Many of us are still warming up to the concept of machine learning and artificial intelligence. To make it easy to understand, artificial intelligence (AI) is a technology that enables a machine to mimic human behavior. While machine learning (ML) is a subset of AI which allows a machine to automatically learn from past data with the help of minimal programming.
Both AI and ML hold promising potentials for transforming the healthcare informatics landscape. Since informatics is basically the representation, processing, and communication of information in natural & engineered systems, there is a need for human connect between all these technological concepts when healthcare comes into picture.
Machine learning in healthcare informatics poses several advantages. Common machine learning algorithms that use deep neural networks for healthcare informatics are convolutional neural networks (CNNs) that play a positive role in medical imaging tasks.
However, the heart sound CNN is linked with disadvantages like class imbalance where healthcare datasets frequently display class imbalances, which tend to bias the model’s metric if ignored. Hence, healthcare stakeholders should gain deep knowledge about machine learning and its role in healthcare informatics in order to mine health data effectively & efficiently.
On the other hand, artificial intelligence is gaining popularity in healthcare informatics. Growing need for precision medicine for personalizing treatment for every individual is becoming imperative.
However, current precision medicine applications in early drug discovery use only a small number of molecular biomarkers to make decisions. Hence, for the development of drugs, artificial intelligence algorithms should be thoroughly investigated to fully personalize the approach in drug design.
To sum up the pros and cons of AI & ML, stakeholders involved in healthcare informatics should invest in continuous R&D and introduce standardization of algorithms for both technologies to fetch uniform outcomes in healthcare systems worldwide.
