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Dr. Ritu Shrivastava serves as Deputy Director at Sagar Institute of Research and Technology, Bhopal, Madhya Pradesh, India. Her professional responsibilities include academic administration and institutional development. Her role contributes to higher education and research-related activities. Her contribution to this chapter focuses on artificial intelligence approaches for infectious disease identification.
Infectious diseases continue to provide a considerable challenge to global public health due to their swift spread, varied clinical presentations, and ability to result in serious illness and death. Prompt identification and precise categorisation are crucial for prompt intervention, infection management, epidemic avoidance, and efficient public health strategising. Conventional diagnostic methods, such as laboratory analyses, clinical assessments, microscopy, molecular tests, and medical imaging, are beneficial but can be limited by turnaround time, resource constraints, and inconsistencies in diagnostic efficacy. AI and ML provide avenues for the examination of clinical manifestations, laboratory data, medical imaging, pathogen-associated information, and epidemiological statistics to aid in the identification and categorisation of infectious diseases. This section explores AI-driven methodologies for identifying and categorising infectious diseases, such as respiratory infections, tuberculosis, malaria, dengue, sepsis-related infections, and several other transmissible ailments. It addresses data collection, preprocessing, feature extraction, traditional machine learning, deep learning, natural language processing, multimodal learning, interpretable AI, and model assessment.