AI in Brain Injury Recovery
- voicesofbraininjur
- May 15
- 2 min read
Introduction
Artificial Intelligence (AI) is rapidly changing the medical industry, particularly in brain injury recovery. In the past decade, the rise of AI has influenced diagnostic predictions, treatment, recovery, and drug development.
Enhanced Diagnostics
Data analyzed in brain injury cases are complex and vast, making rapid decision-making difficult (Rajaei, 2023). Brains are also heterogeneous, meaning each part of the brain has a distinct makeup and performs its own functions. This leads to challenges in creating predictable and accurate models. Computer-aided and machine systems are said to be able to play a vital role in speeding up the decision-making process by quickly processing large amounts of data to
diagnose and recognize clinical symptoms efficiently.
Personalized Treatments
AI can also provide unique and novel insights into the specifics of a brain injury and identify any meaningful or specific parameters related to a case (Myers, 2023). This can help researchers analyze imaging scans (e.g., MRI, CT) to detect patterns of injury and predict where the trauma from the brain injury is originating. Then, they can design specific protective equipment and treatments. AI’s additional ability to analyze extensive amounts of data can tailor a treatment plan according to a patient's history, symptoms and evolving progress to enhance recovery
(2024). Mentally, chatbots can offer patients an engaging path to recovery by serving as emotional support and monitoring their mood to prevent PTSD and depression.
Research and Drug Development
As mentioned earlier, each part of the brain is unique and performs different functions so not only can finding a model be tricky for diagnosing but it also presents challenges for researching. Models are used to determine specific stresses on the brain, but different variants of models mean that several versions must be tested to find one that is effective for a specific stressor. AI can quickly test models, of which there are more than 4,000 possibilities, to see which works
best (Myers, 2023).
Challenges
However, using AI to improve brain injury recovery can also have some challenges. An over-dependence on AI can push a person to follow a recommendation without using underlying logic to ensure no compromising technological errors occur (Adegbesan). Further, ethically, when errors occur, many question: is the doctor accountable or was it AI’s fault? Researchers address this issue by emphasizing that while the introduction of AI into brain injury recovery can result in massive improvements, it must only be used to enhance human-driven recovery rather
than acting as its substitute.
References
Adegbesan, A., Akingbola, A., Aremu, O., Adewole, O., Amamdikwa, J. C., & Shagaya, U.
(2024). From scalpels to algorithms: The risk of dependence on artificial intelligence in
surgery. Journal of Medicine, Surgery, and Public Health, 3, 100140.
Harnessing AI for Enhanced Rehabilitation and Support in Traumatic Brain Injury Care. (2024,
January 22). TBI. Retrieved February 4, 2025, from
Myers, A. (2023, March 1). AI offers 'paradigm shift' in Stanford study of brain injury. Stanford
University. Retrieved February 4, 2025, from
Rajaei, F. (2023). AI-Based Decision Support System for Traumatic Brain Injury: A Survey.
National Library of Medicine. Retrieved February 4, 2025, from
