Artificial intelligence and machine learning in healthcare

Machine learning is an artificial intelligence (AI) technique that can be used to solve various tasks. Machine learning algorithms can analyze large amounts of different data with accurate results.

Increased use of information systems in the health service and the digitization of patient information generates large amounts of data. As a result, the healthcare sector has a lot of data that can be difficult to interpret. Machine learning can be an opportunity to systematize and present the large amount of information and data in an intuitive way.

Machine learning systems differ from traditional software systems. Machine learning uses self-learning algorithms that continuously improve. Each algorithm has its strengths and weaknesses, so it's important to try several algorithms to find out which ones work best.

In healthcare, machine learning can be used in three areas:

• Interpretation of medical images (eye diseases, radiology, pathology)
• Prognostics (dementia, metastatic cancer, stroke)
• Diagnostics (oncology, pathology, rare diseases)

Healthcare is becoming more proactive with the help of artificial intelligence and machine learning, but there is still a need for more research and development before the potential can be fully realized. Developments in the interpretation of medical images have come the furthest, and much will happen here in the coming years. It will take around five years before the prognostics area is ready to use machine learning. Diagnostics is the most complicated area of healthcare, and it will take around ten years before machine learning solutions can be used.