BLOG – Generative Pre-trained Transformers and real world research: on the band wagon with ChatGPT 3

Right now, we are all riding on the Gartner Hype Cycle with regards to ‘AI bots’ and generative pre-trained transformers, based on large language models, such as OpenAI’s ChatGPT 3, or Google’s Bard, based on its LaMDA technology. Despite the excitement, or even hysteria around developments in generative AI (artificial intelligence, but perhaps more EI, ‘ersatz intelligence’), it does not take much imagination to extrapolate a potential role or indeed roles for this technology in health research.

So, for this blog entry, our guest is ChatGPT 3 itself, and I asked it a simple request, ‘please write me a 500-word blog entry on ChatGPT 3’s potential role in health data research’, and below is the response. It’s clear, articulate and actually balanced in terms of opportunities and risks. All generated in about a minute. There has been no editing, this is the unexpurgated work of ChatGPT 3:

As a language model, ChatGPT is capable of processing vast amounts of text data and generating responses that are often indistinguishable from those of a human. However, ChatGPT's capabilities extend far beyond simple text processing. One area where ChatGPT has the potential to make a significant impact is in the field of health research.

Traditionally, health research has relied heavily on human experts to analyze and interpret data. However, with the vast amount of medical literature available, it can be difficult for any one person to keep up with all the latest research. ChatGPT can help address this challenge by processing large volumes of data, synthesizing information from different sources, and providing insights and recommendations to researchers.

One area where ChatGPT has already shown promise is in the field of drug discovery. ChatGPT can help researchers identify potential drug targets by analyzing large datasets of molecular structures and identifying potential interactions between different molecules. Additionally, ChatGPT can help predict the efficacy and potential side effects of new drugs, which can help researchers make more informed decisions about which compounds to pursue.

Another area where ChatGPT can be useful is in the analysis of electronic health records (EHRs). EHRs contain a wealth of information about patients' medical histories, including their diagnoses, treatments, and outcomes. However, analyzing this data can be challenging, as EHRs often contain unstructured data that is difficult to process using traditional methods. ChatGPT can help by processing natural language text and extracting relevant information from EHRs, which can help researchers identify patterns and trends in patient outcomes.

ChatGPT can also be useful in the development of personalized medicine. Personalized medicine involves tailoring treatments to individual patients based on their genetic makeup, medical history, and other factors. However, identifying the right treatment for a particular patient can be challenging, as it requires analyzing a large amount of data. ChatGPT can help by processing this data and identifying the most promising treatment options based on a patient's specific characteristics.

In addition to these applications, ChatGPT has the potential to help researchers in many other ways. For example, ChatGPT can help automate literature reviews, identify research gaps, and generate hypotheses for further study. ChatGPT can also be used to develop predictive models that can help researchers anticipate the outcomes of clinical trials and other studies.

Despite its potential, there are also some challenges associated with using ChatGPT in health research. For example, ChatGPT's recommendations are only as good as the data it is trained on. If the training data is biased or incomplete, then ChatGPT's recommendations may be inaccurate or incomplete as well. Additionally, ChatGPT may not always provide the most transparent or interpretable results, which can make it difficult for researchers to understand how its recommendations were generated.

In conclusion, ChatGPT has the potential to revolutionize health research by processing large volumes of data, synthesizing information from different sources, and providing insights and recommendations to researchers. While there are still some challenges associated with using ChatGPT in health research, the potential benefits are significant. As ChatGPT continues to develop and improve, it is likely to play an increasingly important role in the future of health research.

So, there we have it. ChatGPT 3’s view of how it could revolutionise health research, and indeed a definite future role. Already, researchers are utilising this generative AI (or EI) bot to explore opportunities, constrained only by the capabilities of the current bot and imagination. Disruption is already happening due to the launch of the ChatGPT series, and this will only expand as we see the advent of the media-termed, ‘AI war’ and competition for such generative AI products and services deepen.

It will be fascinating to observe, but also participate in the incorporation of this technology into research, from feasibility through to studies, perhaps on more modest aspects to begin with, but only time and application will tell.

For now, a human reviewed and top and tailed the output of ChatGPT 3, but certainly for this blog, didn’t edit. It’s going to be an interesting next stage in our collective journey on using real world data for health research with AI bots around.


On behalf of EHDEN, Nigel Hughes

Scientific Director


Janssen, & EHDEN Project Lead

And ChatGPT 3!