2nd Artificial Intelligence in Infectious Diseases Workshop 2025
Sanjat Kanjilal
MD, MPH
Brigham & Women’s Hospital, Harvard Medical School & Harvard Pilgrim Healthcare Institute, United States
Biography
Dr. Kanjilal is a Group Leader in the Department of Medical Microbiology and Infection Prevention at the Amsterdam University Medical Center. His research interests lie in the application of machine learning algorithms to observational and experimental data to improve the diagnosis and management of infectious diseases. Specific areas of work include building AI decision support tools to assist healthcare providers in diagnosing infection and choosing optimal treatments. His long-term goal is to lay the foundation for a learning health system through research and practice. This involves building better AI models with healthcare data, better interfaces that actually help clinicians make better decisions, infrastructure to automate complex analytic pipelines, and safety mechanisms to ensure these systems do not harm patients and lastly, running trials to evaluate ML algorithms in real world environments.
RELATED MATERIALS
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CME Series | Implementing AI in Infectious Disease Management
The advantages and limitations of AI-based tools for supporting early diagnosis and decision-making are becoming increasingly recognized in clinical practice. In the first lecture, you will learn how AI can support early patient screening and risk stratification in antimicrobial resistance (AMR) stewardship.
The second lecture focuses on ways to leverage AI to perform a multi-center analysis on microbiology data. It provides insights into the benefits of collaboration among different centers, challenges to data sharing and analysis, and optimal strategies for the implementation of existing AI-based solutions.
What Will Set You Apart?
After following this educational series, participants will be able to:
- Define strategies for early detection and risk stratification to ensure timely, evidence-based antimicrobial prescription
- Analyse the role of AI in early detection and real-time monitoring to support decision-making in clinical practice
- Discuss the advantages of multi-center mircobiologic analysis in understanding microbe epidemiology, their spread, and defining populations at increased risk
- Summarise technological pillars required for complex data analyses involving multiple healthcare centers
Is This Program for You?
This program is designed for clinicians, residents, pharmacists, researchers, and healthcare teams involved in the implementation of AI technology.
What Will You Cover in This Module?
- AI in early detection, risk stratification and antimicrobial stewardship
- AI for multi-center microbiologic analyses
- AI implementation in healthcare systems