Editorial: Pioneers & pathfinders: 10 years of frontiers in medicine
Bibliographic record
Abstract
As Frontiers in Medicine celebrates its 10th anniversary as a journal in the top 25% of its category, we invited authors to submit papers reporting what they considered as meaningful advances, worth publishing in different sections of the journal as part of this research topic. Based on the contributions received and the input of our section editors, we mention here below key developments in different medical disciplines -emphasizing the growing impact of artificial intelligence (AI). Indeed, the paper by Mian et al. highlighted the explosive growth of artificial intelligence (AI) in healthcare, documenting over 1,800 publications from 97 countries between 2019 and 2023 in their bibliometric analysis of AI in medicine. Their study identified key progress areas, emerging fields, and leading contributors-including prominent countries, institutions, and researchers-providing valuable insights into current collaborative frameworks and potential future research directions (1). Among the many domains where AI is making an impact, precision oncology exemplifies its transformative potential, enabling more personalized cancer care through enhanced diagnostic accuracy, Hasham and Sultan have emphasized its growing impact in pediatric oncology, where AI-driven innovations hold promise for improving diagnosis and tailoring therapies for young patients (2). Despite these promising advances, the field remains in its infancy, and significant implementation challenges persist. While the success of AI in precision medicine underscores its ability to address complex medical problems, access to advanced tools remains confined to wellresourced healthcare systems. Moving forward, sustained progress will depend on the establishment of rigorous methodological standards, robust ethical frameworks, and the integration of real-world data with the goal of benefiting all global population. As part of this research topic, the current and anticipated contributions of AI are also discussed in dermatology (3), gastroenterology (4), and intensive care/anesthesiology (5), nephrology (6,7) and rheumatology (8,9). Clearly, regulatory science and public health (10) will also benefit from AI developments. In this new era, it will be essential to maintain public trust in the recommendations made by experts, taking into consideration that the opinions expressed might be conflicting and influenced by political considerations (11).Furthermore, the tremendous potential of analytical techniques for deciphering genotypephenotype relationship has been emphasized by Victoria Bunik (12). She underlines that the field requires development of public databases on genetic variety and associated disease diagnostics, as well as specific programs in medical education.Several other themes are covered in this research topic, including new applications of radiopharmaceuticals in oncology and autoimmune diseases (13,14) as well as new targeted therapeutic modalities in hematology (15,16). We also received an important contribution on the impact of education of healthcare professions with a focus on emotional intelligence (17).We warmly hope that the value of this series of articles will be recognized and incentivize new submissions to our journal which is now established as a flagship among open access medical publications.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.031 | 0.025 |
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".