Artificial intelligence and machine learning in infectious disease diagnostics: a comprehensive review of applications, challenges, and future directions

Infectious diseases remain a leading cause of illness and death, especially where test results arrive late. The need is simple and urgent: to reach the right diagnosis early, at the point of care, so treatment can start on time and transmission is slowed. Traditional tools, such as culture, expert-read imaging, and lab-intensive molecular tests, are accurate but slow, costly, or difficult to scale in low-resource settings. This is the right moment to improve. Health records are digitised, medical images and routine labs are abundant, sensors are cheaper, and reliable computing now fits in pockets and clinics. Artificial Intelligence and Machine Learning (AI/ML) can turn this data into timely answers. Computer vision reads chest X-rays and microscope slides; models using routine labs and vital signs flag sepsis risk; language models summarize clinical notes; and genomics classifiers help identify pathogens and resistance patterns. When properly evaluated and monitored, these systems deliver faster results, more consistent readings, and earlier warnings, reducing the need for unnecessary antibiotics and prioritising care for the sickest patients. Patients benefit from quicker, targeted therapy; clinicians receive clear decision support; laboratories increase throughput; and public health teams detect outbreaks sooner. To ensure safety and fairness, solutions should be tested across diverse sites, respect privacy, minimise bias, and integrate smoothly into everyday workflows. With these guardrails, AI/ML can facilitate faster, more equitable, and truly scalable infectious disease diagnosis. • Infectious diseases require faster diagnosis to reduce mortality and transmission. • Traditional methods are accurate but slow, costly, and resource-intensive. • AI/ML enables rapid insights from imaging, labs, notes, and genomics. • Discusses the benefits, including targeted therapy, clinician support, lab efficiency, and outbreak detection. • Highlights safe deployment demands validation, bias reduction, privacy, and workflow integration.