What aspects of Artificial Intelligence matter most in healthcare?

Supervised and Unsupervised Algorithms

Most AI applications depend on algorithms, which describe a logical process that follows a set of rules. Computers can be taught a series of steps in order to process large amounts of data to produce a desired outcome. Two forms of algorithm have been used in the healthcare space:


  • Supervised algorithms use ‘training datasets’ in which the input factors and output are known in advance. Supervised processes can produce highly accurate algorithms because the ‘right answers’ are already known. For example, scientists may feed a dataset of retina images into the algorithm in which board-certified physicians have already identified and agreed upon diagnoses for each image.
  • Unsupervised algorithms are developed through a process whereby data is fed into the algorithm and the computer has to ‘learn’ what to look for. Unlike the training datasets fed into supervised algorithms, the data fed into unsupervised algorithms does not necessarily include the ‘right answers.’ Unsupervised algorithms are adept at finding clusters of relationships between observations in the data, but may identify erroneous relationships because they are not instructed what to look for.


Machine Learning, Deep Learning, and Natural Language Processing

Machine learning is the process by which computers are trained to ‘learn’ by exposing them to data. Machine learning is a subset of AI, and deep learning is a further subset of machine learning. Deep learning is the process by which algorithms can learn to identify hierarchies within data that allow for truly complex understandings of data. Natural language processing (NLP) refers to the subfield of machine learning designed to allow computers to examine, extract, and interpret data that is structured within a language.

Augmented Intelligence

Augmented Intelligence is a form of AI that enhances human capabilities rather than replacing physicians and healthcare providers. Augmented Intelligence has been embraced as a concept by physician organizations to underscore that emerging AI systems are designed to aid humans in clinical decision-making, implementation, and administration to scale healthcare. In a 2019 white paper, Intel framed augmented intelligence as the AI tools that perform specific tasks and are designed to support users, rather than replacing human experts.

Stages of AI Development 

Existing and potential AI applications vary in their level of sophistication, ranging from simple augmentation of common tasks to full automation of systems and processes. Experts have begun categorizing these stages of AI development. Among them, venture capitalist and author Kai-Fu Lee has characterized four “waves” of AI applications:


Wave 1 Internet AI
Wave 2 Business AI
Wave 3 Perception AI
Wave 4 Autonomous AI


Figure 1. “The Four Waves of AI”

Adapted from Kai-Fu Lee (2018)

According to Lee, the first wave of AI applications uses data generated on the Internet to better understand  the habits, interests, and desires of an individual or population. The second wave of AI applications uses algorithms to inform and improve decision making. Clinical researchers, for example, can construct treatment plans by using algorithms “to digest enormous quantities of data on patient diagnoses, genomic profiles, resultant therapies, and subsequent health outcomes.” The third wave of AI applications relates to the proliferation of sensors and devices that collect data about the physical world such as smart watches and virtual assistants. The fourth wave of AI applications integrates all previous waves and gives machines the ability to make decisions without human intervention. This includes technologies such as automated vehicles.