REVOLUTIONIZING HEALTHCARE WITH DEEP LEARNING: APPLICATIONS OF TENSORFLOW IN DIGITAL MEDICINE

This paper explores the transformative impact of TensorFlow, a deep learning framework, on the evolving field of digital medicine. The integration of artificial intelligence (AI) and machine learning (ML) into healthcare has enabled the development of advanced diagnostic tools, automated clinical workflows, and personalized treatment plans. TensorFlow, developed by Google, provides scalable solutions for building complex neural networks, particularly for image analysis, electronic health records (EHRs), genetic research, and speech-based diagnostics. This study presents a detailed examination of TensorFlow’s core features, followed by a case study on dermatological disease diagnosis using convolutional neural networks (CNNs). Furthermore, the paper outlines existing challenges, including data privacy concerns and model interpretability, while highlighting future directions for the integration of AI in digital healthcare systems. By leveraging TensorFlow, healthcare professionals can enhance early detection capabilities, optimize resource allocation, and improve patient outcomes globally. Keywords: Digital medicine, TensorFlow, deep learning, healthcare AI, medical imaging, electronic health records, convolutional neural networks.