Research consultancy
The integration of Artificial Intelligence (AI) into modern medicine presents transformative potential in enhancing diagnostic accuracy, prognostic evaluations, and clinical decision-making. This review explores the evolution and application of various AI techniques within the medical domain, tracing their origins from classical logic to contemporary computational models. The study specifically examines the structure and utility of Artificial Neural Networks (ANNs), fuzzy expert systems, Genetic Algorithms, and hybrid intelligent systems. ANNs have demonstrated notable success in pattern recognition and non-linear data analysis for diagnosing conditions such as myocardial infarction and interpreting complex medical images. Fuzzy logic systems, by accommodating uncertainty and partial truths, support nuanced clinical decision-making in oncology and anaesthesia. Genetic Algorithms contribute to optimising diagnostic protocols and identifying treatment outcomes by simulating evolutionary processes. Furthermore, hybrid intelligent systems synergise multiple AI techniques to enhance diagnostic precision and adaptability across varied clinical scenarios. Despite these advancements, barriers such as clinician scepticism, ethical considerations, and limited empirical validation hinder widespread adoption. The study underscores the need for further clinical trials and interdisciplinary collaboration to bridge the gap between AI innovation and routine medical practice, paving the way for more efficient, data-driven healthcare delivery.
