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  • Hongjian Zhou

Newsletter from The Neural Medwork: Issue 11

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

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