The Algorithmic Virologist: Integrating Artificial Intelligence Across Viral Detection, Diagnosis, Surveillance, Prediction, and Therapeutics

Algorithmic Virologist

Abstract

      Artificial intelligence is rapidly revolutionizing medical virology, yet its applications remain fragmented, ranging from virus detection and clinical diagnosis to epidemiological prediction, variant monitoring, and treatment development. This review provides a comprehensive overview of AI principles, from basic genomic biology and drug discovery to clinical physiology, and proposes the generation of integrated knowledge, under human supervision, through an "algorithmic virologist" as a conceptual framework to bridge these currently disparate fields. The research assesses deep learning, machine learning, hybrid computational approaches and recent multimodal and generative AI systems with explicit emphasis on their biological validity, diagnostic performance, generalizability, interpretability and translational ability. Current evidence shows that AI can improve reference-free detection of viral sequences, facilitate and inform multimodal clinical diagnosis, combine genomic and epidemiological signals for outbreak and variant prediction, speed up anti-viral target identification and virtual screening and help optimize therapeutics. Yet the review also points out an enduring divide – between computational prowess and real-world clinical utility. Heterogeneous and geographically non-representative datasets, small reference databases of viruses, lack of external and prospective validation, algorithmic black-boxing, potential model drift due to viral and epidemiological changes over time, inadequate interoperability with clinical technologies or other data in use at the bedside or by public health practitioners (and inadequate access at scale) are major barriers to implementation. Consequently, the study proposes that the progress that follows cannot be based on predictive accuracy alone but also needs to keep in mind biological plausibility, external and temporal validity, assessment of uncertainty, explainability; reproducibility; equity and measurable clinical or public-health benefit. Accordingly, it is proposed that the algorithmic virologist is not just a medicine for human knowledge, but an integrated computer ecosystem that links detection, diagnosis, characterization, prediction, monitoring, therapeutic discovery, therapeutic improvement, and continuous verification. Responsibly integrated AI, the review concludes, could facilitate a transition from largely reactive to predictive and anticipatory virology, if they are biologically grounded algorithmic innovations that are experimentally validated, clinically accountable and meaningfully overseen by humans.