Inovatyvių sveikatos technologijų pritaikymo pajėgumų didinimas pasaulinėje sveikatos priežiūros sistemoje.
Bibliographic record
Abstract
more equitable and safe. There is a substantial gap between AI promises and its actual delivery in healthcare settings. AI is a social-technical system and AI technology alone cannot solve our health equity issue. This is the research question: What social, political, and economic elements in the global health system must be addressed such that the capacities of AI can be optimized? The paper employs qualitative research to seek expert opinions, investigate the success cases of implementing particular AI devices in Hong Kong and Singapore, and integrate the lessons learned from the adoption of 87 AI-technology initiatives in a large Canadian hospital. The author recommends how the supply side increases their trustworthiness and the demand side grows trust. The supply side includes technology providers, legal, policy, and professional organizations, venture capitalists, and academic research institutions need to provide responsible AI and govern AI for long-term benefits. Increasing trust from healthcare organizations, including the presence of champions, organization alignments, funding mechanisms, new professional identities, patients' digital and health literacy capabilities, and supportive organization culture, is recommended. The same AI devices should be interoperable among the healthcare systems in and outside their countries. Standardized data quality assessment, benchmarking datasets, funding mechanisms, and agreement on model and clinical performance measures need to be used to facilitate comparison across products and settings. Investment in supporting digital infrastructure in low and middle-income countries is essential for the effective operation of AI devices. Various stakeholders must continuously demystify AI and participate in the collaborative work with those who have less power in the system.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".