I think that we all are in the process of learning the answer to that question. I don't think a simple post will grasp all the areas that should be covered when talking about the role of machine learning (ML) in healthcare data analysis, I’ll try to elaborate what my understanding is.

First of all, let’s see why we need healthcare data analysis.

You must have heard enough of examples the 1st point. Newly emerging predictive models for diagnosis, real-time monitoring systems, personalized management strategies guided by ML Algorithms and much more. The other two may not be obvious at first glance, but identification of fraudulent acts, maintaining regulatory compliance, detecting cyber threats are parts of the healthcare data analysis as well.

We already had these targets even before this wide use of ML. Traditional statistical methods were used in order to fulfill these needs. However traditional statistical methods had their own strengths as well as weaknesses. Their weaknesses became a limitation to expanding the horizon for the next generation.

We needed to analyze data types previously not used by traditional methods such as unstructured data and images. The velocity that data generated exceeded the limits of traditional methods. We needed to overcome the limitations our traditional methods had such as assumptions. We needed methods that could interpret data in different ways.

That's where the traditional methods were upgraded into machine learning.

So the role of machine learning in healthcare analysis is to facilitate reaching the goals I mentioned before better than the method we already had!

One thing to remember even if it is out of the topic is, that ML has its own weaknesses. Lack of explainability of some ML Algorithms, and the possibility of interference by bias are some of these issues better to keep in the back of your mind while dealing with them.

Building on Mohomad Cassim Ruseik Rahumath's points, I’d like to highlight another key aspect.

From a pathology perspective, machine learning can be viewed as a powerful tool for capturing and analyzing the complex, dynamic nature of diseases. Conventional statistical methods often struggle to account for these dynamic changes, which may oversimplify the progression and interactions within a disease process. For instance, cancer progression is highly heterogeneous, involving variables like genetic mutations, epigenetic changes, tumor microenvironment interactions, and treatment responses that can fluctuate over time.

In traditional statistics, analyzing such a high-dimensional dataset is often limited by the need for predefined assumptions about the relationships between variables. This can lead to model overfitting or underfitting, depending on the complexity of the dataset and how well it aligns with the assumptions made by conventional models such as regression analysis or survival models like the Cox proportional hazards model. These methods might fail to capture non-linear interactions or dynamic time-dependent effects within disease trajectories.

On the other hand, ML excels in working with large, multidimensional datasets without needing prior assumptions about the data structure. Techniques like random forests and neural networks can effectively learn from non-linear relationships, missing data, and complex interactions among clinical, genomic, and imaging features. For example, in cancer research, ML algorithms can utilize longitudinal patient data, including evolving tumor characteristics, molecular markers, and treatment responses, to predict survival more accurately than traditional models like Kaplan-Meier curves or Cox regression.

By leveraging these capabilities, researchers can better predict outcomes, personalize treatment plans, and enhance our understanding of disease mechanisms, ultimately leading to more tailored and effective therapeutic strategies.