Overjet is an early-stage VC-backed startup building the future of data-driven dentistry. We are using AI to transform the $130B dental care market and improve patient outcomes. We are seeking an entrepreneurially-minded a highly skilled developer who is comfortable with backend software development including deploying machine learning models, loves challenges and is passionate about impacting lives. Please email your resume to email@example.com. Develop machine learning pipelines Deploy machine learning models for inference Implement and maintain metrics for tracking ML models performance Design and develop microservices and APIs related to data ingestion, machine learning and product quality Ensuring responsiveness of applications.
Over the past decade, machine learning techniques have made substantial advances in many domains. In health care, global interest in the potential of machine learning has increased; for example, a deep learning algorithm has shown high accuracy in detecting diabetic retinopathy.1 There have been suggestions that machine learning will drive changes in health care within a few years, specifically in medical disciplines that require more accurate prognostic models (eg, oncology) and those based on pattern recognition (eg, radiology and pathology).
Enter Microsoft Women Health Risk Assessment competition, develop machine learning solutions to accurately categorize young women for their particular health risk. Enter now - submissions due Sep 30. Microsoft is awarding up to 5,000 in cash prizes for the Women's Health Risk Assessment competition. They are calling for optimized machine learning solutions that allow a young woman (age 15-30 years old) to be accurately categorized for their particular health risk. Based on the category a patient falls within, healthcare providers can offer appropriate education and training programs to help reduce the patient's reproductive health risks.
This study centers on how computer-based decision procedures, under the broad umbrella of artificial intelligence (AI), can assist in improving health and health care. Although advanced statistics and machine learning provide the foundation for AI, there are currently revolutionary advances underway in the sub-field of neural networks. This has created tremendous excitement in many fields of science, including in medicine and public health. First demonstrations have already emerged showing that deep neural networks can perform as well as the best human clinicians in well-defined diagnostic tasks. In addition, AI-based tools are already appearing in health-oriented apps that can be employed on handheld, networked devices such as smart phones.