Transforming Healthcare: The Role of LLMs in Oncology
Large Language Models are reshaping oncology by improving text analysis and research efficiency.
Paul Windisch, Fabio Dennstädt, Christina Schröder, Daniel R. Zwahlen, Robert Förster
― 7 min read
Table of Contents
- What Are Large Language Models?
- Why Do We Need These Models in Medicine?
- The Rise of Transformative Technology
- Chain-of-Thought Prompting: A New Trick
- Text Mining in Oncology
- A New Challenge: Testing LLMs
- How Did They Test the Models?
- Results of the Experiment
- Missed Classifications: A Closer Look
- Cost Comparison: Is It Worth It?
- A Peek at Future Possibilities
- Conclusion: The Road Ahead
- Original Source
- Reference Links
Large Language Models (LLMs) are tools that can understand and generate text. They have made quite a splash in several fields, especially healthcare. These models can sift through heaps of medical documents and extract useful information. Just picture a super-fast librarian who can read every medical paper in the world, and you get the idea.
What Are Large Language Models?
LLMs are computer programs designed to process human language. They learn from tons of text data, which helps them understand how words fit together. These models can help answer questions, summarize texts, and even generate new content. In medicine, they are particularly valuable because they can analyze clinical notes and research papers to glean insights that might take humans much longer to find.
Why Do We Need These Models in Medicine?
In healthcare, information is everything. Doctors need to stay updated with the latest research and patient notes. However, medical literature is dense and complex, often packed with information that can be hard to interpret. This is where LLMs come in handy. They can quickly read through a massive amount of data, helping healthcare professionals make informed decisions.
The Rise of Transformative Technology
Recently, there’s been excitement about a technology called Transformers in the world of LLMs. Think of transformers as a fancy set of gears that help these models work more effectively. They allow the models to recognize patterns in the text and generate responses that seem natural.
AI developers have been trying to make these models bigger and better by giving them more data and increasing their capabilities. It’s a bit like trying to build the biggest and strongest robot. Bigger robots might be able to lift heavier things, but they also need to be smart enough to know how to use that strength correctly.
Chain-of-Thought Prompting: A New Trick
One interesting technique is called chain-of-thought prompting. This is a method where models are encouraged to think out loud, showing their reasoning process step-by-step before arriving at a conclusion. Imagine if your calculator not only gave you the answer to a math problem but also explained how it got there. This approach can help improve the accuracy of these models without having to make them larger.
Recently, OpenAI, a well-known AI company, released a new version of its model that uses this chain-of-thought prompting. This version of the model has shown impressive results in tasks like coding and answering science questions. It’s like they gave the model a little extra brainpower.
Text Mining in Oncology
One specific area where LLMs are making waves is oncology, which is the study of cancer. Text mining in oncology can be complex because it often involves understanding intricate medical terms and various ways of describing cancer trials.
For example, researchers might want to know if a cancer study included patients with localized disease (cancer that hasn't spread) or metastatic disease (cancer that has spread). The information might appear in different formats, such as medical staging systems or vague terms like "advanced" or "extensive." This variability can make it tricky for anyone-whether human or machine-to classify the trials accurately.
A New Challenge: Testing LLMs
Researchers recently set out to test the performance of OpenAI’s latest model against its older sibling, GPT-4o. They wanted to see if the new model could do a better job of predicting whether patients with localized or metastatic disease were included in cancer trials. Instead of using a whole library of studies, they picked 600 cancer trial abstracts from major medical journals.
The idea was to see if the newer model could understand the abstracts better and give accurate information about patient eligibility. This testing process is quite similar to a school test, but instead of pencils and paper, they used advanced AI models and medical research papers.
How Did They Test the Models?
To test the models, the researchers sent specific prompts to them. For GPT-4o, they asked it to classify abstracts based on whether they included patients with localized and metastatic disease. This model performed pretty well, consistently returning the desired response format. For the new model, they fed it both the instructions and the abstract since it didn't support a separate prompt at that time.
They monitored how the two models performed, looking at metrics like accuracy and precision. They wanted to see how often the models correctly identified patient eligibility from the abstracts and what mistakes they made.
Results of the Experiment
The results were quite enlightening. The newer model not only outperformed the older version but also produced better precision when reading the abstracts. In simple terms, it did better at picking out the right details that mattered for classifying the trials.
Specifically, while GPT-4o achieved a solid F1 score (a measure of accuracy), the new model surpassed it significantly when it came to determining whether patients with localized disease were eligible. The numbers reflected that the new model could handle the nuances of the language used in the abstracts more effectively.
Missed Classifications: A Closer Look
However, the testing was not all smooth sailing. The researchers noticed some instances where the new model made mistakes. For example, some abstracts used ambiguous language. Words like "advanced" or "recurrent" could confuse the model, leading to errors in classification. A human reader might understand the full context, but the model had limitations.
During their inspection, the researchers found that many of the mistakes made by the new model came from its inability to assess certain keywords properly. This was similar to when you misread text messages and misinterpret the meaning behind the words. The new model had its fair share of "misunderstandings."
Cost Comparison: Is It Worth It?
Interestingly, the costs involved in using these models were also evaluated. The older GPT-4o was considerably cheaper to run than the new model. In the world of AI, affordability matters. For researchers on a budget, sticking with an older, more cost-effective model may be tempting, even if it delivers slightly less accuracy.
A Peek at Future Possibilities
So, what does this all mean? As LLMs continue to improve, they hold great potential for text mining in oncology and beyond. They could help researchers and clinicians sift through medical information faster and more accurately.
Also, while the new model did better in many respects, there’s still room for improvement. The false positives and issues with ambiguous language show that there’s more work to be done before these models can match or exceed human-level understanding.
Conclusion: The Road Ahead
In short, LLMs are quickly becoming essential tools in the healthcare field, especially in oncology. The ongoing advancements show promise for making text analysis smarter and more efficient. While newer models might command a higher price tag, their enhanced performance suggests they could be worth it for specific tasks.
With further development and fine-tuning, these models could become even more adept at navigating the complexities of medical literature. The journey of AI in medicine is just getting started, and it looks like it will be an exciting ride. Who knows, maybe one day, computers will rival humans in reading and interpreting medical texts-watch out, doctors!
In the meantime, we can only hope these models don’t start writing medical dramas; with all the twists and turns in oncology, that might be a bit of a stretch!
Title: Reasoning Models for Text Mining in Oncology - a Comparison Between o1 Preview and GPT-4o
Abstract: PurposeChain-of-thought prompting is a method to make a Large Language Model (LLM) generate intermediate reasoning steps when solving a complex problem to increase its performance. OpenAIs o1 preview is an LLM that has been trained with reinforcement learning to create such a chain-of-thought internally, prior to giving a response and has been claimed to surpass various benchmarks requiring complex reasoning. The purpose of this study was to evaluate its performance for text mining in oncology. MethodsSix hundred trials from high-impact medical journals were classified depending on whether they allowed for the inclusion of patients with localized and/or metastatic disease. GPT-4o and o1 preview were instructed to do the same classification based on the publications abstracts. ResultsFor predicting whether patients with localized disease were enrolled, GPT-4o and o1 preview achieved F1 scores of 0.80 (0.76 - 0.83) and 0.91 (0.89 - 0.94), respectively. For predicting whether patients with metastatic disease were enrolled, GPT-4o and o1 preview achieved F1 scores of 0.97 (0.95 - 0.98) and 0.99 (0.99 - 1.00), respectively. Conclusiono1 preview outperformed GPT-4o for extracting if people with localized and or metastatic disease were eligible for a trial from its abstract. o1 previewss performance was close to human annotation but could still be improved when dealing with cancer screening and prevention trials as well as by adhering to the desired output format. While research on additional tasks is necessary, it is likely that reasoning models could become the new state of the art for text mining in oncology and various other tasks in medicine.
Authors: Paul Windisch, Fabio Dennstädt, Christina Schröder, Daniel R. Zwahlen, Robert Förster
Last Update: Dec 8, 2024
Language: English
Source URL: https://www.medrxiv.org/content/10.1101/2024.12.06.24318592
Source PDF: https://www.medrxiv.org/content/10.1101/2024.12.06.24318592.full.pdf
Licence: https://creativecommons.org/licenses/by-nc/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
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