Responsible adoption of multimodal artificial intelligence in health care: promises and challenges
Bibliographic record
Abstract
Clinicians rely on various data modalities-such as patient history, clinical signs, imaging, and laboratory results-to improve decision making. Multimodal artificial intelligence (AI) systems are emerging as powerful tools to process these diverse data types; however, the clinical adoption of multimodal AI systems is challenging because of data heterogeneity and integration complexities. The 2024 Temerty Centre for AI Research and Education in Medicine symposium, held on June 17, 2024, in Toronto, Canada, explored the potential and challenges of implementing multimodal AI in health care. In this Review, we summarise insights from the symposium. We discuss current applications, such as those used in early diagnosis of sepsis and cardiology, and identify key barriers, including fusion techniques, model selection, generalisation, fairness, safety, security, and international considerations on the responsible deployment of multimodal AI in health care. We outline practical strategies to overcome these obstacles, emphasising technologies such as federated learning to reduce bias and promote equitable health care. By addressing these challenges, multimodal AI can transform clinical practice and improve patient outcomes worldwide.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".