Should Doctors Learn Artificial Intelligence?

In today's rapidly evolving world, the pervasive presence of AI is undeniable, with its impact extending into various facets of our lives, including healthcare. Traditionally, doctors have regarded AI as residing beyond the boundaries of health sciences. However, as AI's footprint in healthcare expands and its potential for tailored solutions becomes increasingly evident, a pivotal question emerges: Should learning AI become an integral part of a healthcare professional's career?

Can Doctors Learn AI?

While this question may seem straightforward at first glance, a deeper exploration is warranted to construct a compelling argument. To embark on the journey of AI, doctors require proficiency in two fundamental areas: programming languages and mathematics. Among programming languages, Python emerges as a particularly fitting choice for AI due to its user-friendly nature and adaptability. Given that AI endeavors to tackle challenges across diverse domains, including healthcare, it is imperative for specialists like doctors to acquire coding skills to contribute meaningfully.

Moreover, it is vital to underscore that mastering advanced programming concepts is not a prerequisite for delving into AI. Doctors can initiate their AI exploration by mastering basic coding principles. Hands-on experience plays a pivotal role; only through practical engagement with coding can one truly grasp its intricacies. We will delve into available learning resources subsequently.

AI Relies on Mathematics:

Many doctors, including myself, may have left mathematics behind during our pre-university education—a shared experience within our profession. However, it is pertinent to note that while AI necessitates a degree of mathematical understanding, one need not aspire to attain expertise akin to that of a seasoned mathematician to comprehend its principles and develop bespoke AI models.

I staunchly advocate that the foundational mathematical knowledge required for AI can be gleaned from elementary sources, such as the 12th standard NCERT curriculum. Proficiency in fundamental concepts like matrices and basic differentiation serves as the springboard for embarking on your AI learning odyssey. With dedication and practice, additional mathematical techniques and insights will naturally accrue over time.

How Overwhelming is the AI Learning Journey?

Undoubtedly, it is nowhere near as demanding as our medical college subjects. The amount of hard work required there far surpasses what is needed here. This does not trivialize AI learning; I am speaking from the perspective of creating and consuming models. It is much simpler. Of course, from the standpoint of advanced research, AI is a significant field, but for our practical purposes, it is feasible to pick up and create models and customize them to automate everyday tasks of ours.

Domain-specific roles in AI:

Here, I am discussing how our medical knowledge can be applied to models. One of them is the annotation of images, for example, in case you are planning to automate the model to predict some kind of lesion in images or videos. Initially, there needs to be a label of the lesion, which no doubt doctors like us have the capability for. These annotations, when fed to your training model, AI will learn to recognize the lesion.

What part of AI is more relevant to healthcare?

AI is a vast field; in that, deep learning, basically convolutional neural networks or neural networks, are more important for healthcare, as our data is more unstructured. Other kinds of AI also need to be known, but this is more important from the healthcare point of view.

How neural networks work?

Here I will oversimplify the neural network concepts. All that data, be it images or language, can be constructed into a matrix. The matrix means the one which we learn in pre-university courses. When data for input is converted to a matrix, there are methods for manipulating matrices like dot product and transpose. With this, you can continue to manipulate the input matrix by reducing the size. Over multiple layers of manipulation, these input matrices will fall into desired classification groups, such as 2 or 3, that are pre-specified. Thus, neural networks work. This explanation I have provided is an oversimplification.

Now, let's briefly touch upon different types of deep learning:

Supervised Learning: In supervised learning, the model learns from labeled data, where the input data is paired with the correct output. The algorithm learns to map the input data to the correct output during training, making predictions based on this learned mapping.

Unsupervised Learning: In unsupervised learning, the model learns from unlabeled data, seeking to find hidden patterns or structures within the data. Unlike supervised learning, there are no predefined labels for the input data. Instead, the algorithm identifies similarities or differences between data points to organize or categorize them.

Reinforcement Learning: In reinforcement learning, the model learns to make decisions by interacting with an environment. The model receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal behavior through trial and error.

Each of these approaches has its own strengths and applications in various domains, including healthcare.

Resources for AI learning:

My Favotite is FreeCodecamp

Learn Python - Full Course for Beginners [Tutorial]

PyTorch for Deep Learning & Machine Learning – Full Course

Other resources

1. Coursera:

   - Course: "AI For Everyone" by Andrew Ng
   - Link: AI For Everyone
   - Description: This course provides a non-technical introduction to AI, suitable for individuals from various backgrounds, including healthcare professionals. It covers key concepts, applications, and implications of AI.

2. edX:

   - Course: "Introduction to Artificial Intelligence (AI)" by IBM
   - Link: Introduction to Artificial Intelligence (AI)
   - Description: This course offers an overview of AI concepts, including machine learning, deep learning, and neural networks, with practical examples and case studies.

3. Udacity:

   - Course: "Intro to Machine Learning with PyTorch" by Udacity
   - Link: Intro to Machine Learning with PyTorch
   - Description: This course teaches machine learning basics using PyTorch, a popular Python library for deep learning. It covers topics such as supervised and unsupervised learning, neural networks, and model evaluation.

4. Google's Machine Learning Crash Course:

   - Link: Machine Learning Crash Course
   - Description: Google's Machine Learning Crash Course provides a fast-paced introduction to machine learning concepts, tools, and techniques. It includes interactive exercises and real-world examples.

5. Kaggle:

   - Link: Kaggle 
   - Description: Kaggle offers a variety of datasets, competitions, and tutorials related to AI and machine learning. Healthcare-specific datasets and competitions can provide valuable hands-on experience for doctors interested in applying AI to healthcare problems.

These resources provide a mix of introductory courses, practical tutorials, and hands-on projects that can help doctors get started with learning AI.

Conclusion

In conclusion, the integration of AI into healthcare presents a promising avenue for improving patient care, optimizing processes, and advancing medical research. While the idea of doctors learning AI may seem daunting at first, it is essential for healthcare professionals to embrace this technology to stay relevant in an increasingly digital world. By acquiring foundational knowledge in programming languages and mathematics, doctors can leverage AI to address complex healthcare challenges and enhance their clinical practice. With the plethora of online resources available, doctors have ample opportunities to learn AI at their own pace and integrate it into their professional repertoire. Embracing AI learning is not just about adapting to change; it's about empowering doctors to become innovators and leaders in shaping the future of healthcare.