3 Breakthrough Ways AI is Transforming Medical Care

Understanding how artificial intelligence and other cutting-edge technologies are advancing modern medicine is a great way to deepen your ability to spot trends. Here's what you need to know. As AI use becomes more common in the medical field, it continues to play a crucial role in enhancing today's treatments.

Artificial Intelligence systems now span the gamut of the medical market from tracking patient data, all the way to recommending prescriptions, and assisting in surgeries. All of this progress is just a drop in the bucket compared to what the future holds. Next-generation AI systems will improve accuracy, minimize research times, and reduce side effects.

Keenly, advanced AI algorithms empower doctors with better tools to make a difference. These systems have already helped to reduce experimentation costs, minimize human error, and diagnose hard-to-treat and time-consuming diseases without human intervention. Here are the most common uses of AI in the medical field today.

• Prevention: Artificial intelligence is particularly helpful in diagnostics. These systems can be set up to recognize patterns and connections that humans would never notice. As such, AI systems are increasingly being integrated into medical systems as a way to determine early signs of disease in medical images, patients, and other vital data.
• Drug Development: Drug discovery is another key area where you find AI doing some heavy lifting. There are AI systems in use today that enable drug manufacturers to simulate human reactions without the need to use a real patient. These systems can simulate millions of scenarios, drastically reducing drug development times, side effects, and optimizing drug design.
• Tailored Treatment Plans: Many healthcare providers have turned towards AI to improve their overall treatment plans. These systems can help in the creation, execution, monitoring, and personalizing of medical treatments. Consequently, many believe that future AI systems will be able to diagnose, research, prescribe, and even create medicines on site. Here are 3 new ways that AI is advancing modern medicine today.

AI Algorithm Maps Tumors for Cancer Treatment

Northwestern Medicine engineers succeeded in creating an intuitive AI system that can accurately map tumors. Mapping tumors is a crucial part of radiation therapy, which is the most popular way in which people combat cancer. Notably, half of all cancer patients in the US receive radiation treatment.

The system helps to solve the issue of tumor segmentation. This manual process is time-consuming and leads to delays, inconsistencies, and varying accuracy based on the professional conducting the tests. Given the dangerous nature of cancer, these issues can result in death for the patient.

Cancer Stats

Cancer remains a leading cause of death among the population, and sadly, all statistics show that it's on the rise. Experts predict there will be +2M new cancer patients this year. Even worse, +600K will die due to their complications.

Recognizing a need for a more accurate and reliable system, Northwestern Medicine scientists released the paper¹ titled “Deep Learning study for automated, motion-resolved tumor segmentation in radiotherapy.” This research introduces a high-performance AI tool named iSeg that enhances tumor detection. The system could lead to earlier diagnosis and even standardized tumor segmentation globally, eliminating the variations that made diagnosing so difficult.

Training Mapping Tumors Algorithm

The AI system was trained using thousands of CT scans collected from multiple medical facilities. Interestingly, doctors drew tumor outlines around the affected area to enhance the system's identification capabilities. Two hospitals, the prestigious Northwestern Medicine and the Cleveland Clinic, participated in the study by providing scans for the AI data set.

Source – Northwestern Medicine

iSeg is unique in that it's the first AI-powered 3D deep learning protocol dedicated to finding cancerous tumors. The system utilizes 3D scanning to segment tumors and track their actions during each breath, making it easier to spot them before they grow larger.

Mapping Tumors Algorithm Study Benefits

This AI system could lead to an automated discovery system, as the AI proved to be highly efficient. It was capable of matching and exceeding doctor-provided diagnoses. Now, the team seeks to utilize this data-driven approach to improve treatment processes and increase early warning detection.

Using AI to Sniff Out Parkinson's Disease

Another recent example of how AI is advancing modern medicine comes from researchers in ACS’ Analytical Chemistry department. This team has developed an AI algorithm that detects volatile organic compounds (VOCs) in your earwax to determine Parkinson's. The new approach is non-invasive and cost-effective, leading many to see it as a major milestone in the fight against Parkinson's Disease.

Traditional Methods of Detecting Parkinson's

The new system enables doctors to sniff out Parkinson's much earlier than traditional methods rely on monitoring sebum on the skin. The traditional method is easily compromised when the skin becomes exposed to pollutants, humidity, or other contaminants. Additionally, this process is expensive, leading to financial barriers for patients in need.

AI Parkinson's Disease Study

The An Artificial Intelligence Olfactory-Based Diagnostic Model for Parkinson’s Disease Using Volatile Organic Compounds from Ear Canal Secretions study² describes how scientists were able to train an AI system to monitor four crucial biomarkers to streamline Parkinson's diagnosis. Specifically, the team determined that ethylbenzene, 4-ethyltoluene, pentanal, and 2-pentadecyl-1,3-dioxolane were accurate biomarkers for the disease's presence.

Parkinson's Disease

Parkinson's disease is a nervous system disorder that can affect a person's ability to perform basic motor skills. The disease ravages those affected by it. Within a few years, most patients will suffer from a wider array of neurological issues. Sadly, +8.5M people suffer from this disease today.

Training an AI Parkinson's Disease Algorithm – Advancing Modern Medicine

The team gathered a high volume of earwax VOC data to train the AI dataset. They acquired this data from 209 human test subjects. Interestingly, only 108 of the patients suffered from Parkinson's disease. This approach enabled engineers to provide the AI system with a way to cross-reference healthy vs.Parkinson's affected areas.

AI Parkinson's Disease Study Benefits

There are several benefits to this early warning Parkinson's disease detection system. For one, it will help to save the lives of hundreds of thousands of people who don’t know they have this ailment, as treatment options are limited. Notably, this early non-invasive detection method is less expensive than alternatives and much easier to access.

In the future, these systems could provide reliable results in minutes and help to create a standardized and autonomous diagnosis strategy, reducing costs and improving accessibility.

Using AI to Fight Fatty Liver Disease

Researchers from Osaka Metropolitan University’s Graduate School of Medicine have trained and tested a new AI algorithm designed specifically to detect fatty liver disease. This extremely common ailment affects nearly a quarter of the world population and can lead to serious health complications such as liver cancer.

Current Methods to Detect Fatty Liver Disease

The current way that healthcare professionals diagnose fatty liver disease is via ultrasounds, CTs, and MRIs. CTs and MRIs are extremely expensive procedures, and ultrasound requires a professional capable of accurately deciphering what the sound waves display.

Problems with Today's Fatty Liver Disease Diagnosis Options

Sadly, these methods create cost barriers and time constraints for patients. For one, they require specialized locations. You won't find an MRI machine at your local doctor's office. These devices can cost +$100K and often need to be placed in specially built facilities, adding to their overall costs and other factors that limit patient access.

AI Fatty Liver Disease Study

Thankfully, a team of scientists from Osaka Metropolitan University’s Graduate School of Medicine has created an enhanced method to achieve radiology interpretation. Their paper³ Performance of a Chest Radiograph–based Deep Learning Model for Detecting Hepatic Steatosis introduces a novel method of utilizing chest X-rays to find fatty liver disease.

The system can accomplish this task by registering bio markers in the chest only found when patients are suffering from fatty liver disease. This approach reduces diagnosis costs and enables health care professionals to conduct multiple diagnoses at the same time.

Training Fatty Liver Disease AI – Advancing Modern Medicine

To train their AI system, the team created a dataset that included 6,599 chest X-ray images from 4,414 patients. These patients were x-rayed, and the healthy patients' scans were compared to those suffering from fatty liver disease. This information helps the team to create controlled attenuation parameter (CAP) scores, furthering accuracy.

Fatty Liver Disease Study Benefits

The Fatty Liver Disease AI algorithm enables medical professionals to conduct additional diagnoses on patients without requiring extra steps. Notably, there are already thousands of patients who have had x-rays of their chest. Consequently, these individuals provided everything they needed to ensure they didn’t have fatty liver disease without even knowing it.

AI – Advancing Modern Medicine Through Efficiency and Innovation

When you examine the effects of AI systems in modern medicine, it's easy to see that the market is set to enter into a new age of treatment effectiveness and usability. As AI systems become more common and interwoven into the medical field, support for AI-integrated medical options will rise. All of these factors highlight how AI has the potential to revolutionize the medical field and much more.

Learn about other AI advancements here

Studies Referenced:

<sup>1. Sarkar, S., Teo, P.T. & Abazeed, M.E. Deep learning for automated, motion-resolved tumor segmentation in radiotherapy. npj Precis. Onc. 9, 173 (2025). https://doi.org/10.1038/s41698-025-00970-1
2. Sun, C., Zhu, Y., Wang, Q., Zeng, Y., Yu, Y., & Zhang, W. (2025). An artificial intelligence olfactory-based diagnostic model for Parkinson’s disease using volatile organic compounds from ear canal secretions. Analytical Chemistry, 97(23), 8230–8237. https://doi.org/10.1021/acs.analchem.5c00908
3. Matsuo, H., Matsumura, T., Inoue, Y., Tanaka, R., Ito, T., & Tatsumi, M. (2024). Performance of a chest radiograph–based deep learning model for detecting hepatic steatosis. Radiology: Cardiothoracic Imaging, 6(3), e240402. https://doi.org/10.1148/ryct.240402</sup>