Abstract:
Welcome to the 11th edition of the Neural Medwork Newsletter, As we reach this pivotal 11th edition, we find ourselves reflecting on the journey we've embarked upon together. It fills us with immense gratitude to witness the growth of this community, united by a shared enthusiasm for unravelling the mysteries of AI in healthcare. Our mission, from the very start, has been to illuminate the path towards understanding AI's vast potential, keeping you abreast of ground-breaking research, and equipping you with the knowledge to use AI tools safely and efficiently.
The world of AI is fast-evolving, and healthcare professionals must stay informed
and skilled in leveraging these technologies. Today we take a moment to look back and
consolidate our learning, ensuring that we not only keep pace with AI's advancements but also deeply understand the foundational principles that guide its application in healthcare. This recap is more than just a reflection; it's a celebration of our collective progress and a reinforcement of the knowledge we've accumulated.
Core Concepts: An Overview
In terms of core concepts, we spent the past 10 episodes explaining some of the major terms that one needs to know in the world of AI as well as defining various AI models that are used in practice.
Issue Number | Core Concept | Brief Summary |
1 | Neural Networks | Explores the basics of neural networks, illustrating how they mimic human brain functions to process information and make decisions, serving as the backbone for many AI applications in healthcare. |
2 | Large Language Models (LLMS) | Delves into the workings of LLMs like GPT-3, focusing on their ability to understand and generate human-like text, enhancing AI‘s linguistic mastery in healthcare applications. |
3 | Transformers in AI | Introduces the Transformer architecture, crucial for understanding context and nuances in language, significantly improving AI‘s ability to process and generate language effectively. |
4 | Types of Machine Learning | Covers the four primary types of machine learning (Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning), highlighting their applications and importance in healthcare AI. |
5 | Decision Trees | Examines the Decision Tree model, a fundamental AI algorithm that uses a tree-like model of decisions and their possible consequences, useful for classification and regression in healthcare data. |
6 | Random Forest Algorithms | Discusses Random Forest, an ensemble learning method that builds multiple decision trees and merges them to get a more accurate and stable prediction, often used in complex diagnostic modeling. |
7 | K-Nearest Neighbors (KNN) | Explores KNN, a simple yet effective algorithm for classification and regression tasks in healthcare, based on feature similarity. |
8 | Naïve Bayes Classifier | Describes the Naïve Bayes classifier, a straightforward probabilistic classifier based on Bayes's theorem, with strong assumptions about the independence of features, used in various healthcare applications for classification tasks. |
9 | Gradient Boosting | Highlights Gradient Boosting, a method of producing a predictive model through an ensemble of weak prediction models, typically decision trees, emphasizing their utility in handling diverse and complex healthcare datasets. |
10 | XG Boost | Focuses on XG Boost, an optimized gradient-boosting library that enhances model performance and speed, especially in large and complex healthcare data analytics. |
Research Papers: An Overview
The ten papers we highlighted range from summarizing the history of AI to presenting
real-world validation of AI tools in practice, including use cases such as patient consent,
and clinical decision support.
Issue Number | Study Title | Brief Summary |
1 | Competencies for the Use of Artificial Intelligence–Based Tools by Health Care Professionals | Defines AI-related clinical competencies for healthcare professionals, emphasizing the need for AI literacy in improving patient care and clinical outcomes. |
2 | Large Language Model-Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures | Evaluates LLMs in generating informed consent documents, finding them to potentially enhance patient comprehension and streamline clinical documentation processes. |
3 | Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum | Shows AI chatbots like ChatGPT can provide high-quality, empathetic responses to patient questions online, suggesting a supportive role in patient communication. |
4 | Towards Conversational Diagnostic AI | Discusses AMIE, an AI system for medical diagnostics, highlighting its comparable accuracy and communication skills to primary care physicians in simulated exams. |
5 | Three Epochs of Artificial Intelligence in Health Care | Outlines the evolution of AI in healthcare into three epochs, showcasing the advancements and shifting capabilities of AI technologies in medical applications. |
6 | Evaluating GPT-4 as a Clinical Decision Support Tool in Ischemic Stroke Management | Assesses GPT-4's effectiveness in supporting clinical decisions in stroke management, indicating the potential for AI to augment clinical judgment and patient care. |
7 | Enhancing Physician-Patient Interactions with Ambient AI Scribes: A Kaiser Permanente Experience | Examines the impact of AI scribes on documentation workload, physician-patient interaction quality, highlighting both the benefits and the challenges of integration. |
8 | Diagnostic reasoning prompts reveal the potential for a large language model interpretability in medicine | Investigates LLM's ability to form accurate, interpretable diagnoses with specialized prompts, paving the way for enhanced AI use in clinical reasoning. |
9 | External Validation of a Commercial Acute Kidney Injury Predictive Model | Focuses on validating a machine learning model for predicting acute kidney injury, underscoring the importance of external validation in clinical AI applications. |
10 | Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study | Highlights Foresight's capability in forecasting patient health events using EHR data, demonstrating the model's precision and potential in proactive healthcare management. |
Tips and Tricks: An Overview
Issue Number | Study Title | Brief Summary |
1 | WISER Mnemonic for ChatGPT Prompting | WISER (Who, Instructions, Subtasks, Examples, Review) fine-tunes ChatGPT prompts for precise, relevant medical information. Use this mnemonic to structure prompts that yield actionable responses for clinical questions or educational content​. |
2 | Mastering Role Play with ChatGPT | Define ChatGPT’s role (e.g., educator, researcher) to tailor its responses for specialized knowledge or perspectives, especially useful in nuanced medical scenarios​. |
3 | Training ChatGPT on Your Personal Style | Customize ChatGPT to match your communication style, optimizing for personalized patient communication or efficient research​. |
4 | Chain of Thought (CoT) Prompting | Utilize CoT prompting to unravel complex medical reasoning with AI, ideal for enhancing diagnostic processes or exploring treatment options​. |
5 | Adjusting ChatGPT's Temperature | Alter the temperature setting to balance the creativity and predictability of responses, lowering it for accuracy in clinical information, and increasing for ideation or creative tasks​. |
6 | Enhancing CoT Prompting with Self-Consistency | Improve CoT prompting by encouraging AI to generate multiple answers and select the most consistent one, enhancing reliability in clinical reasoning or decision-making processes. |
7 | Mastering Few-Shot In-Context Learning | Leverage few-shot learning by providing ChatGPT with examples within the prompt, guiding it to understand and apply specific knowledge formats or answer styles, particularly effective for unique clinical scenarios or specific research questions. |
8 | Knowledge Prompting in Healthcare | Employ knowledge prompting to extract targeted information from AI, framing questions to draw upon its vast knowledge base for evidence-based answers, useful in keeping up with recent healthcare research or guidelines. |
9 | Prompt Chaining | Use prompt chaining to break down complex tasks into a series of simpler prompts, allowing for a detailed exploration of multifaceted medical issues or comprehensive patient education plans. |
10 | Tree of Thoughts (ToT) | Implement the ToT method for structured problem-solving, mimicking a clinician’s diagnostic process by evaluating multiple reasoning paths, enhancing decision-making in complex clinical cases |
Thanks for tuning in,
Sameer & Michael