Open research challenges and future directions for multimodal healthcare security
Abstract
The swift advancement of multimodal artificial intelligence (AI) has changed the face of integration of diverse data types, such as medical images, texts, biosignals, genomics, wearables, and clinical data in the field of medicine. However, the deployment of reliable, secure, and scaled multimodal healthcare systems is still limited due to a number of pivotal and toothsome research questions. This chapter discusses the technology, security, ethics, and sustainability-related obstacles to the use of multimodal intelligence in healthcare systems and their pinpointed deficiencies. Core issues of data and fusion heterogeneity, scaling and real-time processing, and model generalization and robustness are scrutinized. The chapter also discusses concerns of privacy-preserving learning and secure data exchange, federated systems, and ethical responsibilities of self-governing clinical systems. Aside from the challenges delineated in the section are futuristic research endeavors focused on the advancement of next-generation healthcare systems. Self-supervised and foundation models, neuro-symbolic and explainable AIs, and other human-centric personalized healthcare systems are emerging paradigms that augment trust, interpretability, and adaptability. Sustainability-aware AI design and low power algorithms for edge devices are other emerging technologies highlighted in this section. Also, multimodal digital twins, AI-powered remote diagnostics, wearables, and quantum health computing are discussed. Finally, this chapter provides a coherent vision for the design of secure, energy-efficient, and smart multimodal health systems.
