IBM Quantum Computer Boosting AI to Improve Antibiotic Treatment

Cleveland Clinic researchers are integrating quantum computing with machine learning to predict the most effective antibiotic treatment for urinary tract infections (UTIs).

The initiative, among the first to apply quantum-enabled machine learning in medicine, aims to enhance patient outcomes and combat antibiotic resistance. The researchers are using Cleveland Clinic’s on-site IBM Quantum System One, the world's first quantum computer dedicated to health care research.

Traditionally, identifying the appropriate antibiotic for a UTI requires approximately three days for urine culture results. During this period, prescribers use their judgment to select an antibiotic but about 30% fail to effectively treat the infection contributing to prolonged patient suffering and the rise of antibiotic-resistant strains.

To address this, the research team trained machine learning algorithms on more than 4.7 million antibiotic susceptibility classifications from urine cultures collected over a decade. These algorithms consider patient demographics, comorbidities, hospital resistance patterns and geographical data to provide real-time predictions of antibiotic effectiveness.

The algorithm demonstrated high accuracy across 11 tested antibiotics and outperformed physician-prescribed treatments in providing adequate coverage. By integrating quantum computing, the team aims to refine these algorithms further, enhancing speed and accuracy, and enabling effective training on smaller datasets. This advancement could make personalized medicine more accessible, particularly in underserved populations and smaller clinics.

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This collaboration between AI and quantum computing showcases the potential for personalized medicine on a global scale. Beyond UTIs, such technologies could reshape how diseases are diagnosed and treated, opening avenues for startups and enterprises in the quantum computing and health care AI industries, and potentially addressing global health challenges.