AI for Life Sciences: Is Healthcare Too Human to Mechanize?

Estimated reading time: 4 minutes

Doctor wearing a white lab coat and a stethoscope around their neck holding a blue heart graphic holding one finger in the air.

Physician shortage is a growing concern in many countries around the world, due to a growing demand for physicians that outmatches the supply. The World Health Organization (WHO) estimates that there is a global shortage of 4.3 million physicians, nurses, and other healthcare professionals. This is now more compounded by the current world outbreak of the covid 19 pandemic where we as humans must learn to quickly adapt to new emerging artificial intelligence as healthcare professionals can’t continue working 24 hours a day without a break.

The interest in artificial intelligence research seems to present a solution to this problem. If there is a human resources crisis, then we should welcome the algorithms that can take up some of the tasks in diagnostics, assist humans with the decision-making process, automate the tedious administration and embrace what I refer to as “Big Data”. In short, let the software do the boring, but necessary, repetitive stuff and leave the humans to focus on something more complex that only healthcare professionals can do, like taking care of patients and answering critical questions instantly. There are many vertical sectors where artificial intelligence is starting to take on roles that would once have been performed exclusively by humans.

The ethics compliance debate about how much we should allow artificial intelligence to control this world has clear resonance in an industry which has our health and well-being at its heart. There are those that argue that it is neither desirable nor indeed possible to remove the human element from the provision of care and the development of cures. Robots can make cars, and software can drive them for us. But is health and patient care just too human to mechanize? 

Machines spot correlations and anomalies better than people, and this frees people to do what they do best - work with other people. At Alphanumeric we have successfully implemented the Alphanumeric Virtual Assistant (chatbot) on more than 20 of our life sciences client's websites. The chatbot has given patients access to information 24x7x365, which has released agents of performing repetitive activities and streamlined answers to patients' and HCPs' frequently asked questions (FAQs). You can review the case study results here.

The next phase of AI adoption in the life science sectors will probably involve deep learning. This is a subset of machine learning that uses neural networks in tasks that range from image, voice recognition to translation. This kind of artificial intelligence will get better and more accurate as datasets grow. The bigger the dataset, the better the outcome, the greater the utility.

There are some life science organizations that are investing more in this artificial intelligence than others. Some are perhaps a little on the conservative side. The ones that are investing are already noting advantages over old, manual processes. Experts are making discoveries faster if AI presents them with a handful of possible solutions out of millions of options. What needs to change is the acceptance of what can be done. But you have to remember that it’s all about the data. Without the data, the best artificial intelligence models won’t achieve anything. That is one of our main focus areas at Alphanumeric and we are experts in delivering proven intelligent solutions to the top major pharma organizations in the world.

Artificial intelligence is not the ultimate solution for all of the challenges of the patient and healthcare industry, but it is almost certain to become advantageous in helping a hard-pressed workforce provide patient care to a growing and ageing world population. At Alphanumeric we can certainly help you navigate through your challenges and partner with you to find the most effective intelligent patient care AI solutions that meet your organization's needs. For a free consultation and demo customized to your specific use case, please contact us now! 

Glossary of AI Terms 

With digital innovation comes new terminology. Here we explain some of the common terms and acronyms that crop up when artificial intelligence is discussed in a pharma context.


Algorithm - Behind any AI-driven solution is an algorithm. An algorithm is a set of programming commands, or software-based rules, that can be applied to non-intelligent compute power to try and solve problems.

Anomaly detection - AI can be used to extract unexpected elements from a mass of information. Useful, for example, when reviewing large amounts of data following a clinical trial.

Artificial intelligence (AI) - A computer’s ability to take decisions and perform roles that attempt to approximate human intelligence.

Big Data - A term meaning vast volumes of heterogeneous data that comes in a variety of formats and so cannot be analyzed by using just one approach.

Big Tech - An umbrella term referring to large technology giants, like Google, Microsoft, Facebook, Tencent and Amazon. Big Tech is taking an increased interest in life sciences, pharma and healthcare solutions. Pharmaceutical companies may find themselves competing for available human resources not only with their traditional rivals, but also with Big Tech and other AI-driven start-ups.

Cluster analysis - A kind of unsupervised learning that looks for hidden patterns in datasets.

Data cleansing - The process of improving data quality by removing errors before an ML or AI project gets started.

Data-driven healthcare - A term for an approach to healthcare that makes use of Big Data analytics in the search for a competitive advantage. Rising costs combined with a chronic shortage of skilled professionals are forcing the healthcare industry to turn to data science in order to find innovation solutions. 

Machine Learning - the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

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