Computer Beats Champion Player at Go – What Does This Mean for Medical Diagnosis?

A computer program has recently beaten one of the top players of the Chinese board game Go.[1] The reason that a computer’s success in Go is so important lies in the nature of Go. Draughts (or Checkers) can be solved completely by pre-specified algorithms. Similarly, chess can be solved by a pre-specified algorithm overlaid on a number of rules. But Go is different – while experienced players are better than novices, they cannot specify an algorithm for success that can be uploaded into a computer. This is because it is not possible to compute all possible combinations of moves in order to select the most propitious. This is for two reasons. First, there are too many possible combinations – much more than there are in chess. Second, experts cannot explicate the knowledge that makes them so. But the computer program can learn by accumulating experience. As it learns, it increases its ability to select moves that increase the probability of success – the neural network gradually recognises the most advantageous moves in response to the pattern of pieces on the board. So, in theory, a computer program could learn which patterns of symptoms, signs, and blood tests are most predictive of which diseases.

Why does the CLAHRC WM Director think this is a long way off? Well, it has nothing to do with the complexity of diagnosis, or intractability of the topic. No, it is a practical problem. For the computer program to become an expert Go player, it required access to hundreds of thousands of games, each with a clear win/lose outcome. In comparison, clinical diagnosis evolves over a long period in different places; the ‘diagnosis’ can be ephemeral (a person’s diagnosis may change as doctors struggle to pin it down); initial diagnosis is often wrong; and a person can have multiple diagnoses. Creating a self-learning program to make diagnoses is unlikely to succeed for the foreseeable future. The logistics of providing sufficient patterns of symptoms and signs over different time-scales, and the lack of clear outcomes, are serious barriers to success. However, a program to suggest possible diagnoses on the basis of current codifiable knowledge is a different matter altogether. It could be built using current rules, e.g. to consider malaria in someone returning from Africa, or giant-cell arteritis in an elderly person with sudden loss of vision.

— Richard Lilford, CLAHRC WM Director


  1. BBC News. Artificial intelligence: Google’s AlphaGo beats Go master Lee Se-dol. 12 March 2016.

One thought on “Computer Beats Champion Player at Go – What Does This Mean for Medical Diagnosis?”

  1. In your references you continue to be kindly supportive of my contention that even in our digital world, paper still has a crucial role.

    Over 30 years ago Tim de Dombal et al demonstrated that when patients arrived at an A & E department suffering from abdominal pain, a mini-computer program gave more accurate probability predictions of the final diagnosis than the average doctor. A crucial reason for their success was the use of a well designed paper pro-forma to collect totally standardised data.

    One of the too often neglected principles of IT is “ALWAYS try to get the paperwork right FIRST; only afterwards lock the result into rigid databases”

    The Wisdam initiative is therefore now focused on the Simple WISDAM set of plain English questions and all allowable answer options. Being on paper it is now, after 3 years work, in it’s 177th version. It is downloadable cost-free everywhere via

    Almost every industry, apart from healthcare, reduces their workload by getting ‘customers’ to do as much data entry as possible. Because Simple WISDAM is designed from the start to work in both ELECTRONIC and PAPER formats, it has a unique potential to set a global standard for the documentation of the basic healthcare information which all of us know about ourselves. And as those without medical training increasingly enter their presenting issues directly onto their own laptops and smart phones, the standardization of such data seems likely to be of increasing importance in making our healthcare industry more efficient.

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