Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges

Abstract

Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.

KEY MESSAGES

• AI and ML are revolutionizing human activities in various fields, and infectious diseases are not exempt from their rapid and exponential growth.

• Despite some notable challenges, explainable AI/ML could provide insights into the decision-making process, making the outcomes of models more transparent.

• Improved transparency can help to build trust among healthcare professionals, policymakers, and the general public in leveraging AI/ML-based systems to face the growing challenges of infectious diseases in the present century.

Keywords:

• Artificial intelligence
• machine learning
• explainability
• interpretability
• deep learning
• infectious diseases

Authors contributions

DRG: conceptualization, writing of original draft, review, and editing; JF, YZ: conceptualization, review and editing.

Disclosure statement

DRG and JF are section editors at the same journal. Outside the submitted work, DRG reports investigator-initiated grants from Pfizer, Shionogi, and Gilead Italia, and speaker/advisor fees from Pfizer, Menarini, and Tillotts Pharma. The authors have no other conflicts of interest to disclose.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Funding

No funding was received.