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Automate note-taking for doctors, save time and cut down mistakes.
Summarize patient data and get concise information based on medical history
Summarize and understand key insights in medical research with AI, saving time and enhancing patient care.
Automate note-taking for doctors, save time, prevent mistakes, and guarantee precise records.
Create tailored clinical plans for patients combining patient cases with medical literature to enhance care and reduce provider workload.
Detect CKD risk from clinical variables and prioritize patients for enhaced care and cost reduction.
Identify high-risk Heart Failure patients while reducing hospitalizations and costs.
Predict high-risk diabetes patients for early intervention, reducing complications and costs.
AI for COPD early detection and identify high-risk patients.
Detect patients that will have increase rates of mortality and negative obstetric outcomes using clinical variables.
Use patient intake data to predict the length of stay at hospitalization.
Use clinical variables from the EMR of patients to predict antibiotic resistance.
Poor medication adherence causes 125k deaths, 10% hospital admissions, costs $300B yearly. AI can boost adherence, reduce risks.
Urinary tract infections (UTIs) impose significant health and financial burdens, AI allows providers to intervene accordingly.
AI can help healthcare organizations (HCOs) identify individuals at-risk for developing Metabolic syndrome (MetS).
Readmissions are expensive for hospitals. AI identifies who are the most likely to be readmitted to take preventive measures.
Operational efficiency determines profitability for healthcare organizations (HCOs), AI boosts productivity up to 44%.
Improving the product mix increases competitiveness, customer satisfaction, profitability and efficiency.
AI systems can use past trends and market signals to forecast demand.
Access to the market in the pharmaceutical industry refers to identifying, evaluating, and taking advantage of market opportunities effectively.
50% of clinical trials fail due to inadequate designs or implementation errors, learn how AI solves these challenges.
Hospitals can leverage predictive analytics to identify patients likely to be at high risk of infections or complications.
Increase appointment attendance rates, reduce the financial burden of no-shows, and improve the health outcomes with Artificial Intelligence.
Prior Authorization causes delays in medical attention and increases administrative burden. Improve overall healthcare system efficiency with AI.
Predict drug adverse effects with Artificial Intelligence, increasing patient health and satisfaction.
Improve adherence rates by 20%-30%, optimize healthcare resources, reduce associated costs and improve patient outcomes.
Automated Inventory Management, order routing, demand forecasting and Supply Chain Optimization with AI.
About 1 in 8 women will develop invasive breast cancer throughout their lifetime. Prevent it by identifying potential signs at an early stage.
Cervical cancer prediction with AI to improve efficiency of early detection and diagnosis.
Predicting and preventing suicide, by identifying early high risk patients.
Addressing challenges in triage services with AI by automating tasks and reducing waiting times in waiting lists and administrative burden.
Diagnosing rare diseases is difficult and time-consuming, and there is often no cure. AI improves diagnosis and treatment for patients.
Advanced AI to Detect Drugs and Adverse Reactions in Text from Social Media.
AI offers educational support tools for doctors in their daily basics to efficiently manage patients.
An Expert AI Assistant for Writting Insurance Approval Letters.
30% of patients with diabetes develop disease-related complications. AI-based assistants offer personalized recommendations to improve habits.
Lung cancer screening based on various critical lung cancer risk factors, including age, smoking history, and family cancer history.
Early detection and personalized treatment of prostate cancer.
AI in early gastric cancer detection to reduce mortality and economic burden.
Prediction of hematologic cancers through AI to enhance the precision and efficiency in early detection and management of blood cancers.
CVDs cause millions of deaths and high costs; AI can identify high-risk patients, improve care, and reduce expenses.
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