Cognitive Computing and its Application on Medicine

Written by: NDS Cognitive Labs

April 2, 2019

From its origins to our days, computing has gone through great technological developments, which in turn have made computers cheaper, smaller, better designed, and with greater capabilities, just to mention some characteristics. Nevertheless, to understand what cognitive computing is, we have to find the differences on the stages of computer sciences.

In a broad sense, we could say that there’s have only been two stages in computing, determined by the following characteristics:

  • From 1951 to 1958 – The first era characterized by counting machines. This were used for census, inventory control, or accounting, just to mention a few uses.
  • 1958 to today – In 1958 scientists laid down the basis for our current computer technologies. On this stage, computers are programmed to do a specific task. This base is found on our computers, tablets and smartphones. In summary, every it is every technology that is programmable.


Technology and consultancy Company, IBM, starts a project called Watson in 2000, with the goal of breaking the current computing paradigms, as a result the launched their first version of Watson in 2011 and starting the new cognitive era (Ruiz, 2016).

Unlike the previous computer technologies, Watson is not programmed, it is trained. It learns based on experience and the resources given by the person who is training it. Watson improves through time, giving more accurate answers and preparing for unexpected events (IBM, 2014).

Watson understands human language, voice or text, potentiates human capacity, minimizes the cost of not knowing, and understands seven languages (Arab, French, German, Japanese, Italian, Portuguese and Spanish), furthermore, it can give advices, teach and clear doubts (Ruiz, 2016).

It is important to bring attention to two important Watson’s characteristics:

  1. It’s creation is bases on several technologies, being one of them ARTIFICIAL INTELLIGENCE (IBM, Building Watson – A Brief Overview of the DeepQA, 2010).
  2. Due to the fact that you train it, if it is taught badly, it will learn badly.


Along with an “expert guide”, Watson collects relevant information called “knowledge hub”. Then, it depurates collected data, until it comes with the most important and necessary information. Then it processes this information so it can work in an efficient way. Finally, Watson learns through training, where it has a round of questions and answers with its trainer.

Once training is finished, Watson learns constantly through use and analysis. It also keeps up to date with new information, and can even be retaught by its trainer to make Watson more accurate (Watson, 2014).


Medical information (articles, essays, conferences etc.) duplicates every three years. Due to this growth rate, medical industry cannot keep up with all the new information. For example, for a doctor it would be impossible to know all the discoveries made on his field of expertise, between his daily routine and lack of resources.

Furthermore, it is necessary in modern medicine to have subject experts, knowing with certainty the causes of a disease and give the right diagnosis. All of this in a quick, individual and understandable way (IBM, Replay: IBM Watson Group Launch Event in New York, 2014). Nevertheless, the current ways of obtaining a patient’s information come from, clinical archives, investigations, clinical tests or personal devices that collect information. These pools of information are often difficult to gather and interpret as a whole.

Watson is based in an ecosystem structure (systems that have Watson as center) in order to work efficiently, combining great amounts of information coming form, doctors, patients, pharmaceutical companies, openly and safely, in a single platform, combining traditional analysis with cognitive analytics (Academy, 2015).

As patient’s information is loaded into Watson, the system returns it in a dynamic and more efficient way. This information combines the patient’s conditions, with similar cases. Using analytics tools, Watson makes the best treatment, and medicine recommendations.

Patient’s data is private and anonymous. The only information sent to Watson is of medical use, without personal information. Also, with the increase of information and use of Watson, specialists, can make greater developments, doctors can treat patients better and pharmaceutical companies can know the efficiency of their products (Academy, 2015).


  • We get the best practices.
  • There is more resources for the patient’s care.
  • Information growth and specialization.
  • Makes clinic evidence more democratic (IBM, Replay: IBM Watson Group Launch Event in New York, 2014).
  • More agility and efficiency to obtain an adequate diagnosis.
  • Prevents medical malpractice due to resource scarcity or time.
  • It can save more lives.


The new generation and our ideas will be responsible to expand this technology (Ruiz, 2016). It will allow to explore, imagine and use our imagination to innovate in other areas and improve our lifestyle. It will also allow people and technology to work as one. Furthermore, IBM is beginning to develop a new hardware for this technology (IBM, Replay: IBM Watson Group Launch Event in New York, 2014).

In what other areas do you think you could use this technology? How do you imagine your life and work using cognitive computing? Is this technology useful? Send your comments through our social media or in the comment box below. We are eager to hear from you and continue the discussion of these technologies.


  • Academy, I. T. (20 de Mayo de 2015). How It Works: IBM Watson Health. Obtenido de
  • IBM. (13 de Diciembre de 2010). Building Watson – A Brief Overview od the DeepQA. Obtenido de
  • IBM. (10 de Enero de 2014). Replay: IBM Watson Group Launch Event in New York. Obtenido de
  • Ruiz, D. (2 de Julio de 2016). CPMX7 Principal – David Ruíz y Frida. Obtenido de
  • Watson, I. (07 de Octubre de 2014). IBM Watson: How it Works. Obtenido de

NDS Cognitive Labs rapidly and affordably develops and integrates sophisticated technologies, leading the implementation of conversational AI, cloud, and digital transformation solutions in Latin America. Co-located in Mexico and the United States, NDS delivers essential innovation and a collaborative team experience through three flexible development models including turn-key services, Outstaffing, and Global In-House Centers. Drawing from our proprietary platform of 20,000 highly skilled tech technology professionals trained in U.S. and global standards, we work with the top global technology providers including IBM, Amazon, Google, Microsoft, MongoDB and more.