Version 1
: Received: 22 August 2024 / Approved: 22 August 2024 / Online: 23 August 2024 (11:29:40 CEST)
How to cite:
Cascella, M.; Leoni, M. L.; Shariff, M. N.; Varrassi, G. AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine. Preprints2024, 2024081668. https://doi.org/10.20944/preprints202408.1668.v1
Cascella, M.; Leoni, M. L.; Shariff, M. N.; Varrassi, G. AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine. Preprints 2024, 2024081668. https://doi.org/10.20944/preprints202408.1668.v1
Cascella, M.; Leoni, M. L.; Shariff, M. N.; Varrassi, G. AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine. Preprints2024, 2024081668. https://doi.org/10.20944/preprints202408.1668.v1
APA Style
Cascella, M., Leoni, M. L., Shariff, M. N., & Varrassi, G. (2024). AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine. Preprints. https://doi.org/10.20944/preprints202408.1668.v1
Chicago/Turabian Style
Cascella, M., Mohammed Naveed Shariff and Giustino Varrassi. 2024 "AI-driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine" Preprints. https://doi.org/10.20944/preprints202408.1668.v1
Abstract
Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims at dissecting the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic methods can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. Ethical considerations related to patient privacy, biases, and lack of reliability and generalizability are the main issues. Furthermore, there is a need for high-quality real-word validation as well as the development of standardized protocols and policy rules to guide the implementation of these technologies in diverse clinical settings.
Medicine and Pharmacology, Anesthesiology and Pain Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
