An analytic research and review of the literature on practice of artificial intelligence in healthcare
Bibliographic record
Abstract
Artificial intelligence (AI) has transformed healthcare, particularly in robot-assisted surgery, rehabilitation, medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and adherence, and administrative applications. AI enhances pre-operative planning, intraoperative guidance, and post-operative outcomes in robotic surgery. In rehabilitation, AI enables personalized programs, physical therapy using robotics, and in real time monitoring and feedback mechanisms. The integration of AI with emerging technologies like augmented reality, virtual reality, and the Internet of Things holds promise for broader healthcare applications. However, AI adoption faces technical challenges related to data quality and bias, ethical and privacy concerns, regulatory and legal considerations, and issues of cost and accessibility. Future trends include advances in AI algorithms and robotics, integration with emerging technologies, and the potential for wider applications in healthcare and rehabilitation. Addressing ethical and security considerations is crucial for the successful integration of AI in healthcare while upholding patient safety and legal standards. Overcoming regulatory, ethical, and trust-based challenges with effective governance will be critical to the full realization of AI potential in healthcare artificial intelligence (AI)-driven healthcare solutions powered by IoT can enable in real time patient monitoring, enhancing early diagnosis and chronic illness management. AI applications in AR/VR can transform medical education by allowing healthcare professionals to practice intricate procedures in a safe environment. Wearable technology with AI-driven analytics can offer personalized health insights, facilitating proactive interventions and improved patient outcomes. Adopting these innovations can foster progress, enhance patient care, and boost overall healthcare efficiency. Future studies should refine these cross-disciplinary applications, ensure their smooth incorporation into current healthcare systems, and tackle potential ethical and security issues.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".