🔍 AI Myths & Realities: 🚨Understanding AI Limitations, 💡Predicting Hospital Stays, 🍏Personalized Nutrition with ChatGPT, and More!
Educational Supplement to The 'Med AI' Capsule Newsletter 🤖🩺🚀
Dear Med AI Enthusiast,
Welcome to this exclusive weekly feature, a carefully curated blend of valuable educational insights, complimenting your journey through The ‘Med AI’ Capsule - your essential weekly news digest 📰 on the dynamic intersection of artificial intelligence and other emerging technologies shaping the future of medicine. ⚕️
In today’s supplement:
1 Concept Made Easy
1 Knowledge Resource
1 Research Deep Dive
Industry Showcase - 3 Innovative Startups/ Companies
GenAI Corner
Test Your Knowledge
Reading Time: 5-7 minutes
Concept Made Easy 👨🏫
MYTH: AI algorithms are infallible and always provide accurate results 📊
REALITY:
AI algorithms are powerful tools, but they are not perfect. 🕵️
They can make errors, especially if the data they are trained on is biased or incomplete. 🗂️
Human oversight is necessary to ensure accuracy and interpret the results. 👨💻
Here's a simplified explanation:
Data bias: AI algorithms learn from historical data, and if that data is biased or lacks diversity, the algorithm can perpetuate those biases. 👀
For example, if the training data primarily consists of patient information from a specific demographic group, the algorithm may not generalize well to other populations. 👨🧑🏾👩🏿
Incomplete or inadequate training data: AI algorithms require large amounts of high-quality data to learn effectively. 📈
If the training data is insufficient or incomplete, the algorithm may not be able to generate accurate results. 📊
Additionally, if the algorithm encounters a situation or data point that it hasn't been trained on, it may struggle to provide accurate outputs. 🕵️
Interpretation and context: AI algorithms can generate predictions or recommendations based on patterns they identify in data. 🔍
However, interpreting those results and understanding their context requires human expertise. 🧑⚕️
Healthcare professionals need to critically analyze and validate AI-generated outputs before making any clinical decisions. ⚕️
Lack of transparency: Some AI algorithms, such as deep learning models, can be complex and opaque. 🎁
They may provide accurate results, but it can be challenging to understand the exact reasons behind their decisions. 🤖
This lack of transparency can be a concern in healthcare, where clear explanations and justifications are crucial for patient safety and trust. 🤝
To mitigate these challenges, healthcare professionals must play an active role in the development, validation, and implementation of AI algorithms. 👨 They need to be aware of the limitations and biases of AI systems and use them as tools to support their clinical judgment rather than relying solely on their outputs. 📊 Human oversight is crucial to ensuring the accuracy, safety, and ethical use of AI in healthcare. 🏥
Knowledge Resource 📚
In this YouTube video, Stanford Professor of Medicine Dr. Nigam Shah explains how a usefulness analysis can help convince stakeholders of the impact of machine learning in healthcare. He provides examples of use cases and answers questions pertaining to application in different scenarios, the future of the technology, and more.
You Will:
- Learn about various tools that can be beneficial in completing usefulness analysis in healthcare.
- Understand how existing frameworks can assist in testing the usefulness of predictive models in practice.
- Go through an example of a usefulness analysis to see how predictive models can affect patient care.
Research Deep Dive 🔬
Li W, Zhang Y, Zhou X, Quan X, Chen B, Hou X, Xu Q, He W, Chen L, Liu X, Zhang Y, Xiang T, Li R, Liu Q, Wu SN, Wang K, Liu W, Zheng J, Luan H, Yu X, Chen A, Xu C, Luo T, Hu Z. Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study. J Orthop Surg Res. 2024 Feb 2;19(1):112. doi: 10.1186/s13018-024-04576-4. PMID: 38308336.
This paper focuses on developing a machine learning model to predict prolonged hospital length of stay (LOS) after spine correction surgery.
Key Findings:
K Nearest Neighbors algorithm performed best (AUROC: 0.8191, PRAUC: 0.6175)
Top contributing variables: preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells
Development of a web-based calculator for clinical use
Conclusion: The predictive model provides valuable prognostic information for clinicians in managing patients undergoing spine correction surgery.
Limitations:
Limited consideration of psychological factors
Relatively small sample size
Focus on preoperative variables
Industry Showcase 👨🏭
PathAI - utilizes artificial intelligence to improve pathology services. Their platform assists pathologists in diagnosing diseases more accurately and efficiently by analyzing pathology images. PathAI's technology can be particularly beneficial in diagnosing cancer and other complex diseases, leading to better patient outcomes.
Proximie - a platform that utilizes augmented reality (AR) and artificial intelligence (AI) to enable surgeons to collaborate in real-time, regardless of their physical location. It enhances surgical procedures, provides training opportunities, and aims to improve patient outcomes.
Tempus - revolutionizing healthcare by harnessing data-driven insights. They apply AI and machine learning to clinical and molecular data, aiding healthcare providers in making more informed decisions. Tempus' technology supports personalized treatment plans and advancements in precision medicine.
GenAI Corner 🤖
Tutorial: Using ChatGPT to Create Personalized Diet Plans
Step 1: Understand the Client's Dietary Goals
Begin by discussing the client's dietary objectives and any specific requirements, such as weight loss, muscle gain, or dietary restrictions (e.g., vegetarian, gluten-free).
Step 2: Gather Client Information
Collect essential client information, including age, gender, weight, height, activity level, and any medical conditions or allergies.
Step 3: Outline Nutritional Guidelines
Define the nutritional guidelines that should be followed, such as caloric intake, macronutrient ratios (carbohydrates, proteins and fats), and meal frequency (e.g., three meals and two snacks per day).
Step 4: Craft a Detailed Prompt for ChatGPT
Create a comprehensive prompt for ChatGPT, incorporating the client's dietary goals, personal information, nutritional guidelines, and any preferred food choices or restrictions. For example:
"Generate a personalized daily diet plan for a 35-year-old male client who wants to lose weight. He weighs 180 lbs, is 6 feet tall, and leads a moderately active lifestyle. The plan should consist of three meals and two snacks per day, totaling 1,800 calories. Focus on balanced macronutrients, incorporating lean proteins, whole grains, and plenty of vegetables. Avoid dairy and gluten due to allergies."
Step 5: Generate the Personalized Diet Plan
Utilize ChatGPT to create a tailored diet plan based on the provided information, ensuring it aligns with the client's dietary goals and restrictions.
Step 6: Review and Adjust as Necessary
Carefully review the diet plan to confirm that it meets the client's requirements. Modify any components as needed, such as portion sizes, food substitutions, or meal timings.
Test Your Knowledge 🧩
Your feedback is crucial to me, as it helps me understand your interests and improve my offerings. I would appreciate it if you could take a few minutes to share your thoughts about what you've enjoyed and what you think I could do better - https://forms.gle/fUJZhjUBJe9ojySg6
Stay tuned for our upcoming editions as we explore the latest breakthroughs and dive deep into the transformative power of artificial intelligence and emerging technologies, shaping a healthier future. 🚀
Warm regards,
