Machine Learning in the Medical Field: Use Cases & Challenges

Machine Learning in the Medical Field: Use Cases & Challenges

Posted Oct 2, 2021 from

Applications of machine learning in healthcare can help reduce subjectivity and improve accuracy in clinical diagnosis. Read on to learn more.

Machine learning has the potential to disrupt the medical industry by opening up new ways to handle healthcare data, transforming patient care, and streamlining administrative processes. Terabits of medical records, which previously required a human reading, can now be used as input data for machine learning in healthcare projects.

Simply put, machine learning (ML) is a subset of artificial intelligence (AI) that focuses on using algorithms to learn from the data without the need for further programming. Thanks to its ability to learn from experience and adjust to new inputs, ML can perform human-like tasks. 

Being an expert in healthcare software development, Demigos is perfectly placed to shed some light on the current state of machine learning in the medical field, its most promising use cases, and the challenges hindering the wide adoption of ML. 

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