Academy of Diagnostics & Laboratory Medicine - Scientific Short

"Lab medicine for 2100", or "Watson come quickly!"

Douglas F. Stickle

Many of you will have seen or heard of the recent appearance of IBM's Watson computer as a contestant on Jeopardy. The first show aired on February 14th, 2011 -- the 65th anniversary of the debut of ENIAC. The performance of Watson on Jeopardy was fantastic. The computational challenge was enormous -- Jeopardy was selected precisely because of its complex linguistic difficulties -- and Watson achieved great success. Watson crushed his human competitors. In fairness to humans, it appeared that Watson had a nearly unbeatable advantage to buzz in first. That aside, Watson had the right answer to almost every question. This was a breathtaking feat, demonstrating his ability to take in a verbal question and output the correct answer in very short time, using on-board data and programmed smarts. The IBM and computer science cheerleading that went along with this special Jeopardy program was, if anything, understated -- this type of interactive system will have enormous impact in many fields. Medicine is certainly one of the areas that would clearly benefit from such systems (1). Indeed, medical decision making is an area of emphasis that is being targeted by IBM (2,3). Ultimately, such systems would not only aid medical care, but direct it, with "sign off" capabilities.

Pathology and laboratory medicine are good examples of prime targets for deployment of automated expert systems. What expert systems exist now, and what will it take to bring Watson's progeny to the fore? Delta checks and reflex testing reflect low-level automated expert systems in widespread use. There are some clinical decision support systems (CDSSs) in laboratory medicine (4) and elsewhere (e.g., pharmacy (5), anesthesiology (6), radiology (7), general medicine (8), others?). However, these are not widely deployed or implemented. Nevertheless, there are many expert algorithms or protocols used routinely, albeit manually, in laboratory diagnosis (e.g., acid-base, endocrine, metabolic disorders). These systems do not typically involve ambiguities – there is but one pathway in following an “if-then-else” algorithm that is highly amenable to automated oversight, even without sophisticated language-interactive capability of a Watson-like system.

Similarly, Watson's Jeopardy encounter involved questions that had only one correct answer (referred to as “factoid" questions (9)), and Watson was highly optimized for Jeopardy with respect to data available to it (9,10). Most likely, Watson could easily be reprogrammed to do well on our board exams, which contain questions highly constrained to have one best answer. Essentially, exam questions all boil down to a simple request: "Recognize the canonical set of case conditions described below to choose the best answer." It is highly likely Watson would excel at that, if his database contained Tietz Textbook.

The difference between where Watson is now and where we would like him to go is that we want him to be able to ask the questions, not just answer them. The future Watson will be able to ask, in appropriate sequence, in real time, questions to efficiently elicit an optimized case treatment scheme. Importantly, he will be able to make rapid, highly informed first decisions about how best to proceed down a differential diagnosis tree of 10,000 possibilities based on initial case data. This is certainly feasible in the long run. In fact, IBM recently announced partnerships with software developers and universities to develop a "physician's assistant," a first step for Watson progeny along these lines (3).

Who would not wish to have the world's collected knowledge and expertise applied to their medical care? It is virtually certain that expert systems will ultimately play a pre-eminent role in the practice of medicine to provide an optimal harmonization of standards of care. Given government's financial interest in medical care, such systems will eventually involve formal government sanction and oversight. Professional organizations including NACB (with its future expert systems LMPGs) will likely play an essential role in development, validation and certification of widely deployed expert systems over the next few decades. One hundred years from now, Watson's descendents, in the form of indefatigable, unerringly pleasant robodocs (who will have spent entire milliseconds in medical school), will provide uniformly phenomenal, world-class expertise in diagnosis and treatment of human medical maladies, such as they then exist. Hasten the day!


  1. Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? J Biomed Inform 2009 Oct;42(5):760-72. Epub 2009 Aug 13. (Review)
  2. (accessed April 2011)
  3. “Dr Watson, We Presume.” Science 2011;331:992.
  4. Main C, Moxham T, Wyatt JC, Kay J, Anderson R, Stein K. Computerized decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems. Health Technol Assess 2010 Oct;14(48):1-227. (Review)
  5. Robertson J, Walkom E, Pearson SA, Hains I, Williamsone M, Newby D. The impact of pharmacy computerised clinical decision support on prescribing, clinical and patient outcomes: a systematic review of the literature. Int J Pharm Pract 2010 Apr;18(2):69-87. (Review)
  6. Hemmerling TM. Automated anesthesia. Curr Opin Anaesthesiol 2009 Dec;22(6):757-63. (Review)
  7. Stivaros SM, Gledson A, Nenadic G, Zeng XJ, Keane J, Jackson A. Decision support systems for clinical radiological practice -- towards the next generation. Br J Radiol 2010 Nov;83(995):904-14. (Review)
  8. Elkin PL, Liebow M, Bauer BA, Chaliki S, Wahner-Roedler D, Bundrick J, Lee M, Brown SH, Froehling D, Bailey K, Famiglietti K, Kim R, Hoffer E, Feldman M, Barnett GO. The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs). Int J Med Inform 2010 Nov;79(11):772-7. Epub 2010 Oct 14. (Review)
  9. “Schooling the Jeopardy! Champ: Far from Elementary.” Science 2011;331:999.
  10. Baker S. Final Jeopardy: Man vs. Machine and the Quest to Know Everything. Houghton Mifflin Harcourt Publishing, New York, 2011. 

Scientific Shorts are brought to you by the

The Academy of Diagnostics & Laboratory Medicine logo

Academy of Diagnostics & Laboratory Medicine Designation

Fellows of the Academy use the designation of FADLM. This designation is equivalent to FACB and FAACC, the previous designations used by fellows of the National Academy of Clinical Biochemistry and AACC Academy. Those groups were rebranded as Academy of Diagnostics & Laboratory Medicine in 2023.