Multi-Agent Systems (MAS) for Remote Healthcare with Enhanced Efficiency and Trust through Quantum-Model Methodology and Validation
Bibliographic record
Abstract
Artificial intelligence (AI) chatbots have improved rapidly. However, these systems still face challenges in complex and time-sensitive issues where real-time awareness is imperative, such as remote emergent care. To address these limitations, a Multi-Agent System (MAS) was developed that employs a collection of AI agents with unique and distinct tasks, ranging from symptom analysis and user proficiency to risk assessment and information verification. In conjunction, these agents work together to enhance the clarity of output and thereby mitigate the hallucinatory effects associated with traditional single-agent systems. The trust dynamics of the human-AI team were measured quantitatively using a novel quantum model, implemented with Qiskit. In a human subject experiment, the MAS system significantly reduced the number of follow-up questions and achieved higher trust scores than the single-agent system, indicating the model’s validity. These results suggest that MAS-based systems can substantially improve the reliability and effectiveness of remote emergency care, offering a promising new direction for digital healthcare support. Future research will extend validation across broader populations and emergency scenarios.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".