Intelligent systems for healthcare diagnostics and treatment

Artificial Intelligence (AI) is revolutionizing healthcare by augmenting diagnostic accuracy, personalizing treatment plans, and accelerating drug discovery processes. This review synthesizes current literature and case studies to explore AI's multifaceted applications in healthcare.
The review begins by examining AI's role in early disease detection, highlighting its use of advanced techniques such as deep learning and neural networks for analyzing medical images with unprecedented precision. Case studies underscore AI's success in detecting cancers and predicting cardiovascular risks, demonstrating significant advancements in diagnostic capabilities.
Moving to personalized treatment, the review explores how AI integrates genomic data, electronic health records (EHRs), and lifestyle information to tailor medical interventions. AI-driven predictive analytics enable the forecasting of treatment outcomes and the recommendation of optimal personalized therapies, enhancing patient care and treatment efficacy.
In the realm of drug discovery and development, AI algorithms expedite the identification of potential drug targets, optimize molecular designs, and predict clinical trial outcomes. Case examples illustrate AI's effectiveness in discovering novel compounds and repurposing existing drugs, promising accelerated innovation in pharmaceutical research.
Ethical considerations, including data privacy and algorithmic bias, are critically analyzed alongside regulatory frameworks like GDPR and FDA guidelines, ensuring responsible AI implementation in healthcare.
Ultimately, this review underscores AI's transformative impact on healthcare delivery, offering insights into its potential to reshape medical practices, improve patient outcomes, and pave the way for future advancements in precision medicine and therapeutic innovation.