Glossary Term
Machine Learning (ML)
Definition
Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data without explicit programming. In the context of medicine, ML algorithms analyze large datasets to identify patterns, make diagnoses, predict outcomes, and optimize clinical workflows.
Relevance to the MedTech Industry
Machine Learning accelerates innovation in healthcare by enabling personalized medicine, improving diagnostic accuracy, and streamlining operations. It helps medical professionals process complex data from imaging, genomics, electronic health records (EHRs), and wearable devices to enhance patient outcomes.
Additional Information & Related Terms
Examples of Applications in Medicine
Medical Imaging: ML algorithms in radiology and pathology detect anomalies in X-rays, MRIs, and CT scans, such as identifying tumors or fractures.
Genomics: Analyzing genetic data to predict disease risk and guide personalized treatment plans.
Drug Discovery: Accelerating the identification of potential drug candidates by analyzing molecular and clinical data.
Remote Monitoring: Wearables and IoT devices use ML to monitor vital signs and alert clinicians to early signs of deterioration.
Clinical Decision Support (CDS): Assisting healthcare providers by predicting patient outcomes or suggesting treatment options based on historical data.
Related Terms
Deep Learning: A subset of ML using neural networks to analyze large and complex datasets, common in medical imaging and genomics.
Natural Language Processing (NLP): ML technique for analyzing text data, used in EHRs and medical literature.
Artificial Intelligence (AI): The broader field encompassing ML and other technologies that simulate human intelligence.
Predictive Analytics: Using ML to forecast patient outcomes or disease progression.
FDA SaMD Framework: Guidance for the development and regulation of machine learning-based software used in medical devices.