Written by Shrey Mehta '26
Edited by Yuliya Velhan ‘25
Introduction
Artificial Intelligence will take over mankind.
As the strength of computer science and human-computer interface technology exponentially increases, a large proportion of the general public appears to respond similarly: a slight fear for the potential “artificial intelligence domination.”
While it may take many years of research and development to completely diffuse this public hesitance, artificial intelligence is already beginning to form a symbiotic relationship with the biomedical field, especially in the improvement of diagnosis of challenging neurological diseases [1].
The Urgency For Early Diagnosis
Neurological disorders are among the most difficult and intricate diseases to diagnose, let alone treat. Unfortunately, researchers and medical professionals struggle the most in the stage of disease detection, because an individual’s condition may not be identified until the disease’s pathology has already become irreversible. Examples of these degenerative brain diseases include Alzheimer’s Disease, Parkinson’s Disease, Huntington’s disease, Multiple sclerosis, and many more [2].
Studies spanning across many years have shown that time truly is of essence in the diseases mentioned above. Although the manifestation of the disease may not appear severe initially, research shows that these conditions progress much faster as compared to a linear fashion (for example, protein build-up in a diseased brain can be modeled exponentially). Eventually, as the various hallmarks of neurological disease (unwanted proteins, disconnected neurons, cell death, etc) increase, cognitive decline ensues, making it very difficult for a patient to execute everyday tasks. Evidently, early diagnosis is key in preventing severe decline in patients suffering from brain disorders. However, current diagnostic and brain imaging tools are limited in their ability to accurately detect disease progression before the critical time window is lost [2].
This is where artificial intelligence comes into play.
The Strong Intersection Between Technology and Neuroscience
Today, computer science and artificial intelligence have become significantly more intertwined with the biomedical tools that aid medical professionals. As medical imaging technology becomes more refined, especially those for brain scans, more and more data is outputted for analysis: analysis that would take way too long is completed manually. This could range from gene expression studies in brain regions to brain wave analyses [3].
Probing these massive datasets provides lots of important insight into how the brain’s structure and function change over the progression of a disease. In order to reduce the amount of time it takes to comb through hundreds of numbers and brain images, artificial intelligence and machine learning can be implemented, a prime example being developed at Georgia State University. Researchers specializing in this scientific intersection are working on creating an AI-based model that can analyze brain imaging data and detect pathological markers and subtle changes in a patient’s status [4]. By integrating the changes and markers detected by the models, doctors can more accurately predict the likelihood of an individual developing a severe cognitive disorder.
With an adapting model that can be calibrated to learn about a specific patient’s data, neuroscientists are moving closer to reliable early diagnoses so treatment plans can be implemented before it’s too late.
Conclusion
Due to the growth of human-computer interface such as pathology modeling, perhaps society can become more accepting of the technology. Rather than fearing the potential of artificial intelligence, several disciplines must harness its strengths safely and effectively to better the lives of all.
References
[1] Augmenting neurological disorder prediction and rehabilitation using artificial intelligence. ScienceDirect. (n.d.). Retrieved December 12, 2022, from https://www.sciencedirect.com/book/9780323900379/augmenting-neurological-disorder-prediction-and-rehabilitation-using-artificial-intelligence#:~:text=AI%20technologies%2C%20such%20as%20machine,modifying%20the%20line%20of%20treatment
[2] Segato, A., Marzullo, A., Calimeri, F., & De Momi, E. (2020, October 13). Artificial Intelligence for Brain Diseases: A systematic review. APL bioengineering. Retrieved December 12, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556883/
[3] Ridler, C. (2018, August 30). Artificial intelligence accelerates detection of neurological illness. Nature News. Retrieved December 12, 2022, from https://www.nature.com/articles/s41582-018-0066-z
[4] H;, R. U. A. U. R. A. (n.d.). Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology. Retrieved December 12, 2022, from https://pubmed.ncbi.nlm.nih.gov/31743905/
[5] Alex Ouyang | Abdul Latif Jameel Clinic for Machine Learning in Health. (n.d.). Artificial Intelligence model can detect parkinson's from breathing patterns. MIT News | Massachusetts Institute of Technology. Retrieved December 12, 2022, from https://news.mit.edu/2022/artificial-intelligence-can-detect-parkinsons-from-breathing-patterns-0822