Enhancing Medical Diagnostic Accuracy through Multimodal AI Models in Clinical Environments
Keywords:
Multimodal AI, medical diagnostics, clinical decision support, healthcare data fusion, artificial intelligence in medicine, diagnostic accuracyAbstract
The integration of multimodal artificial intelligence (AI) in clinical environments offers promising advancements in diagnostic accuracy by leveraging diverse data sources such as imaging, clinical records, and genomics. This paper explores how multimodal AI models improve diagnosis compared to unimodal systems, highlights existing literature on the subject, and presents a structured analysis of their performance, implementation challenges, and ethical implications. The review concludes that multimodal models demonstrate superior accuracy, robustness, and adaptability in real-world clinical settings, provided that data governance and interoperability standards are met.
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Copyright (c) 2023 Henrique Castro Vieira (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
