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  • Hongjian Zhou

Newsletter from The Neural Medwork: Issue 12



Welcome to the 12th edition of the Neural Medwork Newsletter. As we explore the building blocks of artificial neural networks, the next concept we want to introduce is the perceptron—a model as fundamental to AI as the neuron is to human cognition. Developed by Frank Rosenblatt in 1957, this simple yet powerful algorithm helps pave the way for understanding more complex neural network structures used in AI today. We then introduce a paper on adapted Large Language Models (LLMs), in which they outperform medical experts in text summarization. Lastly, we showcase the most popular prompt engineering technique - Retrieval Augmented Generation (RAG).


Core Concept: The Perceptron

What is a Perceptron?

At its core, the perceptron mimics a neuron’s basic function: deciding whether to 'fire' based on the inputs it receives. Just as a neuron uses its dendrites to receive signals and, when enough stimulation is achieved (reaching a threshold), it fires an action potential along its axon, a perceptron processes input signals, sums them up, and outputs a decision.

How Does a Perceptron Work?

  • Inputs and Weights: Similar to how neurons receive neurotransmitters, a perceptron receives input features (e.g., clinical measurements) which are each multiplied by a corresponding weight. These weights, akin to the strength of synaptic connections in a neuron, are learned from data and determine the importance of each feature in the decision-making process.

  • Summation and Bias: The weighted inputs are summed together with a bias term—the perceptron’s threshold equivalent to the neuronal action potential threshold. This bias shifts the decision boundary, allowing for more flexible decision-making.

  • Activation Function: The total sum then passes through an activation function which decides whether the perceptron fires (outputs a 1) or not (outputs a 0), analogous to a neuron firing an action potential. This function is what allows the perceptron to make binary decisions, similar to a neuron deciding to fire based on the stimuli it receives.

Perceptron in Medicine: Practical Example

Let’s apply the perceptron to diagnose whether a patient’s symptoms suggest a viral or bacterial infection, using a broader range of inputs:

  • Inputs: Symptoms encoded as binary data, where 1 represents presence and 0 absence (e.g., cough, fever, high white blood cell count, patient age, resp rate, etc).

  • Training: Initially, the perceptron is trained on historical data where each case is labeled as bacterial or viral, allowing it to learn the weights for each symptom's importance.

  • Diagnosis: For a new patient, each symptom is inputted into the perceptron. It calculates the weighted sum and the activation function determines if the symptoms collectively suggest a bacterial infection (if the sum is above the threshold) or a viral infection (if below).

Understanding how a perceptron operates provides a clear insight into the basics of AI decision-making. For clinicians, this means a better grasp of how diagnostic AI tools analyze and interpret patient data, helping to integrate such technologies into your practice effectively. While the perceptron model may not be at the forefront of modern AI applications, its simplicity offers an invaluable lesson in the foundational principles of artificial neural networks. By breaking down the decision-making process into understandable components, it provides a framework for how more advanced computer systems mimic human decision-making behaviors.


Relevant Research Paper:  Adapted Large Language Models can outperform medical experts in text summarization

Purpose: This research investigates the effectiveness of adapted large language models (LLMs) in performing clinical text summarization tasks compared to traditional methods used by medical experts. The study's primary aim was to determine whether LLMs could enhance the accuracy and efficiency of summarizing clinical documents such as radiology reports, patient questions, progress notes, and doctor-patient dialogues.

Methods: The study utilized eight different LLMs, modified through adaptation strategies for specific summarization tasks across various clinical scenarios. Key metrics for evaluation included completeness, correctness, and conciseness of the generated summaries. The models tested included versions of GPT-3.5 and GPT-4, which were assessed using both quantitative syntactic, semantic, and conceptual evaluations, and a qualitative clinical reader study involving 10 physicians.

Results of the Study Adapted LLMs frequently outperformed human experts in summarization tasks:

  • In 36% of cases, summaries by LLMs were preferred by clinicians over those by human experts, with another 45% being rated as equivalent.

  • Safety analysis indicated that summaries from LLMs had a lower potential for medical errors and were less likely to contain fabricated information compared to those created by human experts.

  • Quantitative assessments highlighted the superior performance of LLMs across different types of clinical documentation.

Conclusion The study conclusively demonstrated that adapted LLMs could outperform medical experts in the summarization of clinical texts, suggesting a promising potential for these models to be integrated into clinical workflows. By reducing the documentation burden, LLMs could allow clinicians to devote more attention to patient care, thereby improving both efficiency and safety in healthcare settings.

Van Veen, D., Van Uden, C., Blankemeier, L., et al. (2024). Adapted large language models can outperform medical experts in clinical text summarization. Nature Medicine.


Tips and Tricks: Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) represents a significant leap in utilizing Large Language Models (LLMs) for more knowledge-intensive tasks in healthcare. First introduced by Meta AI researchers, RAG integrates an information retrieval component with a text generator, allowing the model to access and incorporate external knowledge sources dynamically. This method is particularly beneficial for medical applications where accuracy and up-to-date information are crucial.

What is Retrieval Augmented Generation: RAG is designed to overcome the limitations of static knowledge within standard LLMs by retrieving relevant documents or data in response to a query before generating an output. This approach ensures that the information provided by the LLM is not only contextually rich but also factually consistent and current. For healthcare professionals, RAG can be a game-changer by providing AI support that reflects the latest medical standards and research findings without the need for frequent retraining of the model.

Practical Example: Imagine a scenario where a healthcare professional needs to understand the latest treatment protocols for a rare disease. Using RAG, the LLM can retrieve the most recent clinical guidelines and research articles about the disease from trusted medical databases. The model then uses this retrieved information to generate a comprehensive, up-to-date response.

For instance, the prompt could be: "Update on treatment protocols for acute porphyria." RAG would first fetch relevant articles or clinical data on acute porphyria, and then synthesize this information to provide a detailed, accurate overview of current treatment strategies.

This capability not only enhances the reliability of the AI's outputs but also ensures that healthcare providers have access to the most current and validated information, aiding in better patient care and informed decision-making. RAG, by augmenting generation with targeted retrieval, promises to bridge the gap between dynamic medical knowledge and AI application, making it an indispensable tool in modern healthcare settings.

Thanks for tuning in,

Sameer & Michael


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