Artificial Intelligence (AI)
This specific subset of AI focuses on models that learn from examples, which data can be either labeled or unlabeled
Machine Learning
This type of big data analytics use reports, visualizations, and dashboard to summarizes raw data to answer "What happened?"
Descriptive Analytics
This system is recommended for establishing a foundational digital infrastructure in hospitals for AI adoption.
Electronic Health Record system
This learning model narrows the performance gap between its predictions, based on the training data, and the expected outcomes by utilizing labeled data for training.
Supervised Learning Model
This type of analytics uses statistical models and machine learning algorithms to identify patterns that may predict outcomes to answer "What will happen?"
Predictive Analytics
Maintaining this is crucial in to ensure patient data is protected.
Data privacy and security
This type of Machine Learning Model uses Artificial Neural Networks #(ie., the banks use this model to detect fraud)
Deep Learning Model
This analytics helps healthcare providers make informed decisions to achieve optimal outcomes answering the question "How can we make it happen?"
Prescriptive Analysis
This issue occurs when data collected varies widely across different sources and formats
Data Heterogeneity
This model is used in the hospitals to "fine-tune" their specific medical data to improve diagnostic accuracy
Large Language Learning Model (LLM)
This challenge relates to a healthcare provider's ability to keep up with evolving practices to protect patient data from unauthorized access and cyber threats
Cybersecurity
Improving this aspect of data to ensure accuracy and reliability for AI analytics
Data Quality
This categories or subcategories of AI structure are where ChatGPT fit within
LLMs and Generative AI
Algorithms may face this issue when they depend on data from specific demographics, leading to results that may not apply universally across populations
Algorithmic Bias
A common issue in AI adoption where users experience exhaustion due to constant alerts.
Alert Fatigue