AI Changing Diagnostics in Healthcare
For modern medicine, advancements in science have given each generation of medical professionals an edge on diagnostic accuracy. Technological advances have contributed to these improvements, yet one application of a modern technology has the potential to cause a quantum change to the quality of healthcare and diagnostic: artificial intelligence (AI).
Though AI has been a development since the 1950s, only until recent years has it become significantly relevant to healthcare. AI has a handful of levels of applications, but two have developed that are changing the game: “machine learning” and “deep learning.”
Machine learning is based on so-called neural networks (a computer system modeled on the human brain). These applications involve multilevel probabilistic analysis, allowing computers to simulate and build upon the way the human mind processes data. As a result, not even computer programmers understand how their computer programs derive solutions.
AI machine learning is capable to being fed myriads of data about patients and making accurate diagnoses, as well as monitoring the potential dangers that current patients may be facing as regards a deterioration of health status due to neglect or oversight. One of the most endangered sectors of hospital populations is the group of patients that move from short term to ICU care. Machine learning can help medical professionals limit the number of in-house patients needing intensive care by 50% by identifying signs of deteriorating health before nurses and doctors can.
The other AI variant, deep learning, utilizes software to learn to recognize patterns in distinct layers. In healthcare this is becoming increasingly useful. Because each neural network layer operates both independently and integrally with the other layers – separating aspects such as color, size and shape before integrating the outcomes – these newer visual tools enable the transformation of diagnostics. They could even search for cancer at the individual cell level.
Two independent studies found that 50%-60% of women in the US who receive regular mammograms over 10 years will receive at least one false result that wrongly indicates the possibility of cancer. This requires additional testing and, sometimes, unnecessary procedures. As much as one-third of the time, two or more radiologists looking at the same mammography disagree on their interpretation of the results. Deep learning can quantify diagnostics and ultimately limit the number of unnecessary interventions.
Additionally, AI can observe the decision-making processes of exceptional medical professionals and learn from them, even exceeding their performance.
As healthcare and AI draw closer and discover novel possibilities, more people will benefit from accurate diagnostics. In 20 years, experts predict that 47% of healthcare professionals will depend entirely on AI applications to perform their diagnostic services. Healthcare is in for a fantastic development.