Artificial intelligence, or AI, is a broad term for computer systems that can perform tasks that usually require human thinking, such as recognizing patterns, organizing information, and supporting decisions. Machine learning is one of the main approaches within AI. It allows computers to learn from data and improve over time, rather than simply following a fixed set of instructions.
Artificial intelligence is often described as a technology of the future, but in medicine, it is already becoming a practical support tool. It does not replace doctors, pathologists, geneticists, or radiologists. Instead, it can help them work with large amounts of complex information and spot patterns that may otherwise remain hidden.
This is especially important in neuromuscular diseases. These conditions are often difficult to diagnose because symptoms can overlap, and different types of data may tell only part of the story. AI offers a way to bring these pieces together and make them easier to understand.
Looking Beyond a Single Test
Modern medicine no longer relies on a single result. A diagnosis may involve clinical observations, imaging, tissue analysis, and genetic testing. Each of these can reveal something important, but real progress often comes when they are considered together.
This is why researchers increasingly speak not only about genomics, but also about other data-rich fields that help describe disease from different angles. Among the most important are histomics and radiomics.
What Are Histomics and Radiomics?
Histomics focuses on information hidden in tissue images, such as muscle biopsy slides. With digital tools and AI, these images can be studied more systematically, helping specialists describe quantitatively changes in muscle structure, cellular patterns, and signs of damage or repair.
Radiomics works similarly, but with medical scans such as CT and MRI. Images that look familiar to the human eye may also contain subtle details that computers can help organize and interpret. This can support a deeper understanding of how disease affects muscles over time and how different patients may vary.
Although the tasks are clearly defined, there are still no satisfactory or reliable solutions available, and the COMPAS-NMD project is seeking to develop one.
Why Genetics Still Matters
Genetics remains a key part of the diagnostic journey in rare and neuromuscular diseases. But reading genetic data is not always straightforward. Many changes in DNA may be found, and not all of them are clearly linked to disease.
This is where machine learning can help. It can assist researchers in finding patterns and focusing on the variants that matter. One group of methods used in this area is called RVAT (Rare Variant Association Testing).
This method looks at whether rare genetic changes, especially when considered together, may be linked to a disease. Other methods investigate the gene-level modifications by combining all the changes found in a gene into one number called a “gene score”. No matter which approach is used, these techniques can help researchers go from single findings to a bigger picture of how the body works. It also allows using the well-known functional genomics solution to identify the most affected signalling pathways and provides a more overall view of the disease, which hopefully will improve how diseases are diagnosed and treated. COMPASS-NMD teams focus on that, developing the new GORDIAN scoring system.
What This Means for Patients
For patients and families, the value of AI is not in technology itself. The value lies in what it may improve: a clearer diagnostic path, better use of available data, and a more connected view of the disease.
AI can help turn scattered information into a more coherent picture. When tissue data, imaging, and genetics are studied together, the chances of understanding a complex disorder may improve. In that sense, AI is not just about automation. It is about making medical knowledge more connected, more usable, and more supportive of real clinical decisions.
