Multimodal Machine Learning in Image-Based and Clinical Biomedicine:
Survey and Prospects
- URL: http://arxiv.org/abs/2311.02332v5
- Date: Sat, 20 Jan 2024 04:36:50 GMT
- Title: Multimodal Machine Learning in Image-Based and Clinical Biomedicine:
Survey and Prospects
- Authors: Elisa Warner, Joonsang Lee, William Hsu, Tanveer Syeda-Mahmood,
Charles Kahn, Olivier Gevaert and Arvind Rao
- Abstract summary: The paper explores the transformative potential of multimodal models for clinical predictions.
Despite advancements, challenges such as data biases and the scarcity of "big data" in many biomedical domains persist.
- Score: 2.1070612998322438
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning (ML) applications in medical artificial intelligence (AI)
systems have shifted from traditional and statistical methods to increasing
application of deep learning models. This survey navigates the current
landscape of multimodal ML, focusing on its profound impact on medical image
analysis and clinical decision support systems. Emphasizing challenges and
innovations in addressing multimodal representation, fusion, translation,
alignment, and co-learning, the paper explores the transformative potential of
multimodal models for clinical predictions. It also highlights the need for
principled assessments and practical implementation of such models, bringing
attention to the dynamics between decision support systems and healthcare
providers and personnel. Despite advancements, challenges such as data biases
and the scarcity of "big data" in many biomedical domains persist. We conclude
with a discussion on principled innovation and collaborative efforts to further
the mission of seamless integration of multimodal ML models into biomedical
practice.
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