AI-Powered Disease Risk Prediction Model Development

AI is decades in the making, but it primarily gained momentum in the past decade with the emergence of Large Language Models (LLMs) like ChatGPT, Bard, Llama, and Grok. OpenAI’s ChatGPT launch in November 2022 was disruptive enough to send revolutionary ripples through global industries, including healthcare. However, just four months later, OpenAI launched its much-improved version with a massive difference in capabilities. Its new release, GPT-4, was a diffusion model that could assist in medical imaging by taking predictive analysis to the next level. 

This was just some history about the beginning of diffusion and predictive models. However, Predictive analysis is a big part of preventive care. It can help governments lower medical treatment costs in the global healthcare market, which is already at an alarming $11.3 trillion. In terms of both identifying diseases in their early stages and empowering the medical infrastructures of low-income countries with limited resources, the impact of AI models in predictive analysis is groundbreaking.

In this article, we will jump into the various use cases of AI in medical predictive analysis and the best approach to develop an AI-powered disease risk prediction model. 

AI Use Cases in Disease Prediction

In many use cases, we can consider AI to be the closest thing ever developed to a human brain. AI models simply collect data and learn. This is exactly what supervised and unsupervised AI models do to identify risks and patterns associated with diseases and help with an early diagnosis. 

Some essential use cases to note are:

• Help in the Early Detection of Cancer: AI models can collect and analyse medical imaging such as X-rays, MRIs, and CT scans to detect early stages of cancer with high accuracy. 

• Maintain Cardiovascular Health: By combining medical history reports, blood test reports, cholesterol levels, ECG reports, etc., LLMs can run predictive analysis across broad factors and provide a general assessment of a person’s cardiovascular health.

• Diabetes Management: The risk of diabetes can be detected early on from a patient’s family history, blood test reports, glucose reports, and tracking physical activity levels from wearable devices. 

• Monitoring Neurodegenerative Diseases: Neurodegenerative diseases like Alzheimer’s or Parkinson’s develop over time because of age and genetic factors. By analyzing speech patterns, brain imaging, and neural networks, AI can predict these conditions before the arrival of actual symptoms. 

• Controlling Outbreaks: AI models can analyze outbreak data on a large scale to assist with better control and management. For example, AI can identify regions at high risk of outbreak and even predict an increase in the number of patients to help hospitals and governments in the better management of resources. 

Early detection of diseases also means a dramatic improvement in survival rates, thorough management of resources, and timely measures in case of outbreaks. Doctors and general practitioners worldwide can get more time to work on cures and identify solutions to otherwise almost undetectable diseases and conditions.

How AI Models Detect Diseases? 

AI models generate predictive reports and identify diseases by manipulating, or simply adding or removing noise in the training data. This process of adding and removing noise is done with the help of Artificial Neural Networks (ANN), which recognize patterns, and the Markov Chain, which performs predictive analysis based on evidence. 

Step-by-Step breakdown of the process:

Step 1: Training

A supervised learning model is trained on a complex dataset that contains millions of data points, such as CT scans, X-rays, MRI scans, genome data, lab test reports, EHR reports, etc. 

Step 2: Forward Process

AI models add random noise to raw data, such as imperfect medical data of the real world, including spiked values in lab reports, blurred imaging, incomplete patient histories, and inconsistent entries. This process of adding noise is called the forward process or diffusion process.

Step 3: Reverse Process

This is where ANN and Markov Chain algorithms come in to reduce noise, recognize patterns, and identify missing values. For example, improve the clarity of low-quality MRI, detect anomalies in the pattern (like tumours), or identify gaps in health records to diagnose diseases.  This process of refining the noise is called the reverse process.

Step 4: Give Refined Output

After a forward and reverse process, AI refines its output to give a clearer, vivid, and more concrete idea of the problem. For example, let’s say “this patient has a 70% risk of Type 2 diabetes within 5 years” or “this MRI scan shows early signs of Alzheimer’s with 85% confidence.”

Benefits of Using AI Models for Preventive Analysis

1. Preservation of Life

With early detection, AI prevents patients from reaching critical stages of their lives. This way, it helps preserve the value of human life. The number of patients who can be saved with the help of AI and the diseases that can be cured make its adoption in the industry non-negotiable. 

2. Cost Saving

In terms of cumulative expenditure on healthcare, preventive analysis is cheaper than providing medical treatments. This approach is beneficial for both developed and developing countries, where medical costs remain a major concern. Even after identification, AI models can reduce the cost of treatment by providing a clear path of treatment instead of relying on a trial-and-error approach. 

3. Higher Accuracy

With the help of predictive analysis, AI detects risks and health concerns that are otherwise non-recognizable by the human eye, especially in their early stages, without the appearance of any symptoms. The chances of overlooked diseases or misdiagnosis are comparatively lower than traditional diagnostic methods.

4. Time Saving

The speed and efficiency of AI data evaluation are unparalleled. Analyzing millions of reports simultaneously helps medical and research organizations save immense time. This time can be redirected to a more essential workload, such as finding treatments and cures for diseases. The mass scale evaluation also means that hospitals can quickly assess population-level threats and prevent diseases.

5. Personalized Healthcare

Global industries are using AI models to pursue personalization and localization in services. Even in the healthcare industry, personalizing treatment plans and offering valuable healthcare for each and every patient is easier with AI. The personalized assistance means more convenience and better treatment for patients in comparison to following a general healthcare routine for all patients. 

6. Continuous Learning

From the results driven by AI training data, we can infer that AI learns faster than humans. The more data they collect, the more refined and faster their predictions. Also, while human expertise is short-lived, AI can be preserved for a longer time in the future, eventually helping in effectively transferring and carrying forward the current medical knowledge to future generations. 

How to Develop an AI-Powered Disease Risk Prediction Model

You will need to develop two things: the first is the AI-powered disease risk prediction model, and a software application to serve as the front-end interface for the model. Both can be developed by taking the agile development route, starting with building a prototype or MVP and tweaking it afterwards by analyzing large amounts of data. 

Here’s the complete roadmap:

1. Defining the Scope of Work

Prepare a scope of work, which will include the vision and a Software Requirement Specification (SRS) document. The vision will focus on the goals and objectives of the model, for example, detecting early signs of cancer in patients, while the SRS will include a list of features to be developed in the software application. The final build of the software will be tested against SRS. Also, while you can prepare a vision statement yourself, the SRS can be developed with the help of a business analyst as well, who can figure out key challenges in the development, market requirements, and conduct market research as well.

2. Collecting Training Data

Before the development of the model, it is imperative to start collecting your training data from various resources. Collecting the training data can be time-consuming and include many hurdles, such as taking research permissions from various authorities or finding the right channels for transferring and accessing data. Here, the training data can include diverse types of EHR reports, lab test reports, medical images, genome data, treatment plans, and more. While handling the data, you will need to comply with statutory privacy compliance as well.

3. Prototyping the Model

After successful data collection, we can proceed with prototype development, which in our case, will also serve as the MVP. For the development part, get in touch with AI developers who can build the model and also train it for you. In most cases, a supervised model is created, for which labelled data is provided to the ML model as a sample, which learns to filter the output by comparing it with the labeled data. The common supervised models that can be used for developing a disease risk detection system are:

• Artificial Neural Network: Mimics the human brain’s neural networks to identify patterns in data.

• Markov’s Chain: Detects the probability of a disease by comparing data with a patient's medical records.

• Logical Regression: Compares raw data with labelled data to predict an answer.

• Gradient Boosted Trees: Combine multiple predictions to give a stronger and more accurate prediction.

4. Training the Model

Refine the model’s preventive analysis over the course of various feedback loops to achieve higher accuracy levels. You will also need to adjust the model for overfitting and underfitting to reduce bias and variance in prediction. This can take multiple rounds with a practical timeline ranging between 12 months and 24 months. To ensure further accuracy, you can work on the model with actual medical practitioners and subject matter experts, who will act as the early adopters for your AI-powered risk-detection system. 

5. Linking with Front-end and Deployment

Link the model to your preferred software solution(s), be it mobile apps, a desktop app, or a kiosk app. Select one market for an early launch and deploy the model on self-hosted or in-house servers. You may also need to give demos, presentations, and onboard medical staff to help them use the model. Along with the model, deploy a feedback system and continue monitoring the system for continuous feedback and improvement. 

Conclusion

Disease risk prediction models are a step ahead in the medical industry, with the primary benefit being the number of lives they can save with early detection. The added benefits include time and cost effectiveness, higher prediction accuracy, and continuous learning. With that being said, developing every AI model requires time and financial investment by visionaries. Fortunately, due to its humanitarian benefits, finding investors and raising funds for developing a disease prediction model is also easy. You can also apply for several government-backed funding programs for your project. In case of assistance, our AI and machine learning experts can guide you.