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Infectious and noninfectious diseases remain major global health threats, requiring rapid, accurate, and innovative approaches for diagnosis, surveillance, and management. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a powerful catalyst in strengthening disease detection, early outbreak warning, contact tracing, and drug discovery. AI-driven platforms provide real-time insights for vaccine development, prediction of structural proteins, and identification of therapeutic targets, significantly accelerating responses to emerging pathogens. Generative Artificial Intelligence (GenAI) represents a recent breakthrough in healthcare, capable of producing synthetic data, medical images, and clinical text that can assist in diagnosis, enhance clinical decision-making, and improve patient outcomes. Despite its promise, the effective integration of GenAI into routine healthcare is challenged by workforce readiness, medicolegal concerns, ethical implications, and complexities of service delivery. ML and DL continue to revolutionize clinical workflows through applications such as smart electronic health records, medical image interpretation, disease classification, risk prediction, and optimization of clinical trials. DL models—especially Convolutional Neural Networks—excel in processing radiological images including CT scans, X-rays, MRIs (magnetic resonance imaging), and ultrasound, enabling accurate differentiation between normal and pathological conditions. However, AI deployment faces persistent barriers related to data privacy, sensitivity of patient information, interoperability, and the computational demands of large neural networks. Integrating AI with mobile sensing technologies and the Internet of Things (IoT) further increases implementation challenges. Ensuring transparency, fairness, and protection against algorithmic bias is essential to prevent discrimination and maintain trust. Overall, responsible and strategic adoption of AI technologies can significantly strengthen healthcare systems, improve diagnostic accuracy, and better address the needs of an expanding global population.

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Intelligent Diagnosis: Leveraging Artificial Intelligence to Detect and Manage Infectious and Noninfectious Diseases

  • 章节

  • 发表日期：

    2026年

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  • Aroop Mohanty,
  • Shubham,
  • Sakshi Sharma,
  • Atul R. Rukadikar,
  • Ankita Kabi,
  • Shriyansh Srivastava

摘要

Infectious and noninfectious diseases remain major global health threats, requiring rapid, accurate, and innovative approaches for diagnosis, surveillance, and management. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a powerful catalyst in strengthening disease detection, early outbreak warning, contact tracing, and drug discovery. AI-driven platforms provide real-time insights for vaccine development, prediction of structural proteins, and identification of therapeutic targets, significantly accelerating responses to emerging pathogens. Generative Artificial Intelligence (GenAI) represents a recent breakthrough in healthcare, capable of producing synthetic data, medical images, and clinical text that can assist in diagnosis, enhance clinical decision-making, and improve patient outcomes. Despite its promise, the effective integration of GenAI into routine healthcare is challenged by workforce readiness, medicolegal concerns, ethical implications, and complexities of service delivery. ML and DL continue to revolutionize clinical workflows through applications such as smart electronic health records, medical image interpretation, disease classification, risk prediction, and optimization of clinical trials. DL models—especially Convolutional Neural Networks—excel in processing radiological images including CT scans, X-rays, MRIs (magnetic resonance imaging), and ultrasound, enabling accurate differentiation between normal and pathological conditions. However, AI deployment faces persistent barriers related to data privacy, sensitivity of patient information, interoperability, and the computational demands of large neural networks. Integrating AI with mobile sensing technologies and the Internet of Things (IoT) further increases implementation challenges. Ensuring transparency, fairness, and protection against algorithmic bias is essential to prevent discrimination and maintain trust. Overall, responsible and strategic adoption of AI technologies can significantly strengthen healthcare systems, improve diagnostic accuracy, and better address the needs of an expanding global population.