Viewpoint on the Intersection Among Health Information, Misinformation, and Generative AI Technologies
Bibliographic record
Abstract
In recent years, artificial intelligence (AI) has seen rapid advancements, with innovations such as large language models and generative AI evolving at a rapid pace. While this progress offers tremendous opportunities, it also presents risks, particularly in the creation, consumption, and amplification of information and its impact on population health and health program delivery. Thoughtful approaches are necessary to navigate the consequences of advances in AI for different health care professionals and patient populations and from a policy and governance perspective. Through a collaboration between the World Federation of Public Health Associations working groups, this Viewpoint article brings together perspectives, concerns, and aspirations from young adult professionals across 5 continents and from diverse backgrounds to explore the future of public health and AI in the context of the changing health information environment. Our discussion is divided into 2 parts, specifically examining aspects of disinformation and AI, and also the role of public health and medical professionals in a growing AI-driven health information ecosystem. This Viewpoint concludes with 5 key recommendations on how to potentially address issues such as information and disinformation overload; misinformation propagation; and resultant changes in health practices, research, ethics, and the need for robust policies that can dynamically address current and future challenges.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".