A team of Australian researchers recently published a research review analysing the potential of artificial intelligence (AI) to impact ME/CFS research and healthcare. AI plays a crucial role in precision medicine – an approach which aims to develop personalised treatments by analysing large datasets, including aspects such as genetic profiles, medical history, and social factors. Given the complexity and heterogeneity (diverse nature) of ME/CFS, AI-driven techniques offer promising avenues for uncovering biological mechanisms, identifying treatment targets, and predicting individual treatment responses.
In oncology (cancer medicine), precision medicine has improved patient outcomes by enabling targeted therapies for individuals with specific genetic or molecular markers, such as in certain types of breast and lung cancer. A similar data-driven approach could benefit ME/CFS, where evidence suggests abnormalities in areas such as energy metabolism, the immune system, and autonomic nervous system.
Machine learning, a subset of AI, detects patterns in large datasets that might be overlooked by the human eye. AI is being used to help ME/CFS researchers analyse diverse biological data, with the goal of identifying biomarkers, moving beyond a symptom-based diagnosis, and identifying potential treatment targets.
However, AI’s effectiveness depends on the quality of the data it processes. ME/CFS research is often hindered by small sample sizes, inconsistent study designs, and differences in data interpretation across studies. Many of these issues are likely a result of inadequate research funding . Despite the use of AI in ME/CFS research becoming more sophisticated, there is a need for high-quality data and clearly defined objectives to allow for optimal results.
The researchers suggest that the reliability of ME/CFS research should be improved through better handling of data, greater biobank resources, more transparent research reporting, and improved research collaboration. These steps will aid in a shift from exploratory studies to more solid research, making it easier to identify useful data patterns. Ultimately, AI-driven models could lead to better clinical decision-making and more personalised treatments for ME/CFS.
Read about brain scans and artificial intelligence in ME/CFS
