Optimal Human-Computer Problem Solving (HCPS) for medical science

We have been exploring the use of computers to solve hard problems in medicine for several years as part of our Research Analysis project (www.researchanalysis.com).  Research Analysis was born through our need for a way to keep track of thousands of scientific claims that we extracted during the review of over a thousand papers in the cardiovascular field. During our literature review there would be moments of insight that generated new hypotheses. These insights were generated by the connection of concepts in the current article with other concepts stored in our long term memory. When documenting these ideas, we would search back through the earlier literature to confirm and cite the supporting concepts. Two problems came up again and again:

  1. It could take hours or even days of elapsed time to pin point the specific concept in our library of earlier articles.
  2. When we found the concept in the earlier article, it was often not quite as we remembered. We had linked the concept in the current article by analogy to the earlier article, but the analogy did not fit the facts accurately when we revisited them.


The goal of Research Analysis is to avoid these problems through the use of a standardised language for capturing claims, references to specific supporting sentences from within articles and computer tools for capturing and recalling claims. Our ongoing work on Research Analysis lead us to begin thinking and reading about a higher level question: What is the optimal way for humans and computers to work together to solve hard problems? We are of course not the first to ponder this question, but we were surprised to find how little formal research has been conducted on the question. Researching this questions and applying the findings to medical research problems has become an important focus for us.

Chess is a place where humans and computers have been working together for some time. An article by Garry Kasparov discusses the free-style chess competitions run in 2005, where the competitors could compete as teams with other players and/or computers (Kasparov 2010). The surprising result was that the winner was “not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.”. We thought that Garry’s basic formulation of the variables was a good thinking tool.

Garry’s article proposes the following with some minor changes to the terms:

weak human + computer + strong process > strong human + computer + weak process > strong computer > strong human

The above is not thorough or formal and would be specific to the problem type, Chess in this case, but we feel that it provides a good overview and example of our current view on how hard problems in medicine should be approached. Our very high level view is:

optimal problem solving = human + computer + optimal process

There will of course be problem types where optimal problem solving will be achieved by machine only eg. complex arithmetic. There will be problems where optimal problem solving will be achieved by a human only eg. interpreting human emotions in a physical environment. However, we feel that most hard problems in the sciences would benefit from the combined efforts of humans and computers, making the identification of the strongest process critical to solving hard problems.

Today in medical science, computers play an important role in research. However, they tend to play niche number crunching roles like statistical and genetic analysis. Many medical science laboratories confine computers to the control of instruments and a little statistical analysis at the end of the experimental process. They are rarely involved in the hypothesis generation and complex problem solving phases of the research process and even less often in a systematic fashion. This could be represented as: strong human + little or no computer + little or no process. On the other hand, the field of Artificial Intelligence (AI) almost always seeks to develop software systems that are able to solve problems without human involvement. An impressive example of such an AI system is the Robot Scientist (Sparkes 2010) that fully automates the research process including hypothesis generation for a basic biological application. The AI programs in medical science could be represented as: little or no human + strong machine + little or no process (please note that when we use “process” in the formulas, we are specifically referring to the process for human and computer collaboration and not the scientific process or other processes).

While we have named our project Optimal Human-Computer Problem Solving, others in the field use the term machine in place of computer. The two terms have an overlapping definition and we are comfortable with both, but we have chosen to use the term computer because it is more closely related to software and we believe that software will be central to human-computer problem solving. However, the analogy of the machine is a powerful one. The integration of human and machine in the production lines of the early 20th century lead to a dramatic increase in human productivity and wealth. The Ford production line was the proverbial example, where Henry Ford claimed that any man off the street could be productive on the line with less than a couple of days training (Ford 1922). This is a great example of: human + machine + strong process. There were no fancy robots in the line, most of the steps in the production line involved basic tools, cutting and stamping machinery. But the organisation of the machinery and humans into a very focused, efficient and consistent process is what released the productivity boost. Developing equivalently powerful processes for the integration of humans and computers for solving hard problems in medical science is our goal.

Our continuing research and application development will focus on the following areas:

  1. Explore the strengths and weaknesses of both humans and computers: Understanding these will highlight the best opportunities for humans and computers to collaborate.
  2. Algorithmic or process driven approaches to problem solving: Understanding the few examples of algorithmic approaches to problem solving may accelerate the development of strong processes for human-computer collaboration.
  3. Knowledge capture, management and analysis: We will continue our work with Research Analysis as we feel that efficient knowledge capture and manipulation will be critical to optimal problem solving.


We haven’t cited the work that has inspired us to date, instead we will begin publishing brief articles on our Research Analysis knowledge base page that discuss key points we found interesting. We also hope to continue releasing tools we develop through Research Analysis and future platforms.


  1. Kasparov, Garry. “The Chess Master and the Computer” The New York Review of Books 11 FEBRUARY 2010. Web. 10 July 2016. (http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/)
  2. Sparkes, Andrew, et al. “Towards Robot Scientists for autonomous scientific discovery.” Automated Experimentation 2.1 (2010): 1.
  3. Ford, Henry, and Samuel Crowther. “My Life and Work. Garden City, New York, USA.” (1922).