Potential Uses and Limitations of AI in Cancer and Infectious Disease Assessment Diagnostics: A Quick Reference

Abstract

This review explores the prospective applications and constraints of artificial intelligence (AI) in infectious disease and cancer diagnostics, emphasizing its potential for early detection, precision diagnosis, and personalized treatment planning. The study analyzed AI methodologies like machine learning, deep learning, and natural language processing, and their integration with digital health technologies, focusing on their applications in infectious disease management and oncology. AI demonstrates superior sensitivity and specificity across multiple diagnostic domains, facilitating rapid pathogen identification, outbreak prediction, and cancer lesion detection. Integration with next-generation sequencing enables detection of actionable mutations and risk profiling. Digital health integration through wearables, telemedicine, and electronic health records expands accessibility. Predictive modeling platforms like BlueDot and HealthMap illustrate AI's role in real-time surveillance. AI adoption faces challenges like data bias, interpretability, regulatory uncertainties, and infrastructure constraints. Explicit AI and federated training address privacy, reduce prejudice, and foster trust, while clinical integration requires standardized procedures. AI has great potential to transform the diagnosis of cancer and infectious diseases by providing quick, precise, and scalable solutions. Ensuring fair access and long-term adoption in various healthcare systems will need international collaboration to remove ethical, technological, and regulatory obstacles.

Ähnliche Arbeiten