Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications (2411.03782v1)

Abstract: Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While AI models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field's maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine.

• The paper demonstrates that multimodal AI achieves an average 6.2 percentage point AUC improvement over unimodal models in diverse clinical settings.
• The study synthesizes findings from 432 studies to detail fusion strategies and address challenges like heterogeneous data and integration timing.
• The review underscores the need for robust datasets and methodological innovations to overcome regulatory and interoperability barriers for clinical use.

Overview of Multimodal AI in Medicine Paper

The paper "Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications" provides a comprehensive scoping review of the current state, challenges, and future directions of multimodal AI development in the medical domain. It acknowledges the rapid advancement in healthcare technologies and the corresponding increase in patient data quantity and diversity. Traditional unimodal AI models, which focus primarily on single data modalities, have exhibited notable success in clinical applications. However, the integration of complementary data sources through multimodal AI promises further enhancement in clinical decision-making.

Key Findings

The review highlights that multimodal AI models generally surpass their unimodal counterparts, with, on average, a 6.2 percentage point improvement in the area under the receiver operating characteristics curve (AUC). The paper synthesizes insights from 432 studies across various medical disciplines examined from 2018 to 2024, emphasizing architectural approaches, fusion strategies, and application areas.

Technical Challenges

Despite the promising results, the paper identifies several persisting challenges associated with the development of multimodal AI systems. Issues such as cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets are recurring themes. The lack of interoperability between data storage systems from different medical domains adds to the complexity. Furthermore, the inconsistent availability of modalities within a dataset poses a substantial hurdle for efficient training and model utilization. The review also explores the design complexities and methodological constraints inherent in multimodal fusion strategies. While intermediate fusion is prevalent, considerations regarding the integration timing (early, intermediate, or late fusion) remain critical for effective model design.

Clinical Implications and Future Directions

The paper acknowledges the lag in real-world clinical implementation of multimodal AI compared to unimodal models, citing regulatory and data management hurdles as significant barriers. Interestingly, no FDA- or CE-certified multimodal AI models were found among the relevant regulatory databases, reiterating the gap between research and clinical practice.

For future research, the paper encourages efforts to develop robust datasets and improve cross-modality learnability to address missing data challenges. Publicly available datasets have shown significant influences in stimulating research interest in specific medical domains, a trend that should be sustained across more diversified areas to facilitate broader multimodal AI developments. The need for comprehensive explainability of multimodal AI systems is underscored as a priority to gain trust and transparency in clinical environments.

Conclusion

This review enriches our understanding of the capabilities and challenges of multimodal AI in medicine. While the theoretical improvements offered by multimodal AI are recognized, the paper calls for more extensive dataset curation and methodological innovation to bridge the gap toward clinical integration. As research continues to evolve, the potential for multimodal AI to transform healthcare delivery remains a promising horizon yet to be fully realized. This paper stands as a pivotal reference for researchers seeking to navigate and contribute to the maturing landscape of multimodal AI in medicine.