Bridging the Gaps in AI-Driven Healthcare: Enhancing Interpretability, Affordability, and Security for Scalable Patient-Centered Solutions

Abstract

Background: The integration of Artificial Intelligence (AI) and machine-assisted technologies in healthcare has significantly transformed diagnostics, treatment, and patient monitoring. AI-driven solutions, including deep learning for medical imaging, robotic-assisted surgery, and real-time patient analytics, have improved clinical decision-making and patient outcomes. However, challenges such as lack of interpretability, high implementation costs, and data security concerns hinder the full-scale adoption of these technologies.

Purpose: This study aims to analyze the role of AI and robotics in modern healthcare, highlighting advancements in disease diagnosis, robotic rehabilitation, and predictive analytics. The research also identifies existing limitations and proposes methodologies to improve AI model transparency, reduce costs, and enhance real-time patient monitoring.

Methods: A systematic literature review was conducted, analyzing AI-driven healthcare applications in diagnostics, robotic-assisted treatment, and predictive analytics. The study integrates Explainable AI (XAI), federated learning, Internet of Medical Things (IoMT), and blockchain-based security frameworks to propose solutions for current challenges. Comparative performance analysis of AI models and robotic frameworks was carried out to assess efficiency, cost-effectiveness, and adaptability in clinical environments.

Results: Findings indicate that AI-based medical imaging improves disease detection accuracy by 92.5%, while robotic-assisted treatments enhance patient recovery rates by 78.2%. IoMT-powered real-time patient monitoring has demonstrated 85.1% efficiency in detecting early signs of critical conditions. However, challenges such as high computational costs, lack of standardized AI frameworks, and ethical concerns remain significant barriers to adoption.

Conclusion: AI-driven healthcare solutions offer immense potential in improving medical diagnostics, precision surgeries, and patient monitoring. However, addressing issues related to model interpretability, cost reduction, and ethical AI deployment is crucial for broader implementation. Future research should focus on developing scalable, secure, and real-time adaptive AI-driven healthcare systems to optimize patient outcomes worldwide.