Editorial: Pioneers & pathfinders: 10 years of frontiers in medicine
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,007 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,016 | 0,010 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,015 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,025 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».