The Integration of ChatGPT With the Metaverse for Medical Consultations
Bibliographic record
Abstract
Recent years witnessed a promising synergy between healthcare and the Metaverse leading to the development of virtual healthcare environments. This convergence offers accessible and immersive healthcare experiences and holds the potential for transforming the delivery of medical services and enhancing patient outcomes. However, the reliance on specialist presence in the metaverse for medical support remains a challenge. On the other hand, the newly launched Large Language Model (LLM) chatbot, the ChatGPT of OpenAI, has emerged as a game-changer, providing human-like responses and facilitating interactive conversations. By integrating this cutting-edge language model with the Metaverse for medical purposes, we can potentially revolutionize healthcare delivery, enhance access to care, and increase patient engagement. This study proposes a new medical Metaverse model utilizing GPT-4 as a content creator, highlighting its potential, addressing challenges and limitations, and exploring various application fields. We conclude by outlining our ongoing efforts to transform this concept into a practical reality.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".