AI Shows Potential In Early Autism Detection

A research paper published yesterday presented some promising results of a machine learning model designed to identify children at risk of autism spectrum disorder (ASD) at an early age. The model, named AutMedAI, achieved an accuracy rate of 80%, offering hope for early detection.

Developed by researchers at Karolinska Institutet, AutMedAI analyzed data from approximately 30,000 individuals to identify patterns linked to autism. The data was based on 28 parameters that can be easily obtained before a child turns two, such as the age of the first smile, the first short sentence, and the presence of eating difficulties.

In a statement, the study author Shyam Rajagopalan emphasized the significance of these findings: “The results of the study are significant because they show that it is possible to identify individuals who are likely to have autism from relatively limited and readily available information.”

The researchers highlight the potential of this study to screen children at an early age, which could lead to the implementation of timely interventions, helping children with autism develop optimally.

However, the researchers caution that while the findings are promising, the model is not a substitute for comprehensive clinical evaluation. Further research and validation are needed to fully assess the model’s potential for clinical use.

It’s important to note that AI tools can sometimes lead to misdiagnosis with potentially harmful consequences. A recent study found that AI struggled to accurately diagnose pediatric cases, with incorrect diagnoses in 83% of the cases it analyzed.

Additionally, another study published yesterday reports that while AI tools can accurately diagnose genetic diseases based on textbook descriptions, their accuracy drops significantly when analyzing summaries written by patients.

“Generative AI technologies have the potential to improve health care, but only if those who develop, regulate and use these technologies identify and fully account for the associated risks,” said Jeremy Farrar, the WHO’s chief scientist, as reported by Nature.