Question answering systems in biology and medicine--the time is now.

摘要

On February 16th, 2011, it could be argued that the world quietly changed as a milestone in human history was reached. A question answering (QA) system, able to deconstruct a natural language question into information retrieval and analysis tasks, implemented across 2880 CPUs and embodied as an IBM-engineered system named Watson, handily defeated the top two human champions on a game show called Jeopardy. Similar to the achievement of IBM's Deep Blue in beating chess champion Gary Kasparov in 1997, it was not a surprise to technology enthusiasts that such a feat was possible, but it publicized the progress that has been made and the capabilities on information retrieval that are within reach. Thus far, the most salient examples of QA system implementations, as well as publications, are outside the biomedical domain, yet I would argue the sheer number of studied entities, heterogeneous nature of the data, exponential growth of information and emphasis on generation of new knowledge makes biomedicine the field in most need of good QA systems. Anecdotally, I was giving a talk to a couple hundred biomedical scientists and clinicians a week after the Watson challenge and only a couple dozen were aware of it when I asked. There seems to be a gap of awareness between the eventual beneficiaries of advanced QA systems and the developers in terms of recognizing its potential. This, unfortunately, might lead to marginalization of biomedical QA research in terms of publication and funding venues. We should take the Watson milestone as an opportunity to consider the ways biomedical research—including bioinformatics—can benefit from QA systems and some of the possible non-technical hindrances to progress.