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Breaking the ‘intellectual bottleneck’: How AI is computing the previously uncomputible in healthcare
- AI at University of Texas Medical Branch (UTMB) analyzes CT scans for cardiac risk scores, enabling proactive care.
- The AI identifies patients at high cardiovascular risk, regardless of the scan's initial purpose.
- AI helps in early detection by scanning for coronary artery calcification, a predictor of heart disease risk.
- Automated processes analyze CT scans and provide risk tiers for patients based on AI-derived scores.
- UTMB also employs AI for stroke and pulmonary embolism detection, aiding care teams with rapid findings.
- The facility ensures model performance by validating algorithms pre and post-deployment, addressing bias and error detection.
- To prevent anchoring bias, UTMB utilizes 'peer learning' techniques and assesses radiologist responses to AI-highlighted anomalies.
- AI tools help in flagging anomalies, improving diagnostic accuracy and reducing missed findings.
- UTMB's AI systems extend to areas like assisting inpatient admission justifications and examining gaps in care for proactive measures.
- AI's role in healthcare is crucial in processing vast data feeds efficiently and addressing the existing intellectual bottleneck to enhance proactive healthcare practices.
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