Rethinking Personalized Medicine: AI’s Limits in Clinical Trials
Published in Artificial Intelligence, Journals, Links, Personalized medicine.
Summary: A new study reveals limitations in the current use of mathematical models for personalized medicine, particularly in schizophrenia treatment. Although these models can predict patient outcomes in specific clinical trials, they fail when applied to different trials, challenging the reliability of AI-driven algorithms in diverse settings.
This study underscores the need for algorithms to demonstrate effectiveness in multiple contexts before they can be truly trusted. The findings highlight a significant gap between the potential of personalized medicine and its current practical application, especially given the variability in clinical trials and real-world medical settings.
Key Facts:
Mathematical models currently used for personalized medicine are effective within specific clinical trials but fail to generalize across different trials.
The study raises concerns about the application of AI and machine learning in personalized medicine, especially for conditions like schizophrenia where treatment response varies greatly among individuals.
The research suggests that more comprehensive data sharing and inclusion of additional environmental variables could improve the reliability and accuracy of AI algorithms in medical treatments.
Source: Yale
The quest for personalized medicine, a medical approach in which practitioners use a patient’s unique genetic profile to tailor individual treatment, has emerged as a critical goal in the health care sector. But a new Yale-led study shows that the mathematical models currently available to predict treatments have limited effectiveness.
https://neurosciencenews.com/ai-personalized-medicine-25502/