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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 QAsystems 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. On the surface, Watson’s performance would seem more a victory for trivia retrieval than scientific research. The most pressing scientific questions are not those that are limited by factual retrieval. However, the scientific endeavor is predicated upon both observation and inquiry. As we gather observations, we are naturally curious if they are consistent with other observations, if what we observed has been reported before, and if what we believe may be the implications of our observations have been explored by others. This requires us to search the peer-reviewed literature which is not only large and growing exponentially in terms of publications (MEDLINE is currently growing ∼5%/year), but the total searchable domain is becoming rapidly larger with an increasing use of supplementary information and availability of full text. Thus, the quest for answers can not only be time consuming, but fraught with difficulties since the average research lexicon is filled with synonyms, acronyms and variations in naming conventions. A priority is placed on thoroughness (i.e. sensitivity/recall) because not being aware of relevant prior art can render a completed project moot. We are accustomed to trying to translate our questions into Boolean-based keyword searches that we hope will yield the best specificity to sensitivity trade-off in PubMed since it is not practical for us to sort through hundreds or even thousands of results when we know most of them are probably not directly relevant. The most difficult part, however, is not locating documents with single facts, but locating and connecting strings of facts that may be in different documents. The potential to engage in factual inference is probably the most powerful and appealing advantage of QAapproaches over traditional information retrieval (IR) techniques. While keyword-based IR is a process researchers are accustomed to despite the inefficiencies, what the Jeopardy experiment showed us is that the technological know-how is here to automate, at least partially, this part of the process. And that, relative to human experts, it can perform well—it is specific enough to get answers correct, sensitive enough to find data hidden in mountains of text and smart enough to know when it does not know the answer. Even though the QA field itself goes back, arguably, to the 1960s, development of QA systems for biomedical applications is a more recent phenomenon that has seen some but not much activity within PubMed (Cao et al., 2011; Olvera-Lobo and GutierrezArtacho, 2010; Overby et al., 2009; Zweigenbaum, 2003), even within bioinformatics journals. Yet, there are many vibrant research programs across the world working on the issue. Much of their progress is being reported in conferences such as TREC, which had a genomic QA track from 2006 to 2007 (Hersh and Voorhees, 2009) and BioCreative (Leitner et al., 2010), which focuses on information retrieval, a foundational technology for QA. Yet, these are not often accessed by (and perhaps not even targeted to) biomedical researchers in general, the largest end user audience for the product. In defense of this apparent gap between producers and consumers, it could be argued that biomedical QA systems are not as ready for prime-time as Watson was. The issue is not one of whether or not outreach has taken place, but how effective it has been or will be. As a commercial endeavor, IBM cannot afford to spend effort advancing QA capability for purely academic reasons, and Jeopardy was their showcase for their product. Sometimes, it is argued the value of an advance is independent of its application, but I would argue

Question answering systems in biology and medicine—the time is now