Summary of Navigating the Landscape Of Multimodal Ai in Medicine: a Scoping Review on Technical Challenges and Clinical Applications, by Daan Schouten et al.
Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications
by Daan Schouten, Giulia Nicoletti, Bas Dille, Catherine Chia, Pierpaolo Vendittelli, Megan Schuurmans, Geert Litjens, Nadieh Khalili
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This scoping review examines deep learning-based multimodal artificial intelligence (AI) applications across medical disciplines, analyzing 432 papers published between 2018 and 2024. The study reveals that multimodal AI models outperform unimodal counterparts by an average of 6.2 percentage points in AUC. Despite promising results, challenges persist, including heterogeneous data characteristics, incomplete datasets, and cross-departmental coordination. To address these challenges, the paper discusses potential strategies for clinical implementation, including commercially available multimodal AI models. The review also identifies key factors driving multimodal AI development and proposes recommendations to accelerate its maturation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal artificial intelligence (AI) in medicine is a growing field that combines multiple data sources to improve clinical decision-making. This paper looks at how deep learning-based multimodal AI applications are being used across different medical disciplines. Researchers analyzed 432 papers published between 2018 and 2024 and found that these models can be more accurate than those using individual data modalities. However, there are still some challenges to overcome, such as dealing with different types of data and coordinating efforts across different departments. |
Keywords
» Artificial intelligence » Auc » Deep learning
