A survey on artificial intelligence and robotic systems in biomedical applications: challenges and future prospects
Bibliographic record
Abstract
The rapid emergence of new disease variants, ongoing health issues, various types of accidents, and the continuous challenges associated with aging pose significant burdens that the healthcare sector must address daily. Historically, these challenges were managed through manual methods until the advent of smart technology. The integration of smart technologies, such as AI-driven robotics, has created a paradigm shift in healthcare. However, the growth and global acceptance of these innovative systems face several challenges, including interpretability, uncertainty, and clinical trust; human-robot interaction in clinical settings; robustness, adversarial safety, and cybersecurity; hardware limitations; effective sensing and multimodal data integration; and the need for outcome-oriented, long-term clinical trials, among others. This review presents enhanced approaches to tackle these challenges through the application of advanced AI-powered robotics. Key strategies include the development of sophisticated AI models, replacing single-sensor systems with sensor fusion (incorporating vision, force, and biosignals) to prevent failures, secure model execution through encrypted inference and hardware enclaves, implementing shared and adjustable autonomy frameworks, enhancing anomaly detection and input validation, and utilizing adversarial training and robust optimization with certified bounds.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".