Abstract:
We hope you've found our recent explorations into Natural Language Processing (NLP) both enlightening and practical. As we wrap up our journey through the linguistic aspects of AI, we're excited to shift our focus to another fascinating frontier in artificial intelligence: generative models.
Over the next few editions, we'll be delving into various types of generative models, unraveling their complexities, and exploring their potential applications in healthcare. These powerful AI systems are revolutionizing how we create and manipulate data, opening up new possibilities for medical imaging, drug discovery, and personalized medicine.
Let's kick off this series by examining one of the most intriguing and powerful generative models: Generative Adversarial Networks, or GANs.
Core Concept: Generative Adversarial Networks (GANs)
Generative Adversarial Networks, or GANs, are a class of AI models that have taken the world of machine learning by storm. At their core, GANs are about creating new data that mimics the characteristics of real data. But what makes GANs truly fascinating is how they achieve this feat through a clever adversarial process.
The Components of a GAN
A GAN consists of two main components:
The Generator: This is the creative force of the GAN. Its job is to produce new data (like images or text) that resembles the training data.
The Discriminator: This is the critical eye of the GAN. It examines both real data from the training set and fake data from the generator, trying to distinguish between the two.
How GANs Work: An Analogy
To understand how GANs work, let's consider a medical analogy: Imagine a training exercise between a resident (our generator) and a staff physician (our discriminator). The resident, as part of their training, is tasked with creating patient case presentations. The staff physician's role is to determine which cases are real and which are fabricated.
The resident (generator) aims to create case presentations that are so realistic that the staff physician can't distinguish them from actual patient cases.
The staff physician (discriminator) uses their extensive experience to critically evaluate each case, determining whether it's genuine or fabricated.
As this educational process progresses:
Initially, the resident's fabricated cases are easily spotted by the staff physician, who provides detailed feedback.
With practice and feedback, the resident becomes increasingly adept at crafting believable cases, incorporating nuanced details and realistic presentations.
The staff physician continuously hones their ability to spot even the subtlest inconsistencies, pushing the resident to improve further.
This back-and-forth continues until the resident can create case presentations that are nearly indistinguishable from real patient cases, while the staff physician maintains a keen eye for authenticity. A few important aspects to remember with GANs:
The training process typically takes weeks to months, depending on the complexity of the data and the desired output quality.
The goal isn't for the resident (generator) to consistently fool the staff physician (discriminator). Instead, we aim for an equilibrium where the generated cases are of such high quality that they're useful for training purposes, while the staff physician maintains the ability to provide meaningful feedback.
A GAN is considered ready for practical use when it consistently produces high-quality, diverse outputs that serve the intended purpose – whether that's generating realistic medical images, synthesizing patient data for research, or another application.
It's worth noting that in practice, we often use the generator part of the GAN (our skilled resident) once training is complete, as it's the component that creates new data. The discriminator (our staff physician), having served its purpose in training, is typically not used in the final application. Within medicine, GANs have shown some promising applications:
Medical Imaging: GANs can generate synthetic medical images for training AI diagnostic systems, helping to address data scarcity issues in rare conditions.
Drug Discovery: By generating molecular structures, GANs can assist in the early stages of drug development, potentially speeding up the discovery of new treatments.
Anonymization: GANs can create synthetic patient data that maintains the statistical properties of real data while protecting individual privacy.
As we continue to explore generative models in upcoming editions, we'll dive deeper into these applications and examine how GANs and other generative models are reshaping the landscape of healthcare AI.
Research Paper: Integrating Clinical Guidelines With ChatGPT-4 Enhances Its' Skills
Purpose of the Study
The researchers aimed to evaluate whether integrating specific clinical guidelines into ChatGPT-4 could improve its accuracy and performance in answering medical questions. They focused on two areas: Clostridioides difficile infection (CDI) management and colon polyp surveillance guidelines.
Methodology
The team selected 10 multiple-choice board-style questions and open-ended clinical questions related to CDI management and colon polyp surveillance.
They tested ChatGPT-4's performance on these questions before integrating any guidelines.
Using the "askyourpdf" plugin, they uploaded PDF files of clinical guidelines from the American College of Gastroenterology (ACG) for CDI and the American Gastroenterological Association for colon polyp surveillance into ChatGPT-4.
They then retested ChatGPT-4 on the same questions after guideline integration.
In a secondary analysis, they explored how ChatGPT-4 handles multiple, potentially conflicting guidelines by integrating both ACG and Infectious Diseases Society of America guidelines for CDI.
Key Findings
Before guideline integration:Â
ChatGPT-4 answered 50% of multiple-choice questions correctly.
For open-ended clinical scenarios, ChatGPT-4's accuracy was 70%.
After guideline integration:Â
ChatGPT-4 achieved 100% accuracy for both multiple-choice and open-ended questions.
Responses included explanations and citations from the guidelines with page numbers.
When handling multiple guidelines:Â
ChatGPT-4 demonstrated the ability to summarize information from conflicting guidelines effectively.
Main Conclusions and Future Implications
The study demonstrates a remarkably simple yet effective method to enhance ChatGPT-4's clinical decision-making capabilities. By merely feeding important guidelines into the LLM, the researchers achieved a significant improvement in its ability to answer real-world clinical questions, jumping from 50-70% accuracy to 100%. This ease of implementation suggests a promising future for rapidly developing accurate clinical decision support tools using current AI technology. While this research is preliminary, it showcases the potential for seamlessly integrating up-to-date medical knowledge into AI systems, potentially revolutionizing how healthcare professionals access and apply clinical guidelines in practice. However, the authors caution that further research is needed to validate these tools across a broader range of clinical scenarios and to address important considerations such as ethical use, integration into existing workflows, and continuous updating of the knowledge base.
Tariq R, Voth E, Khanna S. Integrating Clinical Guidelines With ChatGPT-4 Enhances Its' Skills. Mayo Clin Proc Digital Health. 2024;2(2):177-180. doi:10.1016/j.mcpdig.2024.02.004
Tips and Tricks: Enhancing AI with Multimodal Chain-of-Thought (CoT)
Multimodal Chain-of-Thought (CoT), introduced by Zhang et al. (2023), brings a new dimension to AI decision-making by incorporating both text and visual data. Traditional CoT focuses solely on text-based reasoning, but Multimodal CoT allows the AI to integrate visual inputs—such as medical images—into its reasoning process, making it highly suitable for complex tasks like diagnosing from medical scans or interpreting lab results.
What is Multimodal CoT: This approach operates in two stages. First, it generates a rationale by combining textual and visual information. Then, the AI uses this multimodal rationale to infer a final answer. For healthcare professionals, this means that the AI can simultaneously analyze a patient’s medical history (text) and their X-ray or MRI results (visuals), leading to more informed and comprehensive decisions.
Practical Example:
Imagine an AI-powered system assisting in the diagnosis of pneumonia. Using Multimodal CoT, the process would involve:
Rationale Generation: The AI analyzes the patient’s symptoms and medical history (text), while also reviewing their chest X-ray (visual).
Answer Inference:Â Combining the information from both modalities, the AI produces a detailed diagnosis, taking into account not only the textual data but also the X-ray findings.
Thanks for tuning in,
Sameer & Michael
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