The Role of Artificial Intelligence and Machine Learning in Clinical Data Management

I guess everyone remember that chapter we all learned in history books while in our high-school class approximately in 8th, not so sure but do know the name- it went like industrial revolution! Yeah, that one about steam engine and machinery mass production. The idea I’m pointing at is that it was not the only industrial revolution we humans have witnessed or we may say discovered. From steam engine to the age of science and mass production to the digital revolution and yet again to the fourth one, on-going and because of which everyone is thrilled both in wonder and fright- Artificial intelligence and Machine learning! 

As a tool with immense power, one can predict that it will invade and impact every industry out there. And yes, clinical data management is no exception! So, the question will be ‘how?’ Firstly, clinical data management system is responsible for managing the massive amounts of data collected during clinical trials. This is where it correlates with AI. Then again ‘how?’ simple answer is- the more data a system has, the better the AI will function. When it comes to machine learning, the present of clinical trials is reactive but in near future when machine learning will be applied it may become proactive. Let us see more roles and impacts that will be brought by AI-Machine learning.

Data Capture and Clean Data: - 

Electronic data capture aka EDC are the standard systems for data entry at present. When we look back on data entry capabilities of the last twenty years, EDC reduced processing time and costs by allowing for the automation of processes, storage, and electronic data display. With artificial intelligence and machine learning, research teams can complete the task of data reviewing and obtaining clean data more quickly with the difference of more than 3000 hours that it takes to do manually. Amazed? Yeah, you should be!

Clinical Data Management: -

As clinical studies continue to become more complex, it is important that the data generated is used in the optimal way. AI and machine learning developers are siting this and are finding a way to use the data points that are collected not only in the short run but also in future trials. Machine learning is leading researchers into the future of data optimization, or getting more from the information collected from the patients. The points that are ignored in a trial but have potency to be used in near future are highlighted. Thus, as a result less data will be go in waste and maximum output from the clinical researchers’ efforts will be achieved. 

Then what is further once the data is cleaned and managed? Yes, it is ‘data analysis’.

Clinical Data Analysis: -

There are multiple machine learning techniques that are used in clinical data analysis. The artificial intelligence- based analysis of clinical data can improve the efficiency. Machine learning and other subsets of artificial intelligence also help to make predictive analysis 


possible. It is based on machine learning algorithms that are able to learn many different patterns of normal behaviour very accurately, and provide correlations between anomalies in a way that is nearly impossible for an analyst to perform (correlation between millions of time series in some cases).

This summarizes overall changes that will be brought by AI and machine learning in big threes of clinical data management. But what should be the preparations one can make to adopt with the change? Let us see-

  1. Control the variable you can control.

  2. Teach and reinforce CDISC standards. (No standard is changing only the technology is evolving.)

  3. Have clear SOPs and Work Instructions.

  4. Demonstrable upper management enthusiasm. (Let the talented employees learn and adapt to change.)

So, owing the roles AI and machine learning plays in clinical data management it will be challenging to make predictions in a complex field like this one. However, given the recent major developments in healthcare, it is one of a possibility that machine learning will have ever-increasing application in clinical trials.

Data management in clinical trials is a good example of automation of tasks that are manually done, with the help of machine learning algorithms. But the data it contains is vast, rising the cost very high.

Though there are plenty of advantages, but to make the most of AI and machine learning and apply it in real world, there are many challenges too. The challenges that are yet to overcome are privacy protection, model interpretability, ethics issues, etc. these challenges are prominent in healthcare and clinical trials because the protected health information (PHI) should be handled with great security and the CIA triad that is confidentiality, integrity and authenticity should be intact throughout the process.

Let us see, what the future brings!

References: -


  1. https://www.syneoshealth.com/insights-hub/ai-and-machine-learning%E2%80%99s-role-clinical-data-management

  1. https://unicsoft.com/blog/the-role-of-ai-in-clinical-data- management/

  1. https://www.clinion.com/insight/clinical-trial-data-managers-and-aiml/

  1. https://www.sciencedirect.com/science/article/pii/S235291481730 1247 

  1. https://www.aniusoftware.com/insights/clinical-data-management-ai

  1. https://www.tcs.com/insights/blogs/next-gen-technologies-clinical-data-management#:~:text=are%20chain%20events.-,Artificial%20intelligence,enhance%20subject%20identification%20and%20enrollment.



Student Name: Vaishnavi S. Patil

Student ID: CSRPL_STD_IND_HYD_ONL/CLS_092/052023

Qualification: M. Pharmacy

e-Mail ID: patilvaishnavi4049@gmail.com

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