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Artificial intelligence and infectious diseases: Scope and perspectives.

S Abbara1, Y Crabol2, J Goupil de Bouillé3

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Artificial intelligence (AI) and machine learning (ML) are transforming infectious disease care, enhancing diagnostics, prognosis, and drug discovery. Challenges in AI implementation require rigorous evaluation for safe and equitable adoption.

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Area of Science:

  • Infectious Diseases
  • Medical Informatics
  • Computational Biology

Background:

  • Artificial intelligence (AI) is increasingly integrated into healthcare, particularly in infectious disease practice.
  • Machine learning (ML) models leverage routine care and research data for various applications.
  • Large language models (LLMs) offer advanced capabilities in processing clinical narratives and supporting decision-making.

Purpose of the Study:

  • To review current AI applications in infectious disease practice.
  • To evaluate the evidential strengths and limitations of AI tools.
  • To propose a roadmap for AI adoption aligned with clinical and regulatory needs.

Main Methods:

  • Synthesis of current literature on AI in infectious diseases.
  • Analysis of AI applications in diagnostics, prognostics, antimicrobial stewardship, and drug discovery.
  • Identification of challenges and requirements for real-world AI deployment.

Main Results:

  • AI enhances infectious disease diagnostics (e.g., image analysis, pathogen identification) and prognostics (e.g., predicting clinical deterioration).
  • ML models improve antimicrobial stewardship and accelerate drug discovery.
  • LLMs transform clinical data, aid in cohort generation, and provide bedside decision support.
  • Deployment challenges include costs, implementation constraints, model transferability, and governance of risks.

Conclusions:

  • AI offers significant potential to revolutionize infectious disease management.
  • Addressing implementation hurdles and establishing robust evaluation frameworks are crucial for safe and effective AI adoption.
  • A translational roadmap is needed to bridge technical innovation with clinical and regulatory realities.