Machine Learning V/S Artificial Intelligence for Health IT: Which is Better?
Many of us are still learning about the concept of machine learning and artificial intelligence. To aid understanding, artificial intelligence (AI) is a technology that allows a machine to mimic human behavior. Whereas Machine Learning (ML) is a subset of AI that allows a machine to automatically learn from past data using minimal programming.
AI and ML hold promising potentials to transform the health informatics landscape. Since computing is essentially the representation, processing, and communication of information in natural and man-made systems, a human connection is needed between all these technological concepts when healthcare comes into the picture.
Machine learning in health informatics has several advantages. Common machine learning algorithms that use deep neural networks for health informatics are convolutional neural networks (CNN) which play a positive role in medical imaging tasks.
However, CNN heart sound is linked to drawbacks such as class imbalance where healthcare datasets frequently display class imbalances, which tend to bias the model metric if ignored. Therefore, healthcare actors need to acquire in-depth knowledge about machine learning and its role in healthcare informatics in order to leverage healthcare data effectively and efficiently.
On the other hand, artificial intelligence is gaining popularity in health informatics. The growing need for precision medicine to personalize treatment for each individual is becoming imperative.
However, current applications of precision medicine in early drug discovery use only a small number of molecular biomarkers to make decisions. Therefore, for drug development, artificial intelligence algorithms should be thoroughly studied in order to fully customize the drug design approach.
To summarize the advantages and disadvantages of AI and ML, stakeholders involved in health informatics should invest in continuous R&D and introduce standardization of algorithms for both technologies to achieve uniform results in health systems around the world.