JAMA Medical News Diagnosis and Treatment of Infectious Disease Using AI
7 snips
Feb 14, 2025 Sanjat Kanjilal, an assistant professor at Harvard Medical School, explores how machine learning is reshaping the diagnosis and treatment of infectious diseases. He discusses a groundbreaking study on urinary tract infections, revealing the effectiveness of traditional antibiotics despite outdated guidelines. Kanjilal emphasizes the importance of understanding antimicrobial resistance and the challenges of biases in AI within healthcare. He advocates for better data infrastructure to harness AI's full potential in enhancing patient care and medical decision-making.
AI Snips
Chapters
Transcript
Episode notes
From Antimicrobial Resistance to AI
- Dr. Kanjilal's interest in antimicrobial resistance led him to EHR data analysis.
- This sparked his exploration of machine learning for improving infectious disease diagnosis and treatment.
EHR Data: Potential and Pitfalls
- EHR data contains valuable information for understanding disease processes, like prior antibiotic exposure and infections.
- However, it's crucial to understand the disease and its presence in EHR data before using it for modeling.
Mitigating AI Bias in Healthcare
- Medical societies should prioritize patient needs over AI model deployment, establishing guardrails against bias.
- Researchers must actively study and mitigate AI biases, while educating providers to critically evaluate AI models.