Notice bibliographique
Résumé
What Is the 2025 Watch List? The Watch List is an annual Horizon Scan report from Canada’s Drug Agency that presents emerging technologies and issues that have potential to shape the future of health care in Canada. The 2025 Watch List focuses on the use of artificial intelligence (AI) technologies in health care and the issues that may arise with the implementation of these technologies. AI technologies have the potential to significantly transform health care systems. These technologies could increase efficiency by reducing administrative burden, improve patient outcomes, and enhance patient experience by creating more access points to the health care system. However, there are also legal, ethical, environmental, and social implications with the rollout of these technologies. Why Is This an Issue? Substantial public and private investments are being made in AI technologies for health care. AI technologies are already being implemented in some parts of the Canadian health care system. Commercial options, such as ChatGPT, allow AI technologies to be used by patients to assist with their health care journeys. Because they are readily available and easy to use, these same tools are sometimes used by clinicians and, in some cases, without sanction or training from employers or regulators. AI health care technologies also present an opportunity to fundamentally change health care by its ability to replace, displace, or augment tasks that have traditionally required human cognition. The potential health human resources impact of machines taking on some this load is significant given the increasing demand for health care services and the finite capacity of health care systems in Canada. What Is the Potential Impact? The Watch List signals which technologies are poised to make an impact and the policies, regulatory or organizational enablers, and/or guardrails that are needed to optimize the proliferation of these technologies in the health care system. The 2025 Watch List also focuses on considerations for optimizing and accelerating implementation, such as the massive potential impact on operations, clinical outcomes, and staff and patient experience, while minimizing risks. What Else Do We Need to Know? The 2025 Watch List of AI technologies and issues in health care was developed through consensus-based decision-making at a workshop in November 2024 including individuals from across Canada with experience and expertise in AI. The 2025 Watch List identifies and describes the top 5 new and emerging AI technologies in health care. Examples include AI for notetaking and AI for disease detection and diagnosis. We also explore some considerations for health care decision-makers about the impact these technologies may have on health human resources, health care infrastructure, and health equity. The 2025 Watch List also identifies the top 5 issues related to AI technologies in health care. Examples include the importance of establishing guidelines around what data are used to train AI algorithms and how that might contribute to bias as well as considerations about the liability and accountability of health care providers and systems that use these technologies. These are key issues that warrant more attention and will influence the wider adoption, diffusion, and implementation of new and emerging AI technologies. Monitoring ongoing developments and evidence related to the top technologies and issues highlighted in the 2025 Watch List can help guide health system planning in Canada and improve access to high-quality care.
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 | 0,007 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,018 | 0,014 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,135 | 0,078 |
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 ».