Revolutionising Healthcare through Artificial Intelligence: A Systematic Review and Bibliometric Analysis
Notice bibliographique
Résumé
Background: With its innovative solutions for patient care, diagnostics, and operational efficiency, artificial intelligence (AI) is beginning to significantly transform the healthcare industry. In addition to examining new trends and potential directions, this study provides a comprehensive analysis of AI's applications in healthcare. The study is to analyze the potential and difficulties that result from AI integration and to identify important themes, leaders in the field, and important breakthroughs in AI healthcare research. Methods: A PRISMA-based systematic review was carried out, examining 3567 papers on AI and healthcare from Scopus between 2000 and 2023. VOSviewer was used for network research and data visualization. Results: The analysis reveals a sharp increase in AI-related articles that have been published after 2019. Themes that are widely discussed in these publications include machine learning, health informatics, and AI's ability to fight COVID-19. With notable contributions from universities like the University of Victoria and the University of Toronto, the United States leads the world in research output. Conclusions: AI has the potential to completely transform healthcare by boosting patient outcomes, increasing operational effectiveness, and promoting sustainable medical practices. Interoperability, ethical issues, and data privacy are still problems, though. Realizing AI's full potential requires constant cooperation between technologists, medical practitioners, and legislators. Background: With its innovative solutions for patient care, diagnostics, and operational efficiency, artificial intelligence (AI) is beginning to transform the healthcare industry significantly. In addition to examining new trends and potential directions, this study provides a comprehensive analysis of AI's applications in healthcare. The study is to analyze the potential and difficulties that result from AI integration and to identify important themes, leaders in the field, and important breakthroughs in AI healthcare research. Methods: A PRISMA-based systematic review was carried out, examining 3567 papers on AI and healthcare from Scopus between 2000 and 2023. VOSviewer was used for network research and data visualization. Results: The analysis reveals a sharp increase in AI-related articles that have been published after 2019. Themes widely discussed in these publications include machine learning, health informatics, and AI's potential in combating COVID-19. With notable contributions from universities like the University of Victoria and the University of Toronto, the United States leads the world in research output. Conclusions: AI has the potential to completely transform healthcare by boosting patient outcomes, increasing operational effectiveness, and promoting sustainable medical practices. Interoperability, ethical issues, and data privacy are still problems, though. Realizing AI's full potential requires constant cooperation between technologists, medical practitioners, and legislators.
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 | 0,021 | 0,096 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,010 |
| Bibliométrie | 0,107 | 0,097 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».