Neural Networks in Healthcare: A Critical Review of AI-Assisted Diagnostics
Keywords:
Artificial Intelligence, neural networks, healthcare diagnostics, convolutional neural networks, recurrent neural networks, explainable AI, medical imaging, algorithmic bias, clinical decision support, ethical concerns in AIAbstract
Artificial Intelligence (AI), particularly neural networks, has emerged as a transformative force in modern healthcare diagnostics. This critical review evaluates the role, effectiveness, and limitations of neural network-based diagnostic systems in clinical settings. Neural networks, inspired by the human brain’s architecture, have demonstrated high accuracy in interpreting medical data such as imaging, pathology, and electronic health records. Their application ranges from early disease detection—including cancer, cardiovascular disorders, and neurological conditions—to predictive analytics that support personalized treatment planning. Convolutional Neural Networks (CNNs) have especially revolutionized radiology by outperforming traditional diagnostic techniques in detecting anomalies in X-rays, MRIs, and CT scans. Meanwhile, Recurrent Neural Networks (RNNs) are gaining traction in sequential data analysis, aiding in the prognosis of chronic illnesses.
