This study introduces an AI-based framework for stroke diagnosis that merges clinical data and curated imaging data. The system utilizes traditional machine learning and advanced deep learning techniques to tackle dataset imbalances and variability in stroke presentations. Our approach involves rigorous data preprocessing, feature engineering, and ensemble techniques to optimize the predictive performance. Comprehensive evaluations demonstrate that gradient-boosted models outperform in accuracy, while CNNs enhance stroke detection rates. Calibration and threshold optimization are utilized to align predictions with clinical requirements, ensuring diagnostic reliability. This multi-modal framework highlights the capacity of AI to accelerate stroke diagnosis and aid clinical decision making, ultimately enhancing patient outcomes in critical care.

The rapid development of artificial intelligence (AI) this decade has created brand new opportunities for companies to improve and rethink their business processes. When adopting AI solutions such as ChatGPT, it is important that current IT governance practices are adapted for this AI revolution. This paper is a literature review that overviews current AI governance trends and future opportunities. Based on this review, the authors have put forth their recommendations for successfully governing AI in business use cases.