Editorial: Artificial intelligence solutions for global health and disaster response: challenges and opportunities
Bibliographic record
Abstract
Artificial intelligence solutions for global health and disaster response: challenges and opportunitiesArtificial intelligence (AI) has emerged as a transformative tool in disaster preparedness and response, promising to reshape how we address some of the most pressing global health challenges.The rapid development of AI technologies offers unprecedented opportunities to enhance early warning systems, optimize resource allocation, and facilitate real-time decision-making processes.This editorial introduces a Research Topic of articles that explore the cutting-edge applications of AI across various domains of global health and disaster response, aiming to provide a comprehensive overview of both the potential and the hurdles associated with these technologies.This Research Topic gathers insights from leading researchers, practitioners, and policymakers at the forefront of integrating AI into health crisis management.The scope of discussion spans multiple critical areas, including AI-powered surveillance systems, AIsupported decision-making in resource distribution, and the role of AI in managing mental health during crises.Each contribution advances our understanding of AI's capabilities and critically examines the ethical, legal, and social implications of deploying AI in sensitive and high-stakes environments.Through this Research Topic, we aim to highlight the innovative ways in which AI can promote health equity and improve healthcare delivery during disasters while also addressing the significant challenges that must be overcome to harness the full potential of AI in enhancing global health outcomes.The Research Topic includes original research, systematic reviews, brief research reports, mini reviews, perspective, review, and methods papers.We received 66 submissions, 25 of which, after a careful review process, were accepted for publication in this Research Topic.The paper by Liu P. et al. explores healthcare professionals' and patients' perceptions and experiences regarding mobile health (mHealth) apps in China.The study identifies the advantages and challenges of using mHealth apps using semistructured interviews.It highlights concerns such as the ease of use, effectiveness, and potential risks associated with
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".