Revolutionizing Patient Care with Medical IoT and Generative AI
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
Generative Artificial Intelligence (GAI) and the Internet of Medical Things (IoMT) are rapidly transforming the healthcare landscape. Their convergence, termed GAIoMT, offers a powerful paradigm that combines GAI’s ability to generate synthetic data, support predictive analytics, and enable autonomous decision-making with IoMT’s real-time sensing and connectivity capabilities. This paper introduces and formalizes the concept of GAIoMT, presenting a layered architectural framework that illustrates how generative models can be integrated across medical devices, data infrastructures, and clinical workflows. A thematically structured review of the current literature is provided along with performance and complexity analysis across representative GAIoMT methods. A practical use case scenario is included to demonstrate real-world applicability, particularly in chronic disease management. Finally, we identify key challenges and outline future research directions for building robust, explainable, and inclusive GAIoMT systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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