The Integration of ChatGPT With the Metaverse for Medical Consultations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 it