Evaluating AI in Healthcare: The Role of Knowledge Graphs

In recent years, machine learning has made waves in many fields, especially in healthcare. With the rise of large language models (LLMs), healthcare professionals have started to look at these tools as potential game-changers in how we approach medical tasks. Imagine having a computer that can quickly analyze piles of medical information, similar to a doctor but way faster—this is what LLMs are doing.

However, while LLMs show promise, they are not perfect. In the medical field, the stakes are high, and we need to ensure that these tools make accurate decisions every time. When lives are at risk, we can’t afford to gamble. Many experts are now questioning whether traditional testing methods, like multiple-choice questions, are sufficient to assess these advanced models.

To tackle this issue, researchers have developed new methods to evaluate how well LLMs can understand Medical Concepts and relationships. Instead of asking a model to answer quiz-like questions, they are interested in how these models connect various medical ideas to mimic human reasoning. This is where Knowledge Graphs come into play—a way to visualize and understand the connections between medical concepts.

#What Are Knowledge Graphs?

Knowledge graphs are like maps for information. They show how different concepts relate to each other using nodes (the concepts) and edges (the connections). Think of it as a web of knowledge where each piece of information is connected. In healthcare, these graphs can illustrate how symptoms relate to diseases or how one medication may influence another.

By using knowledge graphs, researchers can see whether LLMs truly “understand” medicine rather than just relying on memorized facts. It is a bit like trying to figure out if someone is really a chef or just a good cook because they have a cookbook memorized.

#The Aim of the Research

The key goal is to make LLMs more transparent in their reasoning processes. We want to know how these models reach their conclusions. Are they using proper medical knowledge? Or are they just guessing based on patterns they have seen in the data? To answer these questions, scientists took three different LLMs—GPT-4, Llama3-70b, and PalmyraMed-70b—and put them to the test.

They created knowledge graphs from various medical concepts and had medical students review the graphs for their accuracy and comprehensiveness. The idea is that by looking at the generated graphs, they could understand how these models think about health-related topics.

#Analyzing the Models

The researchers generated a total of 60 graphs from 20 different medical concepts. After generating these graphs, the next step was to evaluate them. Medical students reviewed the graphs to see how accurate and complete they were. They looked for two main things: whether the graphs contained correct medical information and whether they included all important related concepts.

Interestingly, the results were mixed. For instance, GPT-4 delivered the best overall performance in the human review but struggled when compared to established biomedical databases. On the flip side, PalmyraMed, which is designed specifically for medical tasks, did better in comparison to established benchmarks but was found lacking in human reviews.

This revealed an oddity: specialty models weren’t necessarily the best at making connections when human reviewers looked closely at their outputs.

#How the Testing Was Done

The research involved two main steps: expanding nodes and refining edges. To expand the nodes, researchers asked each model to identify medical concepts that either lead to or are caused by a specific medical condition. Picture it as a game of “What comes next?” where you’re trying to figure out all the different paths a particular topic might take.

Once they identified the nodes, they refined the connections between them. The researchers would ask the models if a connection existed between two concepts, ensuring that all plausible relationships were included. It’s like connecting the dots to see the whole picture instead of just a few scattered points.

#The Different Models

The three models used—GPT-4, Llama3-70b, and PalmyraMed-70b—each brought something unique to the table. GPT-4, a generalist model, excelled in connecting broad concepts, showing a varied understanding of medical information. Llama3-70b performed well but didn’t quite hit the marks set by GPT-4. Meanwhile, PalmyraMed was designed for medical applications but seemed to struggle when it came to making those complex connections that require a deeper understanding of causality.

#What the Results Showed

After conducting the tests, it became apparent that there were different strengths and weaknesses among the models. GPT-4 showcased a strong ability to distinguish between direct and indirect Causal Relationships—an essential skill for medical reasoning. It was able to say, “This factor influences that condition,” while other models sometimes muddled the line between cause and correlation.

Interestingly, reviewers noted that PalmyraMed, while being factually accurate, often had difficulty recognizing whether one factor directly caused another or if it was simply related. This could be likened to mistaking someone’s “big day” for their “big success” without realizing they might be completely unrelated.

#The Role of Human Review

Having medical students evaluate the generated graphs was crucial. It offered insights into whether the models could deliver outputs that make sense to people trained in medicine. The students were tasked with rating the graphs for accuracy and how well they covered the topic.

Their feedback revealed that while all models performed well, there were still significant gaps in comprehensiveness. It was clear that even advanced models need guidance and couldn't replace human experts.

#Precision and Recall in Comparison

In addition to human reviews, the researchers compared the models' graphs against a trusted biomedical knowledge graph known as BIOS. This comparison assessed two key metrics: precision and recall. Precision measures how many of the generated connections are accurate, while recall measures how many of the expected connections were identified.

Surprisingly, PalmyraMed, despite the negative feedback in human evaluations, excelled in recall, indicating that it may have captured a broader range of connections. GPT-4, on the other hand, showed lower recall, suggesting it missed several critical relationships.

#Complexity in Generated Graphs

The complexity of the generated graphs varied significantly among the models. GPT-4 produced graphs rich in detail and connections, offering a wide-ranging view of medical concepts. PalmyraMed, in contrast, tended to create more conservative graphs with fewer connections, potentially leading to less comprehensive outputs.

The density of the graphs—how tightly packed the information is—also showed a clear pattern. Models that produced richer data often had lower density scores, meaning they included a vast amount of information without overwhelming the viewer with connections.

#Causality and Connections

As the review process continued, the distinction between direct and indirect causal relationships became more evident. GPT-4 shone brightly in this area, with several reviewers praising its ability to identify these nuances. In contrast, PalmyraMed often blurred these lines, leading to some confusion—similar to thinking every cat video online is an indicator that your cat needs more attention when, in reality, it has everything it wants right next to it.

#Conclusion: What Can We Learn?

The research highlights that while LLMs are promising tools for healthcare, they are not without their challenges. It’s clear that human expertise remains irreplaceable and that even the most advanced models require careful monitoring and evaluation.

Moving forward, there’s a lot of potential for these models to improve. Future research could focus on developing better ways to train LLMs to enhance their understanding of medical concepts, particularly in causal reasoning. By doing this, we could potentially have machines that not only know medical facts but also understand how those facts interact—becoming even more helpful in healthcare settings.

The balance between being a tech-savvy assistant and an actual human expert is delicate. But with continued exploration and innovation, LLMs could become reliable partners for healthcare professionals, enhancing patient safety and improving outcomes without accidentally recommending a “magic potion” for a cold.

In the end, the pursuit of integrating AI with healthcare is akin to trying to bake the perfect cake: a mix of the right ingredients, careful measurements, and knowing when to pull it out of the oven before it burns. With more research, we can make sure this cake is delicious and safe for everyone to enjoy!