ML In Healthcare: How Machine Learning Impacts Healthcare Industry

May 26,2020 | 11:45 PM

Industry leaders and upcoming startups are running head-to-head in the race to leverage Machine Learning (ML) and develop healthcare applications. It’s a well-known fact that IBM’s Watson and Google’s DeepMind are transforming the healthcare industry. Similarly, Nervanasys, Ayasdi,, and CascadeMD are tech startups who contribute towards the latest innovations in Machine Learning for healthcare. They aim to address the most challenging problems in the healthcare industry, especially in medical diagnosis and management information systems.

Unstructured Data to Structured Data

CascadeMD is an AI-driven documentation workflow system that helps automate data entry for doctors. The transcription engine receives unstructured data from the doctor's dictation notes and transcribes these audio files into transcripts. Furthermore, the AI engine maps the patient's medical information to the respective fields in the EHR with a higher level of accuracy. This reduces the workload of doctors and affords them more time to spend on treating patients.

ML in Medical Diagnosis

Computer Vision, a breakthrough in ML and Deep Learning (DL) is now applied to image diagnostic tools for its advanced pattern recognition capabilities. Microsoft’s InnerEye is making notable advancements in ML-driven image analysis for tumor detection. However, a vast repository of medical data including rich and varied forms of medical imagery is a pre-requisite for the development of ML-driven diagnostic tools and applications. A trained deep learning system is good at predictive analytics and makes more accurate predictions and recommendations on a patient’s health condition. Conversely, it cannot explain the logic behind its predictions even if accurate. So, it mandates human involvement and expertise to leverage the advancements in ML for healthcare and validate the outcomes.

ML in Treatment Recommendations

Providers and healthcare organizations employ a myriad of processes and procedures to improve the accuracy of medical diagnosis. It involves a complex combination of human intelligence that compares and analyses every aspect of a patient’s body to determine their health condition. Currently, machines can assist providers to arrive at the right decisions related to diagnosis and treatment. They extend support to complement the scientific knowledge of providers and healthcare organizations.

ML in Medical Data Collection

Healthcare is evolving by the minute owing to the ever-changing health risks prevalent among the general public. ML can support the health management information system by collecting and collating live health data from patients across the globe via mobile devices. Apple has developed the ResearchKit in its pursuit to leverage ML for healthcare. Currently, it aims to aggregate live health data for the treatment of Parkinson’s disease and Asperger’s syndrome using interactive healthcare applications powered by ML. Likewise, ML in clinical documentation supports archiving and managing millions of patient records for providers to deliver better healthcare. Apps like CascadeMD leverage Artificial Intelligence to infer meaningful insights from the provider’s real-time observations and historical data and make them available for consumption by the Electronic Medical Records (EMR).

Machine Learning is applied to drug discovery and robotic surgery in addition to medical diagnosis, treatment recommendations, and medical data collection. However, the healthcare industry has several practical implications in implementing ML-driven healthcare applications for healthcare institutions, providers, and patients. Scientists and researchers are continuously experimenting with emerging technologies to make sense of live health data from the dynamic healthcare environment.

CascadeMD Blog Author
Shan M