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Victor
Student
Awka, Anambra, Nigeria
1
Question
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Where do I start off as a medical student interested in AI/ML?
I'm a medical student who's deeply interested in AI and machine learning and building valuable AI systems in healthcare and medicine. I'm quite good at Python and I've been trying to take AI/ML courses on my own, but I've never been so confused.
Where do I start? How do I proceed? Do I need all the math? All the resources I lay my hands on end up confusing me more the previous.
How do I get to the point where I'll actually start building stuff? I feel like I've been so stuck for months now.
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1 answer
Jean Noel Sanchez
Developer/Data Analyst
138
Answers
New York, New York
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Jean Noel’s Answer
Hi -I am in the AI industry. You are stuck because almost all AI/ML courses are built for **computer science majors** who need to build algorithms from scratch, rather than **clinicians** who need to apply, adapt, and validate models on medical datasets.
You do **not** need multivariable calculus, linear algebra proofs, or theoretical backpropagation to build valuable medical AI systems. You need practical **applied machine learning** and domain-specific data handling.
Here is a clear, step-by-step roadmap to unstick yourself and get directly to building.
---
## 1. Ditch General Courses, Switch to Applied Medical AI
Stop taking broad "Machine Learning 101" courses that start with linear regression matrix derivations. Instead, focus on applied libraries and medical workflows.
* **For Core ML/Deep Learning:** Take the **[Fast.ai](https://course.fast.ai/)** course ("Practical Deep Learning for Coders"). Fast.ai uses a top-down approach: you build and train a working deep learning model in lesson 1, and only dive into the mechanics later as needed.
* **For Medical-Specific AI:** Complete the **[Coursera AI for Medicine Specialization](https://www.coursera.org/specializations/ai-for-medicine)** (by DeepLearning.AI). It directly covers medical image classification, survival analysis, prognosis models, and NLP on EHR data using real medical datasets.
* **Framework to Focus On:** Standardize on **PyTorch** and **MONAI** (Medical Open Network for AI). MONAI is the industry-standard PyTorch-based framework specifically built for medical imaging (DICOM/NIfTI files, 3D segmentation, data augmentation).
---
## 2. How Much Math Do You *Actually* Need?
As a clinical AI researcher or builder, your math requirement is **conceptual, not computational**:
| What You DO Need | What You DO NOT Need |
| --- | --- |
| **Basic Probability & Stats:** Sensitivity, specificity, PPV/NPV, ROC-AUC, confidence intervals, calibration curves. | Calculating gradients by hand or writing matrix multiplication algorithms from scratch. |
| **Intuitive Vector Understanding:** Understanding embeddings as numeric representations of text or images. | Rigorous proofs in linear algebra or differential equations. |
| **Loss Functions (Conceptual):** Understanding what cross-entropy or Dice loss penalizes during training. | Deriving partial derivatives for backpropagation. |
If you can interpret clinical trial statistics and ROC curves, you already possess 80% of the statistical foundation required to build and evaluate medical AI models.
---
## 3. Your Path to Building: The "Project-First" Strategy
The fastest way out of tutorial paralysis is to stop watching videos and start building simple, end-to-end projects with real clinical datasets.
1. **Start with Tabular Clinical Data:** Focus: Feature engineering & baseline models.
Begin with simple tabular healthcare datasets (e.g., predicting ICU readmission, diabetes risk, or sepsis onset). Use **scikit-learn** or **XGBoost**. This teaches you data cleaning, handling missing clinical values, train/test splitting, and evaluation metrics without needing GPUs.
2. **Move to 2D Medical Imaging:** Focus: Transfer learning.
Use a pre-trained model (like ResNet or DenseNet) to classify chest X-rays (e.g., using the NIH ChestX-ray14 dataset or Kaggle Pneumonia dataset). Transfer learning lets you achieve state-of-the-art results on medical images with only a few dozen lines of code.
3. **Explore Medical NLP or Segmentation:** Focus: Specialized domain tooling.
* **Imaging:** Use **MONAI** to perform 3D brain tumor segmentation on MRI scans (BraTS dataset).
* **Clinical Text:** Fine-tune a small LLM or BioBERT model on clinical notes or medical board-style QA data.
4. **Deploy a Simple Demo:** Focus: Usability for clinicians.
Wrap your trained model into a web interface using **Gradio** or **Streamlit** (5–10 lines of Python). Being able to drag-and-drop an X-ray into a web browser and display a prediction heat map makes your work immediate, interactive, and easy to demonstrate to clinical mentors.
---
## 4. Leverage Your Medical Superpower
As a medical student who can code, **your domain knowledge is your primary edge**. Pure computer scientists often struggle with:
* Understanding clinical context and actionable endpoints.
* Spotting data leakage (e.g., an AI detecting a chest tube rather than the pneumothorax itself).
* Knowing what clinical questions actually matter to physicians.
To capitalize on this:
1. **Find a Clinical Mentor:** Approach attendings or researchers in specialties heavy on digital data (Radiology, Pathology, Ophthalmology, Dermatology, Cardiology, or ICU Medicine). Ask them for a specific clinical problem or dataset they want analyzed.
2. **Join Competitions:** Explore healthcare challenges on **Kaggle** or **Grand Challenge** (grand-challenge.org). Looking at top solution notebooks on these platforms teaches you production-grade code faster than any course.
You do **not** need multivariable calculus, linear algebra proofs, or theoretical backpropagation to build valuable medical AI systems. You need practical **applied machine learning** and domain-specific data handling.
Here is a clear, step-by-step roadmap to unstick yourself and get directly to building.
---
## 1. Ditch General Courses, Switch to Applied Medical AI
Stop taking broad "Machine Learning 101" courses that start with linear regression matrix derivations. Instead, focus on applied libraries and medical workflows.
* **For Core ML/Deep Learning:** Take the **[Fast.ai](https://course.fast.ai/)** course ("Practical Deep Learning for Coders"). Fast.ai uses a top-down approach: you build and train a working deep learning model in lesson 1, and only dive into the mechanics later as needed.
* **For Medical-Specific AI:** Complete the **[Coursera AI for Medicine Specialization](https://www.coursera.org/specializations/ai-for-medicine)** (by DeepLearning.AI). It directly covers medical image classification, survival analysis, prognosis models, and NLP on EHR data using real medical datasets.
* **Framework to Focus On:** Standardize on **PyTorch** and **MONAI** (Medical Open Network for AI). MONAI is the industry-standard PyTorch-based framework specifically built for medical imaging (DICOM/NIfTI files, 3D segmentation, data augmentation).
---
## 2. How Much Math Do You *Actually* Need?
As a clinical AI researcher or builder, your math requirement is **conceptual, not computational**:
| What You DO Need | What You DO NOT Need |
| --- | --- |
| **Basic Probability & Stats:** Sensitivity, specificity, PPV/NPV, ROC-AUC, confidence intervals, calibration curves. | Calculating gradients by hand or writing matrix multiplication algorithms from scratch. |
| **Intuitive Vector Understanding:** Understanding embeddings as numeric representations of text or images. | Rigorous proofs in linear algebra or differential equations. |
| **Loss Functions (Conceptual):** Understanding what cross-entropy or Dice loss penalizes during training. | Deriving partial derivatives for backpropagation. |
If you can interpret clinical trial statistics and ROC curves, you already possess 80% of the statistical foundation required to build and evaluate medical AI models.
---
## 3. Your Path to Building: The "Project-First" Strategy
The fastest way out of tutorial paralysis is to stop watching videos and start building simple, end-to-end projects with real clinical datasets.
1. **Start with Tabular Clinical Data:** Focus: Feature engineering & baseline models.
Begin with simple tabular healthcare datasets (e.g., predicting ICU readmission, diabetes risk, or sepsis onset). Use **scikit-learn** or **XGBoost**. This teaches you data cleaning, handling missing clinical values, train/test splitting, and evaluation metrics without needing GPUs.
2. **Move to 2D Medical Imaging:** Focus: Transfer learning.
Use a pre-trained model (like ResNet or DenseNet) to classify chest X-rays (e.g., using the NIH ChestX-ray14 dataset or Kaggle Pneumonia dataset). Transfer learning lets you achieve state-of-the-art results on medical images with only a few dozen lines of code.
3. **Explore Medical NLP or Segmentation:** Focus: Specialized domain tooling.
* **Imaging:** Use **MONAI** to perform 3D brain tumor segmentation on MRI scans (BraTS dataset).
* **Clinical Text:** Fine-tune a small LLM or BioBERT model on clinical notes or medical board-style QA data.
4. **Deploy a Simple Demo:** Focus: Usability for clinicians.
Wrap your trained model into a web interface using **Gradio** or **Streamlit** (5–10 lines of Python). Being able to drag-and-drop an X-ray into a web browser and display a prediction heat map makes your work immediate, interactive, and easy to demonstrate to clinical mentors.
---
## 4. Leverage Your Medical Superpower
As a medical student who can code, **your domain knowledge is your primary edge**. Pure computer scientists often struggle with:
* Understanding clinical context and actionable endpoints.
* Spotting data leakage (e.g., an AI detecting a chest tube rather than the pneumothorax itself).
* Knowing what clinical questions actually matter to physicians.
To capitalize on this:
1. **Find a Clinical Mentor:** Approach attendings or researchers in specialties heavy on digital data (Radiology, Pathology, Ophthalmology, Dermatology, Cardiology, or ICU Medicine). Ask them for a specific clinical problem or dataset they want analyzed.
2. **Join Competitions:** Explore healthcare challenges on **Kaggle** or **Grand Challenge** (grand-challenge.org). Looking at top solution notebooks on these platforms teaches you production-grade code faster than any course.