AI Supports Neurosurgeons in Improving Patient Outcomes
August is Neurosurgery Awareness Month, a time to spotlight the complex medical specialty focused on diagnosing and treating patients with injury to or diseases/disorders of the central nervous system. This week’s Domain Knowledge highlights AI developments that are poised to aid neurosurgeons in improving patient outcomes.
Neurosurgeons are among the most highly trained doctors, with a minimum of 15 years of education and training to equip them with the required knowledge and technical skills. Globally, 13.8 million patients undergo neurosurgical procedures yearly, while another 5 million individuals with neurological conditions go untreated. An additional 23,000 neurosurgeons are needed to close the gap between required and current neurosurgeries being performed today. Artificial intelligence (AI) continues to evolve and is proving helpful to neurosurgeons, potentially closing this care gap. There are several advantages to the use of AI as a counterpart to neurosurgeons, including:
- Pre-Op/Diagnosis: AI can better discern subtle abnormalities and be more reliable in interpreting radiological images.
- Surgical Care: AI can perform 24/7 and does not face the same fatigue as human providers.
- Research: Through machine learning, AI has the greater capacity to learn and identify patterns that can be used to reduce medical errors.
- Post-Op Follow-up/Prognosis: Improved prognosis can be achieved by utilizing AI to reduce variations in patient care and providing guidelines on surgical interventions.
Heidelberg University Hospital and the German Cancer Research Center conducted a real-world example of AI in Neurosurgery specific to neuro-oncology. Five hundred MRI scans from brain tumor patients were reviewed, and the AI algorithm automatically detected and localized the tumors. This use case is valuable in diagnosing, treating, and defining the prognosis of brain tumors.
In addition, AI solutions are being evaluated to help diagnose and assess Parkinson’s, Alzheimer’s, and Amyotrophic Lateral Sclerosis (ALS). Applying AI to a wide range of diagnostic tests, including speech recordings, cognitive test scores, and radiography images can assist in the continual treatment of complex neurological conditions.
That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.
If you have AI tips, suggestions, or resources you’d like to share, leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.