I am a pediatric cardiologist and have cared for children with heart disease for the past three decades. In addition, I have an educational background in business and finance as well as healthcare administration and global health – I gained a Masters Degree in Public Health from UCLA and taught Global Health there after I completed the program.
Artificial intelligence is a very active computer science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including medicine. Therefore, it is no surprise that innovation plays an important role in delivering better medical and health.
Initial efforts in AI and its application in medicine began in the 1960s, which focused mainly on diagnosis and therapy. The best-known early works on AI in medicine include Stanford’s Ted Shortlife and his innovative program MYCIN; MYCIN was a rule-based expert system with if–then rules with certainty values that recommended selection of antibiotics for various infectious diseases [1]. Although not used in actual clinical setting, MYCIN was proved to be superior to human infectious disease experts.
Szolovits edited a textbook on artificial intelligence in medicine in 1982 with a collection of papers on various topics in this domain [2]. During this early era, academic centers involving AI in medicine included Stanford, MIT, Rutgers, and Pittsburgh in the United States as well as a few centers in Europe. A biennial meeting on artifcial intelligence in medicine (The European Society for Artificial Intelligence in Medicine, or AIME) was started in Marseille, France in 1987. Towards the end of the era, Szolovits organized a course on medical artificial intelligence at MIT in 2005 that was one of the first organized educational efforts on this burgeoning topic.
During this period, popular AI methodologies other than the traditional expert systems included fuzzy logic and neural network, with the latter applied to various clinical situations such as clinical diagnosis and medical images as well as in the critical care setting [3, 4]. The emergence of the “trifecta” of: large volumes of available data that required new computational methodologies (or simply Big Data), the escalation of computational power and cloud computing, and the advent of machine learning have promulgated the recent new era of artificial intelligence.
Present popular concepts of machine learning include supervised methodologies such as support vector machines, neural networks, and naive Bayesian classifiers, as well as unsupervised techniques such as k-means clustering and principal component analysis. Recent hybrid techniques such as semi-supervised sequence learning can be used with less labeled data.
In 2012, the team from University of Toronto used a deep learning algorithm with 650,000 neurons and 5 convolutional layers to reduce the error rate by half during a computer vision challenge. In addition, the IBM supercomputer Watson with its victory in the game show Jeopardy, heralded the era of cognitive computing with its potent natural language processing, knowledge representation and reasoning capabilities.
Finally, the DeepMind AlphaGo program defeat of the human Go champion Lee Sedol proved that the computer and deep learning can reach new heights and further advance human understanding in certain topics. The present state of AI in medicine includes a myriad of applications.
