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).
The promise of artificial intelligence (AI) and machine learning to improve care and outcomes, lower the cost of care, and increase patient and provider satisfaction is fast-tracking these disruptive technologies for significant growth in healthcare in the immediate future. In a recent Healthcare IT News/HIMSS Analytics survey, about 35% of healthcare organizations plan to leverage artificial intelligence within two years – and more than 50% intend to do so within five years.* These technologies can categorize and analyze huge amounts of both structured and unstructured data to glean clinical insights to improve individual and population health through better diagnoses, disease pattern identification and treatment methods. They can improve infrastructure, workflows and data management, and other tasks and processes – increasing productivity, consistency and quality, and reducing costs and errors. They can improve the provider-patient experience, allowing physicians to spend more time with patients by automating time-intensive tasks like medical image analyzation, data entry, and procedure and condition monitoring.