ReviewVirusdisease2023
Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook.
Review in Virusdisease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Next-generation viral detection through AI-enhanced nanotechnology: advances, challenges, and future directions.Frontiers in molecular biosciences · 2026Article
- Clinical research on secure multi-party computation technology for auxiliary respiratory pathogen diagnosis and treatment design in preschool-aged children.Frontiers in pediatrics · 2026Article
- Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.Journal of virology · 2025Review
- Review
- Democratization of Point-of-Care Viral Biosensors: Bridging the Gap from Academia to the Clinic.Biosensors · 2025Review
- Enhancing the response to avian influenza in the US and globally.Lancet regional health. Americas · 2025Review
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome.Environmental science and pollution research international · 2025Review
- Advancing Precision Oncology: Overcoming Treatment Resistance for Personalized Cancer Care.Current molecular medicine · 2025Article
- Non-surgical nursing care for tumor patients: an overview of sedation, analgesia, and recent innovations.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.