ReviewInterdisciplinary perspectives on infectious diseases2025
A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis.
Review in Interdisciplinary perspectives on infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Pharmacological advances inJournal of enzyme inhibition and medicinal chemistry · 2026Review
- Antimicrobial Stewardship, Laboratory Surveillance, and Quality Management in Africa: A Narrative Review of Challenges and Opportunities for Integrated AMR Control.Health science reports · 2026Article
- Artificial Intelligence in Infectious Disease Care: Selected Applications in Tuberculosis, Sepsis, and Antimicrobial Stewardship.Diagnostics (Basel, Switzerland) · 2026Review
- Medical biotechnology and artificial intelligence powered companion diagnostics: indispensable pillars driving next generation precision health care.Biotechnology letters · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- From burden to action: Saudi Arabia's strategy on antimicrobial resistance.Open medicine (Warsaw, Poland) · 2026Review
- An explainable prognostic prediction panel for sepsis based on serum amino acid profiles.Frontiers in immunology · 2026Article
- AI-Generated Antibiotic Therapies for Acute Periprosthetic Joint Infections with Implant Retention in Comparison with an Interdisciplinary Team.Antibiotics (Basel, Switzerland) · 2025Article
- Combating antimicrobial resistance in Asia: A comprehensive review of strategic action plans, policies, and the role of the WHO.Saudi medical journal · 2025Review
- From AI-AssistedPharmaceuticals (Basel, Switzerland) · 2025Review
- The Changing Landscape of Antibiotic Treatment: Reevaluating Treatment Length in the Age of New Agents.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper explores the transformative potential of integrating artificial intelligence (AI) in the diagnosis and prognosis of infectious diseases. By analyzing diverse datasets, including clinical symptoms, laboratory results, and imaging data, AI algorithms can significantly enhance early detection and personalized treatment strategies. This paper reviews how AI-driven models improve diagnostic accuracy, predict patient outcomes, and contribute to effective disease management. It also addresses the challenges and ethical considerations associated with AI, including data privacy, algorithmic bias, and equitable access to healthcare. Highlighting case studies and recent advancements, the paper underscores AI's role in revolutionizing infectious disease management and its implications for future healthcare delivery.
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.