AI-driven clinical decision support systems for personalized treatment planning

AI-driven Clinical Decision Support Systems (CDSS) are transforming personalized treatment planning in healthcare. Traditional CDSS models which rely on rule-based algorithms are limited in providing individualized care as they offer generalized treatment recommendations. This research explores how AI-enhanced CDSS utilises machine learning models, natural language processing (NLP) and deep learning techniques to overcome these limitations by incorporating patient-specific data to realise personalized treatment planning in healthcare. The research also examines the effectiveness of AI-CDSS in improving patient outcomes, with a focus on oncology, cardiology, and chronic disease management. Findings show that the integration of machine learning, natural language processing, and deep learning techniques into CDSS demonstrates the ability to optimize treatment outcomes in clinical areas like oncology, cardiology and chronic disease management. AI systems like IBM Watson for oncology and sepsis treatment algorithms illustrate potential of AI in improving diagnostic accuracy, reducing treatment-related risks and enhancing overall patient care. Despite these promising advancements, several challenges such as the need for high-quality, diverse datasets, overcoming clinician hesitancy and navigating regulatory and ethical concerns remain. Future research should focus on enhancing AI system transparency, improving data integration with emerging technologies like wearables and genomics, and developing comprehensive ethical frameworks for AI deployment in healthcare. These findings highlight the transformative potential of AI in clinical settings, ushering in a new era of personalized, data-driven healthcare.