THE RISE OF AI IN HEALTHCARE: Practical Applications and Real-Life Examples

Artificial Intelligence is all the rage now and the healthcare industry is no exception. Large language models, neural networks etc are increasingly solving complex medical problems in efficient and remarkable ways. In this blog post, i share insights on a few practical examples currently in use. The list is endless and the post will be periodically updated to include more.

AI In Breast Cancer Screening

One of the key breakthroughs in AI application is how we can use these models to detect diseases earlier before they wreak havoc. There are many examples. Google Health has developed currently an AI model that can detect breast cancer in mammograms without the input of a radiologist and with greater accuracy in many cases than humans would. The idea is to eliminate human error to the barest minimum and reduce false positives and negatives. It is an amazing example of what is to come. This, their yet -to- be-publicly- released Med PALM-1 and 2 models, Medical Gemini and others are a powerful statement that Google is serious about securing its position as a health tech industry leader.

How it works:

It uses pattern recognition and image analysis. The AI model is trained on thousands of mammogram images and uses deep learning algorithms to recognize patterns and anomalies associated with breast cancer. Sometimes, it can detect patterns that are not immediately visible to the human eye. Radiologists who integrate this model into their practice and workflow will realise the enormous ease it presents to their job, reading mammograms and generating reports.

IBM Watson for Oncology

IBM The tech giant has launched many AI tools for Healthcare unto the market. This particular one , IBM Watson for Oncology helps improve the work efficiency of oncologists by analyzing large amounts of medical literature, clinical guidelines and patient data and using them to create personalized treatment plans for their patients. Their chatbot, IBM Watsonx AI can also help free up these physicians' time by answering clients questions or follow ups.

How it works

IBM Watson uses natural language processing (NLP) to break down and analyze unstructured data from medical records, research papers and clinical trials. This data is then compared with a patient's electronic medical records and current test results after which it can suggest treatment plans that are most likely to be effective and improve patient outcomes by reducing the trial-and-error approach that currently plagues how we treat cancer.

AI in Diabetes Management with One Drop

Diabetes presents serious public health concerns as an estimated 1 in 10 people worldwide live with the condition and the date suggests this number will keep growing. One Drop is a digital health company that uses AI to help manage chronic diseases specifically diabetes. They have developed a mobile app and what it does it to track blood sugar levels, food intake and physical activity. This provides users with recommendations and insights that have been personalized for them. It's quite similar to the Hybrid Closed Loop Insulin diabetes management system i covered in a previous video.

How it works:

The One Drop app uses machine learning algorithms to analyze the data users feed it. It then predicts blood sugar levels based on patterns it learns from this data including that of food intake and recorded physical activity. This real-time feedback helps patients to manage their condition more effectively, and reduce the risk of developing complications. Knowing that diabetes presents multi organ system complications and can shorten one's lifespan or reduce the quality of life, such developments which increases patient autonomy in the management of their conditions must be welcomed.

Sepsis Prediction with Epic's Deterioration Index

Sepsis is a serious condition that requires immediate medical attention. This is where Epic comes in. Epic is a software company which developed the Deterioration Index (DTI), an AI tool that predicts the likelihood of a patient developing sepsis in clinical settings. Their Deterioration Index is a proprietary machine learning model that has been adopted across hundreds of hospitals in the United States since it was first released in 2007. It is literally a prognosis tool.

How it works:

DTI uses an ordinal logistic regression model to analyze real-time data from electronic health records including vital signs, lab results, and patient history which is updated every 15 minutes and assign a score between 0-100, indicating the risk level. By continuously monitoring these parameters, the model can alert doctors and nurses to patients at risk of sepsis, allowing for timely life saving interventions.

AI-Powered Mental Health Chatbot

AI Chatbots are all over the place now. This particular one, Woebot is designed to provide mental health support. It is a platform which engages users in conversations and offer them cognitive-behavioral therapy (CBT) techniques and coping strategies for managing their stress, anxiety, and depression.

How it works:

Woebot uses natural language processing to understand the inputs from users. Think of it as something like chatGPT but a more specialized one with a particular focus on mental health. It is able to pick emotional cues from the text input from users and provide the right feedback and suggestions. This can be a good companion for especially lonely people suffering from depression or as a supplement to traditional therapy for those whop might not have easy access.

BenevolentAI in Drug Discovery

BenevolentAI uses AI to help discover new drugs and improve drug targeting for pharmaceutical companies and scientists. Their platform analyzes existing scientific literature, data from clinical trials and proprietary data to identify potential drug candidates and simulate how effective they can be.

How it works:

BenevolentAI uses machine learning models to extract biomedical entities such as genes, diseases, drugs, processes and cell types, and infer relationships that capture how these entities interact in a human system. By sifting through massive datasets, it can identify new drug targets and suggest novel therapeutic approaches. This capability can significantly shorten the time and reduce the cost of bringing new drugs to market.

The integration of AI in healthcare is surely transforming the industry, offering innovative solutions, improving diagnostic accuracy, personalizing treatment plans, managing chronic diseases and supporting mental health. Patients, physicians, regulators and scientists are all benefiting from these advancements by making life simpler and easier for everyone.

There are countless such models already in operation in various parts of the world as well as those being developed. The speed of advancement and the accuracy of these models are mind blowing. Keep checking back for some of the latest updates.