Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)
Year: 2023

Abstract:
Large language models, particularly GPT3, are able to produce high quality summaries of general domain news articles in fewand zero-shot settings. However, it is unclear if such models are similarly capable in more specialized, high-stakes domains such as biomedicine. In this paper, we enlist domain experts (individuals with medical training) to evaluate summaries of biomedical articles generated by GPT-3, given zero supervision. We consider both singleand multi-document settings. In the former, GPT-3 is tasked with generating regular and plain-language summaries of articles describing randomized controlled trials; in the latter, we assess the degree to which GPT-3 is able to synthesize evidence reported across a collection of articles. We design an annotation scheme for evaluating model outputs, with an emphasis on assessing the factual accuracy of generated summaries. We find that while GPT-3 is able to summarize and simplify single biomedical articles faithfully, it struggles to provide accurate aggregations of findings over multiple documents. We release all data and annotations used in this work.1

heading: Future Work
text: This highlights the ability to aggregate across documents as a direction for future work. We release all data and annotations to facilitate such work in the medical space going forward. Limitations
This evaluation focussed on expert manual assessments of model outputs and their factual accuracy. Domain expertise (in medicine) was invaluable for this task, but is also expensive and therefore limited the scale of our evaluation. Consequently, all findings are derived over a modest sample (100s) of triple-annotated instances. Another limitation here is that we have considered only articles describing randomized control trials (RCTs). We focused on such articles because RCTs are the most reliable means of assessing medical interventions, and therefore inform the practice of evidence-based medicine; summarizing such articles is therefore critical to help physicians stay on top of the evidence. Moreover, RCTs provide a natural grounding with respect to factuality, given that all such trials will investigate the relative efficacy of an intervention for a particular condition (i. e. , on a specific population of patients) and with respect to an outcome of interest. That said, this is restrictive by design, and our analysis has therefore excluded large swaths of other types of medical texts. Ethical Considerations
In Appendix D, we note the costs of hiring domain experts for annotation. Large language models (such as GPT3-D3) have been shown capable of generating concise and fluent summaries. But these often contain factual inaccuracies. This poses unique risks in the domain of medicine, where inaccurate summaries of published evidence have the potential to (mis-)inform patient care. This work has attempted to empirically assess the tendency of models to introduce inaccuracies into summaries of medical literature
by enlisting domain experts to identify and characterize omissions and errors in model generated summaries. Understanding such issues is a first step toward designing methods to mitigate them. While we found that GPT3-D3 appears to produce summaries of single biomedical article abstracts that are reasonably factual, relying on such outputs still poses risks, and even in this setting we would caution against trusting model outputs without further verification at present. Moreover, we found that in the multi-document case—i. e. , on the task of synthesizing evidence reported across multiple clinical trials—GPT3-D3 struggles to provide synopses that agree with reference (expert written) summaries. In sum, despite their ability to produce consistently plausible outputs, our view is that summaries of medical literature produced by LLMs should not yet be used to directly inform care given the risks of factual inaccuracies. More research is needed to better characterize the kinds of mistakes such models make, and ultimately to mitigate them