Application of AI-Meta-Analysis in Clinical Research Strategy

18 March 2024
Katerina Krinitsyna , Head of Business Development
Application of AI-Meta-Analysis in Clinical Research Strategy

The application of AI-meta-analyses in clinical research strategy is a topic which has received an increasing amount of attention. AI-powered designs promise to revolutionize how clinical research is conducted. They’ve been proven to be apt at coming up with accurate findings while saving much time compared to traditional methods. 

However, using artificial intelligence in trials also raises a series of challenges and considerations that must be addressed. 

This article delves into the multifaceted prospect of integrating AI-meta-analysis into clinical research. 


What is an AI-meta analysis?  

Simply put, an AI-meta-analysis is a meta-analysis study involving AI tools. A meta-analysis itself is a comprehensive and systematic study design. It analyzes an event by combining and comparing results from previous relevant studies, such as randomized control trials (RCTs). Researchers often conduct meta-analyses to reach conclusions on various interventions or treatment modalities.  

Meta-analyses are the gold standard for evidence. However, they take a lot of time and effort to conduct. As a result, they may be vulnerable to human bias.[1] This is precisely where AI comes in.  

AI offers automation tools that can significantly boost efficiency and save time. It mediates an accelerated form of meta-analysis known as rapid meta-analysis (RMA).[2] RMA might prove crucial during clinical crises when time is of the essence.  


How is AI used in clinical trials?  

AI has become established in several clinical research areas. AI tools can contribute to different phases and aspects of trials by: 

  • Analyzing real-world data (RWD) from electronic health records 
  • Extracting and organizing data from scientific publications in the context of meta-analyses
  • Reducing bias  
  • Drafting clinical protocols
  • Enhancing patient recruitment for clinical trials
  • Predicting the potential adverse effects
  • Simplifying and accelerating new drug development processes
  • Examining large datasets from the human genome

AI employs various automation techniques to achieve its results.[3] These include the following: 

  1. Machine learning (ML)
  2. Deep learning (DL)
  3. Natural language processing (NLP)
  4. Optical character recognition (OCR)

Many tools based on these technologies are freely available.[4] 


How does an AI-meta-analysis improve clinical research?  

Recent studies have found that AI may indeed help improve clinical research. In this regard, automated meta-analyses have shown promising initial results.[5] An AI-meta-analysis model has the potential to achieve clinical targets as effectively as healthcare researchers but in a much shorter timeframe.  

A case study from the Journal of Medical Internet Research examined the use of AI-meta-analysis in assessing the ocular toxicity of hydroxychloroquine.[6] Using AI, the researchers found a significant association in less than thirty minutes. The study contrasts this with a traditional meta-analysis, which took several months to come to the same conclusion. 

Thus, AI has the potential to equip researchers with powerful tools that could: 

  • Expedite trials and save time, effort, and money
  • Improve sampling and results
  • Create innovative and effective diagnostic and therapeutic strategies
  • Reduce bias and undesirable outcomes

Is an AI-meta-analysis better than a traditional meta-analysis?  

It is still too early to confidently say that an AI-meta-analysis is better than traditional methods. Despite the capacities of an AI-meta-analysis, manual practice cannot yet be completely replaced. At present, AI in clinical research is only applied under close human supervision.[7] 


Considerations for AI-meta-analysis in clinical research strategy 

As previously mentioned, applying AI-meta-analysis in clinical research strategy necessitates a few considerations.[8] 

First, researchers must ensure that they’re implementing a relevant and cost-effective AI tool and algorithm.  

After choosing the correct AI tool, they should feed it with representative and accurate data. Researchers must also devise models that avoid bias risk. The integration of AI systems must be monitored and updated continuously. 

The application of AI should always follow ethical standards and legislation, which concern numerous issues, such as patient data privacy, data security, and informed consent.  

The results of AI-meta-analyses must be clear and explainable. Healthcare professionals should understand and be able to interpret them. They must be able to apply them to routine clinical practice. Additionally, the results must apply to a broader population.  

The future of research appears to be AI-oriented. Thus, addressing these considerations is vital in improving the efficiency of meta-analyses in clinical research strategies. 


The bottom line 

Meta-analyses are the gold standard for therapeutic evidence. However, without careful efforts to eliminate bias, it can lead to inaccurate conclusions. Using AI tools in a meta-analysis can offer a potential solution to these problems.  

AI-meta-analyses promise to improve clinical research strategies. In addition to increasing efficiency, AI automation tools save time. This may be especially crucial during clinical crises when time is of the essence.  

Using AI in research strategies presents many technical and ethical challenges. It’s a process that should be done only under expert supervision. At Therapyte, we fully understand the potential of AI-driven trials as well as their associated drawbacks. Reach out to us to learn more. 


[1] Automated Meta-Analysis: A Causal Learning Perspective. 

[2] Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.

[3] Artificial intelligence in managing clinical trial design and conduct: Man and machine still on the learning curve?

[4] Towards Automated Meta-Analysis of Clinical Trials: An Overview.

[5] Ibid.

[6] Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine.

[7] Ethics and governance of artificial intelligence for health.

[8] Ibid.