How AI Is Shaping the Future of Treatment for These 6 Diseases

Artificial Intelligence (AI) is making waves in the healthcare industry, helping researchers and doctors address some of the most challenging health conditions of our time.

From diabetes to cancer, AI is playing an increasingly significant role in diagnosing, managing, and even predicting diseases.

Below, we explore how AI is being used to tackle six major diseases, transforming the way we understand and treat them.

1. Parkinson’s Disease: Sniffing the Disease Early

The Problem Diagnosing Parkinson’s disease is difficult and expensive, often not occurring until motor symptoms appear, which is when significant neurological damage has already happened. Currently, there are no treatments that can stop the disease’s progression—only options to slow the symptoms. How AI Helps

Researchers in China have developed an AI-powered system that uses earwax to detect Parkinson’s disease with up to 91% accuracy.

This system analyzes volatile organic compounds (odors) in earwax through a combination of gas chromatography and machine learning to identify specific scent biomarkers.

The method, known as the "electronic nose," offers a promising, low-cost, and non-invasive screening tool.

Earwax is a preferred testing medium because it is less exposed to environmental factors than skin oils, providing more reliable diagnostic results.

Early detection through this method can enable timely intervention, improving the quality of life for patients. The findings were published in ACS Analytical Chemistry.

2. Diabetes: Decoding Subtypes for Personalized Care

The Problem

Type 2 diabetes is traditionally treated as a single disease, but it actually consists of multiple subtypes with different causes and risks. Current methods to distinguish these subtypes are invasive, expensive, or unavailable for many patients.

How AI Helps

Researchers at Stanford developed an AI algorithm that analyzes data from continuous glucose monitors (CGMs) to identify three common Type 2 diabetes subtypes (e.g., insulin resistance, beta-cell deficiency).

This non-invasive AI system detects unique glucose patterns with about 90% accuracy, far better than traditional metabolic tests. It enables people to classify their diabetes subtype from home and tailor lifestyle or drug therapies early on.

This personalized approach could reduce complications by assigning treatments that better match the underlying metabolic disorder. The research appeared in Nature Biomedical Engineering and was supported by NIH and the American Diabetes Association.

3. Alzheimer’s Disease: Streamlining Research

The Problem

Reviewing complex documents, such as adverse event reports and clinical protocols, traditionally takes several days, causing delays in the approval of important medications, including treatments for Alzheimer's disease.

How AI Helps

To streamline this process, the FDA introduced Elsa, a generative AI tool designed to assist employees—from scientific reviewers to investigators—in working more efficiently. Elsa uses large language model technology to rapidly read, write, and summarize complex documents, allowing tasks that once took two to three days to be completed in just six minutes. Specifically, Elsa helps by:

• Summarizing adverse events to support safety assessments of drugs

• Performing faster drug label comparisons

• Generating code to facilitate database development for research and regulatory applications

• Identifying high-priority inspection targets for enforcement and oversight
  

By accelerating clinical protocol reviews and scientific evaluations, Elsa promises to shorten the timeline for evaluating Alzheimer’s treatments and other therapies.

This improvement can help bring effective drugs to patients faster, furthering precision medicine efforts and allowing for treatments tailored to specific patient subgroups.

4. Heart Disease: Mayo Clinic’s AI Advances for Early Detection and Care

The Problem

Heart disease, one of the leading causes of death worldwide, can often go undiagnosed until it becomes severe.

Conditions like left ventricular dysfunction, atrial fibrillation, and stroke are sometimes detected too late, preventing timely intervention and affecting patient outcomes. Accurate and timely diagnosis is crucial, but traditional methods can be slow and may miss subtle patterns in data.

How AI Helps

Mayo Clinic has integrated AI technology to significantly improve the diagnosis and treatment of heart disease. Their AI tools help detect conditions like left ventricular dysfunction with 93% accuracy, surpassing even the accuracy of mammograms.

This AI system is also integrated into devices like the Apple Watch to continuously monitor heart function in real-time, allowing for constant and convenient monitoring.

AI also speeds up stroke diagnosis by analyzing CT scans faster, and it identifies irregular heart rhythms like atrial fibrillation early through ECGs.

By training neural networks on millions of ECGs and patient data, Mayo Clinic’s AI systems are capable of detecting subtle patterns that might be overlooked by human clinicians.

These innovations not only lead to earlier diagnoses and treatment, improving patient outcomes, but also allow physicians to spend more time focusing on care rather than time-consuming diagnostic processes.

5.  Cancer: Unlocking Biomarkers and Personalized Treatment

The Problem

Cancer diagnosis and treatment are difficult due to the differences between tumors in various patients and cancer types. Current AI tools often focus on specific cancers or diagnostic tasks, limiting their ability to apply across different types. Additionally, predicting how a patient will respond to treatment has been challenging, especially when trying to create personalized treatment plans.

How AI Helps

Scientists at Harvard Medical School developed an advanced AI model called CHIEF (Clinical Histopathology Imaging Evaluation Foundation).

This AI can analyze digital pathology slides and perform diagnostic tasks across 19 cancer types. It can identify cancer cells, predict tumor characteristics, forecast patient survival, and even spot features in the tumor microenvironment that relate to how the cancer will respond to treatment.

CHIEF is highly accurate, outperforming current AI models by up to 36%. It has shown nearly 94% accuracy in detecting cancers and can predict survival rates with precision. In addition, CHIEF discovered new tumor characteristics linked to survival and created heat maps that highlighted important interactions in the tumor and surrounding tissue, which pathologists found clinically significant.

6. AI Improving Early Detection of Inflammatory Arthritis

The Problem

Rheumatic and musculoskeletal diseases, including inflammatory arthritis (IA) often go undiagnosed because their symptoms can be vague, and healthcare systems are overwhelmed with cases.

Without early diagnosis, patients risk permanent joint damage and disability, making timely detection critical.

How AI Helps

RMD-Health, a machine learning system developed by Henley Business School at the University of Reading, is designed to detect inflammatory arthritis and other rheumatic diseases earlier and more accurately.

The system analyzes patient referral data, helping doctors make faster, more precise referrals to specialists. This decision-support system outperforms existing clinical criteria and aims to reduce delays caused by a shortage of rheumatology experts.

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