How useful is GPT-4 in Medicine?

Artificial intelligence (AI) applications in medicine have been increasing both vertically and horizontally. Examples of areas where it has been found to be of significant value include medical imaging, medical note-taking, identifying high-risk subjects, and many other areas. But how useful is GPT-4 in medicine more specifically?

To understand the feasibility of large language models (LLMs), like generative pre-trained transformer-4 (GPT-4), used in the context of medicine, a group of researchers sought to test the potential of the technology in the aforementioned area. In this article published in the New England Journal of Medicine, Lee et al. explored various applications of GPT-4 in the field of medicine. While the research did not provide quantitative information on the safety and efficacy of using the technology, this has subsequently encouraged a group of Stanford University experts to try to quantitatively determine the safety and accuracy of medical responses generated by GPT-4.

Large Language Models

LLMs, like GPT-3.5 and GPT-4, grew to over 100 million users in a span of weeks, highlighting user-driven enthusiasm and curiosity. With this significant worldwide number of users, it has also inevitably become used by healthcare professionals too. Despite the potential it holds in transforming the healthcare landscape, it should be dealt with carefully. This is because LLMs have been shown to suffer from problems like bias, lack of consistency, hallucinations, and other alarming challenges. Such concerns should prompt more careful use in the context of medicine since wrong answers provided by these LLMs could risk the lives of human beings.  

Quantitative Approach to Safety and Usability

Stanford University researchers have quantitatively assessed the safety and usability of the answers provided by LLMs in response to clinical questions submitted by Stanford medical professionals during care delivery. The sample consisted of 64 questions that were drawn from a larger pool of about 150 clinical points. An example of the questions used during care is, “In patients at least 18 years old who are prescribed ibuprofen, is there any difference in peak blood glucose after treatment compared to patients prescribed acetaminophen?”.

To assess the produced responses, twelve clinicians from multiple specialties were consulted. Preliminary results reveal that initial answers provided by the LLMs were deemed as generally safe in about 9 out of 10 submitted queries. The remaining 7-9% of responses were considered harmful due to hallucinated citations. The difference between models was slight, with 91% of GPT-3.5 and 93% of GPT-4 answers being deemed as safe, respectively.

Furthermore, the responses provided by the LLMs were in agreement with the known answers to clinical queries in about 1 to 2 in every five answers. In this context, 21% of the GPT-3.5 responses were in agreement with the known answer, while this value was almost doubled (41%) with GPT-4 responses.   

Quantitative Assessment of LLMs Reliability

To evaluate the reliability and reproducibility of the LLMs, the researchers assessed the responses provided by the models when given the same question multiple times over several days. They highlighted that the prompt engineering aspect involved instructing the GPT-4 model with the following: “You are a helpful assistant with medical expertise. You are assisting doctors with their questions” and the GPT-3.5 model with “Act as an AI doctor” to receive and assess the responses they produce in comparison to those provided by consultation reports.

Results revealed that when the LLMs were provided with the same prompt several times across the days, they showed low similarity and high variability in their responses. The reliability of the answers was assessed using Jaccard (measures similarity between two unique sets of words) and cosine (measures similarity between the total length of two vectors)  similarity. The ideal values for the metrics mentioned above are as close as possible to 1. However, the average Jaccard and cosine similarity values were 0.29 and 0.45, respectively, for GPT-4. Closely, GPT-3.5 produced similarity values of 0.27 and 0.36 for the same metrics.

The researchers mentioned that their study is ongoing but concluded that the current results demonstrate that LLMs hold great potential and carry a significant risk at the same time, requiring further measures to refine and assess the outcomes.

Narrativa: Effective, Accurate, and Reproducible Results

Narrativa, a leading company in the field of generative AI, provides the life sciences industry with reliable solutions that empower various stakeholders in the clinical research and regulatory documentation industries to augment their capacities. Narrativa hosts its own refined and fine-tuned LLMs tailored to support the teams of pharmaceutical and biotech companies in bringing their life-saving treatments to the market rapidly without compromising quality through means of expediting the process of generating regulatory documentation. Narrativa’s solutions, through its platform, are designed in a way that requires no prior coding experience and upholds the privacy of the data it handles. It can shorten the duration of document generation from months to just a few days, giving medical writers, statistical programmers, and biostatisticians a significant lead time.

With a suite of automated generative AI and related solutions, such as automated authoring of clinical study reports, patient safety narratives, generation of Tables, Listings, and Figures (TLFs), and an automatic redaction tool, Narrativa understands the importance of producing consistent, reliable, and scientifically accurate documents. This is because when human lives are at stake, there is no room for errors. This is why we continuously remind our potential and current business partners of the risks associated with using unrefined and general-purpose LLMs on the health and privacy of the general population and individuals involved in clinical trials.   

We continue to expand our pipeline of life sciences solutions as current and new business partners approach us. We help their teams become more efficient in their workplaces and support their efforts in utilizing human capital for higher-value tasks. In short, we partner with organizations and businesses to help them accelerate their potential. Our expanding portfolio of clients involves partners like TCS, the Leukemia & Lymphoma Society, and leading pharmaceutical and biotechnology companies.

About Narrativa

Narrativa is an internationally recognized generative AI content company that believes people and artificial intelligence are better together. Through its proprietary content automation platform, teams of all types and sizes are empowered to build and deploy smart composition, business intelligence reporting, and process optimization content solutions for internal and external audiences alike. 

Its tech stack, consisting of data extraction, data analysis, natural language processing (NLP), and natural language generation (NLG) tools, all seamlessly work together to produce content quickly and at scale. In this way, Narrativa supports the growth of businesses across a variety of industries, while also saving them both time and money. Accelerate the potential with Narrativa.

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