AI-Driven Autonomous Artifacts Inverse Design of Personalized Drug Delivery Artifacts for Healthcare Support in Special Education Populations

Abstract

The growing need for personalized healthcare solutions for individuals with specific educational needs has inspired researchers to develop innovative drug delivery solutions for these populations. An autonomous design framework utilizing generative intelligence, inverse design, and interpretable machine learning can be used to develop personalized drug delivery artifacts. This framework considers the relationship between the characteristics of the patient, the drug delivery artifact components, and the desired therapeutic objectives to produce customized drug delivery systems. Generative AI can be used to create different drug delivery systems, and surrogate models can evaluate the performance of these systems. Inverse design techniques can be used to generate these artifacts to meet the objectives of the patient and the drug delivery system. Kolmogorov-Arnold Networks (KANs) can be used to create surrogate models to assess the performance of these drug delivery systems. These models can consider multiple objectives for the customized drug delivery systems, such as efficacy, accessibility, manufacturability, robustness, and cost-effectiveness. The results of these models can help develop novel healthcare solutions that can be applied to populations with special educational needs. The information obtained from these personalized drug delivery systems can help researchers and developers better understand the potential use of generative intelligence, inverse design, and explainable AI in the development of drug delivery systems that can cater to specific groups of patients with special needs in order to better provide healthcare to these populations.