How is Machine Learning used in Medical Diagnosis
Machine Learning in the medical field will improve a patient’s health with minimum costs. Use cases of ML are making a near-perfect diagnosis, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles.
Diagnostic errors account for about 10% of yearly patient deaths, mostly due to issues like poor tracking, misinformation, and miscommunication. Machine Learning can help both save practitioners valuable time in automation as well as improve accuracy and outcomes.
Chatbots and Conversation
Much of the value in diagnosis comes from the natural conversation between doctor and patient, and a doctor’s ability to suss out the relevant symptoms from the patient is key. Machine learning-powered chatbots can do a good chunk of that work without the need for an actual doctor: asking targeted questions and recommending the appropriate courses of action (visit a pharmacy or a doctor).
Oncology
More and more studies are showing the benefits of early detection of cancer. Stanford recently developed an algorithm that does as well as doctors in identifying skin cancer and a similar idea for lung cancer is deployed in 35 hospitals in China by 12 Sigma.
Pathology
Analyzing bodily fluids and tissue is part of the core of diagnostics, and algorithms can help beyond just oncology. Advances in digital pathology are providing more and more quality input images as training data from under a microscope.
Scanning and MRI
Researchers who applied deep learning to MRI images at early ages (6 to 12 months) were able to reliably predict and diagnose Autism. Neural nets have also been used for MRI image segmentation to delineate the boundaries of certain tissue.
Rare Diseases
Facial recognition software is being combined with machine learning to help clinicians diagnose rare diseases. Patient photos are analyzed using facial analysis and deep learning to detect phenotypes that correlate with rare genetic diseases.
Machine Learning-based Behavioral Modification
Behavioral modification is an important part of preventive medicine, and ever since the proliferation of machine learning in healthcare, countless startups are cropping up in the fields of cancer prevention and identification, patient treatment, etc. Somatix is a B2B2C-based data analytics company which has released an ML-based app to recognize gestures which we make in our daily lives, allowing us to understand our unconscious behavior and make necessary changes.
Smart Health Records
Maintaining up-to-date health records is an exhaustive process, and while technology has played its part in easing the data entry process, the truth is that even now, a majority of the processes take a lot of time to complete. The main role of machine learning in healthcare is to ease processes to save time, effort, and money. Document classification methods using vector machines and ML-based OCR recognition techniques are slowly gathering steam, such as Google’s Cloud Vision API and MATLAB’s machine learning-based handwriting recognition technology. MIT is today at the cutting edge of developing the next generation of intelligent, smart health records, which will incorporate ML-based tools from the ground up to help with diagnosis, clinical treatment suggestions, etc.
