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Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook

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Abstract

In the rapidly evolving field of clinical virology, technological advancements have always played a pivotal role in driving transformative changes. This comprehensive review delves into the burgeoning integration of artificial intelligence (AI), machine learning, and deep learning into virological research and practice. As we elucidate, these computational tools have significantly enhanced diagnostic precision, therapeutic interventions, and epidemiological monitoring. Through in-depth analyses of notable case studies, we showcase how algorithms can optimize viral genome sequencing, accelerate drug discovery, and offer predictive insights into viral outbreaks. However, with these advancements come inherent challenges, particularly in data security, algorithmic biases, and ethical considerations. Addressing these challenges head-on, we discuss potential remedial measures and underscore the significance of interdisciplinary collaboration between virologists, data scientists, and ethicists. Conclusively, this review posits an outlook that anticipates a symbiotic relationship between AI-driven tools and virology, heralding a new era of proactive and personalized patient care.

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Authors and Affiliations

  1. Department of Microbiology, All India Institute of Medical Sciences, Rajkot, Gujarat, 360110, India

    Abhishek Padhi & Ashwini Agarwal

  2. Centre for Advanced Research (CFAR), Faculty of Medicine, King George’s Medical University (KGMU), Lucknow, India

    Shailendra K. Saxena

  3. All India Institute of Medical Sciences, Rajkot, Gujarat, 360110, India

    C. D. S. Katoch

Authors
  1. Abhishek Padhi
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  2. Ashwini Agarwal
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  3. Shailendra K. Saxena
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  4. C. D. S. Katoch
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Correspondence to Ashwini Agarwal.

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Padhi, A., Agarwal, A., Saxena, S.K. et al. Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook. VirusDis. 34, 345–355 (2023). https://doi.org/10.1007/s13337-023-00841-y

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