Descriptive and Predictive Analysis of Artificial Intelligence Research and Innovation in Health
Bibliographic record
Abstract
Objectives: The aim of this study is to assess the research and innovation status of artificial intelligence (AI) in health sciences with a special focus on different application areas in health sciences and start-ups companies. Materials and Methods:Here in, two different datasets were used for analysis.The Web of Science database was analyzed to examine the scientific and technological knowledge production of AI technology in the health sector in general and in predetermined application areas.Secondly, the database of the technological investment portal dealroom.co,which includes innovative start-up organizations that produce AI-based solutions in the field of digital therapeutics and healthtech, was investigated.Results: In terms of contribution to AI-related literature, the USA and China lead in AI-focused publications, while Germany (22.2%) and the USA (21%) have the most health-specific coverage.Italy, Canada and the England follow these countries respectively (13.8%).Türkiye's rate was found to be 10.1%.On the other hand, the United Kingdom (n=24) and Israel (n=21) stand out in terms of AI-powered start-ups in the health sector.Türkiye stands in the top ten countries distributing to AI-powered health science research but has no companies in the same field.Conclusion: AI in healthcare is on the rise, driven by increased research and applications, especially during the coronavirus disease-2019 pandemic.Some health subtopics remain underexplored, but start-ups are making promising strides.Wider AI adoption in healthcare is expected as financial and regulatory challenges are addressed.
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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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".