Pfizer's ML Accelerates Paxlovid: Years to Months
Pfizer harnessed machine learning for structure-based drug design to develop COVID-19 antiviral Paxlovid in record 4 months, slashing computational processes by 80-90% and enabling lightspeed pandemic response.[1][4]
Read case study →Duke Health's Sepsis Watch: AI Saves Lives Early
Duke Health deployed Sepsis Watch, a deep learning AI that predicts sepsis hours before onset using EHR data. Integrated into clinical workflows since 2018, it enables earlier interventions, reducing mortality and improving care. Backed by real-world studies showing high accuracy and clinician adoption.
Read case study →Insilico Medicine: AI Drug to Phase II in 30 Months
Insilico Medicine harnessed generative AI and deep learning via Pharma.AI to discover Rentosertib (ISM001-055) for idiopathic pulmonary fibrosis, achieving Phase II trials in a record 30 months from project start—slashing traditional 5-year timelines and costs.
Read case study →NYU Langone's NYUTron: AI Predicting Patient Outcomes
NYU Langone Health's custom LLM, NYUTron, trained on millions of clinical notes, outperforms traditional models in forecasting mortality, readmissions, and hospital stays, enabling proactive care with up to 96% AUROC accuracy.
Read case study →HSBC's AI Powerhouse: Fraud ML, NLP Bots & GenAI Surge
HSBC, a global banking leader, harnesses machine learning to screen 1B+ transactions monthly for fraud, deploys NLP chatbots for seamless service, and partners with Mistral for generative AI—slashing false positives, boosting efficiency, and ensuring compliance amid massive scale.
Read case study →RBC NOMI: ML Personalizes Savings & Budgeting
Royal Bank of Canada leverages NOMI AI with machine learning to analyze customer spending, deliver tailored savings tips, budgeting tools, and cash flow forecasts—doubling mobile engagement and boosting financial confidence.
Read case study →PayPal's Deep Learning Fraud Shield Blocks Billions
PayPal deploys machine learning and deep learning models to detect anomalies and fraud in real-time, analyzing hundreds of signals per transaction. This AI system blocks $2B+ in fraud annually, cuts false positives, and safeguards millions of daily payments with evolving threat adaptation.
Read case study →Revolut's ML Anomaly Detection Crushes APP Fraud
Revolut harnesses machine learning for real-time anomaly detection to combat Authorized Push Payment (APP) fraud, achieving a 30% drop in losses from investment scams since February 2024 launch. Protects 35M+ users with proactive interventions.
Read case study →Stanford's AI Boost: Generative Text & Predictive ML
Stanford Health Care pioneers Azure OpenAI in Epic EHR for automated patient correspondence and ML for predictive analytics/computer vision, slashing admin time and enhancing precision medicine amid clinician burnout.
Read case study →Mass General Brigham: AI in Imaging & Surgical Prediction
Mass General Brigham tackles massive medical imaging volumes and clinician burnout with an enterprise AI center deploying computer vision for radiology/pathology and predictive models for operations. Backed by a $30M fund, it manages hundreds of models, boosting diagnostics and outcomes via key partnerships.
Read case study →CBA: ML & GenAI Slash Fraud 70%, Boost Service
Commonwealth Bank of Australia combines machine learning and generative AI to protect customers from scams, reducing losses by 70% and easing contact center burdens with NameCheck, CallerCheck, and virtual assistants.
Read case study →Citibank HK Wealth 360: AI-Powered Wealth Revolution
Citibank Hong Kong's Wealth 360 AI delivers predictive analytics and conversational personalization in its mobile app, transforming personal finance management. Launched in 2024, it boosts engagement with tailored insights, forecasts, and chat interfaces amid rising mobile banking demands.
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