Cleveland Clinic researchers, in collaboration with IBM’s Discovery Accelerator Program, are using Quantum System One to enhance machine learning algorithms for faster and more accurate antibiotic prescriptions, addressing a concerning gap in patient care.
Machine learning algorithms trained on 4.7 million antibiotic susceptibility classifications demonstrated impressive accuracy, outperforming physicians in predicting effective treatments and providing real-time results. By integrating quantum computing, the project intends to refine these algorithms for improved speed and accuracy as well as for use with smaller datasets, making personalized medicine accessible to underserved populations and smaller clinics.
While this study was primarily focused in the domain of urology, the implications of this research go far beyond UTIs. By equipping providers with precise, patient-specific predictions, this technology could reduce the misuse of antibiotics, improve patient outcomes, and slow the progression of resistance patterns.
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