalable and privacy-conscious solutions for modern medical challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".