Description
Predict personalised health outcomes to recommend individual treatment approach
Benefits & ROI
1.2
Key considerations
Upside4
Linked Case Studies
Case Study
Imperial College London
Imperial College Researchers diagnose ovarian cancer patients with survival rates under 2 years
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Anthem
Anthem aims to predict the occurrence of allergies using machine learning
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Blue Cross Blue Shield
Blue Cross Blue Shield predicts individual propensity for opioid abuse with 85% accuracy to modify insurance pricing and support appropriate interventions using machine learning analytics
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Cigna
Cigna to identify at-risk patients from laboratory results and clinical diagnostic data, and offer early treatment options using machine learning
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Institute Gustave Roussy
Scientists at Institute Gustave Roussy predict immunotherapy efficacy in patients with machine learning
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University of Toronto
University of Toronto aims to early detect individuals at risk for clinical decline with the use of machine learning
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Cornell University
Researchers at Cornell University are using deep learning to develop dental restorations with better accuracy than human eye
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Banner Health
Banner Health saves $29m in three years by helping avoid hospitalisation for patients with multiple chronic conditions by remote monitoring of vitals and analysis with machine learning
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"Zhongshan Ophthalmic Centre, Sun Yat-sen University"
Sun Yat-sen University researchers predict high myopia for young adults at clinically acceptable levels 8 years in advance
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MIT
MIT researchers aim to make cancer treatments less toxic with machine learning
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PLA General Hospital
PLA General Hospital in Beijing is using an algorithm for predicting if patients will wake up from a coma
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Takeda Pharmaceutical Company
Takeda and ConvergeHEALTH aim to better understand how treatment-resistant depression responds to medication using deep learning models
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Harvard University
Researchers at Harvard University predict tuberculosis'' resistance to first-line and second-line drugs with 94% accuracy and 90% accuracy using machine learning
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Biogen
Biogen to design individualized treatment plans for newly diagnosed patients with providers using machine learning
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Government of Japan
Japan to automate patient information entry and some medical procedures by investing in 10 AI-powered hospitals
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Imperial College London
Researchers develop a model to provide individualised and clinically interpretable treatment decisions to improve sepsis patients outcomes
Case Study
Pharmaceutical Company
Pharmaceutical company identifies warnings for non-Hodgkin’s lymphoma patients requiring change of treatment using machine learning
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Source: mckinsey.com · Editor: origina;