Published Paper Details:

Artificial intelligence (AI) is progressively reshaping medical practice, leveraging advancements in digitized data acquisition, machine learning, and computing infrastructure. Previously exclusive to human experts, AI applications now permeate diverse biomedical domains. This Review Article delineates recent AI breakthroughs, their biomedical implications, challenges hindering further progress in medical AI systems, and the economic, legal, and social ramifications of AI in healthcare. Our approach incorporates Python, benefiting from its extensive array of freely available packages spanning various domains. Key scientific Python libraries, including NumPy, SciPy, and pandas, offer efficient implementations of numerical operations, catering to common scientific and engineering tasks. These libraries provide a robust foundation for developing advanced scientific software without delving into low-level algorithms. Furthermore, domain-specific packages tailored to meteorological requirements supplement our methodology. Leveraging Convolutional Neural Networks, we classify diverse diseases alongside state-of-the-art Machine Learning algorithms.