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The application of Artificial Intelligence (AI) and Machine Learning (ML) in tropical disease management represents a paradigm shift with great promise for improving health outcomes in affected populations. This chapter explores the diverse applications of AI and ML in this context, including their roles in predictive modeling, diagnostic imaging, data mining, drug discovery, and vaccine development. AI and ML applications like IBM Watson Health for diagnostics, Aidoc for CT scan analysis, AlphaFold for protein prediction, Tempus for personalized cancer treatments, DeepMind Health for kidney injury prediction, Babylon Health for symptom triage, Biofourmis for real-time monitoring, Woebot for mental health support, and the da Vinci Surgical System for precision surgeries are transforming healthcare through enhanced accuracy, efficiency, and accessibility. These technologies are invaluable, from enhancing diagnostic accuracy and accelerating drug discovery to optimizing public health interventions and strengthening disease surveillance systems. By leveraging predictive modeling, AI facilitates early detection and outbreak forecasting, enabling timely and targeted interventions. Furthermore, AI-driven image recognition enhances diagnostic imaging, improving access to healthcare in resource-limited settings. Natural Language Processing (NLP) contributes significantly by mining large datasets, including electronic health records and scientific literature, to uncover disease trends and generate actionable insights. Tools like Epic Systems exemplify NLP for analyzing clinical notes, IBM Watson Health for evidence-based decision-making, Amazon Comprehend Medical for extracting medical information, and Suki AI for voice-enabled documentation assistance, streamlining workflows, and improving care efficiency. AI also plays a transformative role in drug discovery and vaccine development, expediting the identification of novel therapeutic candidates and supporting equitable health solutions. Integrating these technologies into tropical disease management aligns with sustainable development goals, addressing healthcare access, cost-effectiveness, capacity building, and ethical considerations. However, data quality, algorithmic biases, and ethical implications must be addressed to ensure equitable implementation. As AI and ML technologies evolve, their potential to revolutionize tropical disease management becomes increasingly apparent. By fostering global health equity, advancing healthcare delivery, and promoting sustainability, these innovations pave the way for a more effective and inclusive approach to combating tropical diseases.

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Artificial Intelligence and Machine Learning in Tropical Disease Management

  • 章节

  • 发表日期：

    2025年

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  • Matthew Chidozie Ogwu,
  • Sylvester Chibueze Izah

摘要

The application of Artificial Intelligence (AI) and Machine Learning (ML) in tropical disease management represents a paradigm shift with great promise for improving health outcomes in affected populations. This chapter explores the diverse applications of AI and ML in this context, including their roles in predictive modeling, diagnostic imaging, data mining, drug discovery, and vaccine development. AI and ML applications like IBM Watson Health for diagnostics, Aidoc for CT scan analysis, AlphaFold for protein prediction, Tempus for personalized cancer treatments, DeepMind Health for kidney injury prediction, Babylon Health for symptom triage, Biofourmis for real-time monitoring, Woebot for mental health support, and the da Vinci Surgical System for precision surgeries are transforming healthcare through enhanced accuracy, efficiency, and accessibility. These technologies are invaluable, from enhancing diagnostic accuracy and accelerating drug discovery to optimizing public health interventions and strengthening disease surveillance systems. By leveraging predictive modeling, AI facilitates early detection and outbreak forecasting, enabling timely and targeted interventions. Furthermore, AI-driven image recognition enhances diagnostic imaging, improving access to healthcare in resource-limited settings. Natural Language Processing (NLP) contributes significantly by mining large datasets, including electronic health records and scientific literature, to uncover disease trends and generate actionable insights. Tools like Epic Systems exemplify NLP for analyzing clinical notes, IBM Watson Health for evidence-based decision-making, Amazon Comprehend Medical for extracting medical information, and Suki AI for voice-enabled documentation assistance, streamlining workflows, and improving care efficiency. AI also plays a transformative role in drug discovery and vaccine development, expediting the identification of novel therapeutic candidates and supporting equitable health solutions. Integrating these technologies into tropical disease management aligns with sustainable development goals, addressing healthcare access, cost-effectiveness, capacity building, and ethical considerations. However, data quality, algorithmic biases, and ethical implications must be addressed to ensure equitable implementation. As AI and ML technologies evolve, their potential to revolutionize tropical disease management becomes increasingly apparent. By fostering global health equity, advancing healthcare delivery, and promoting sustainability, these innovations pave the way for a more effective and inclusive approach to combating tropical diseases.