Table 4.

Learning elements for which no consensus was achieved organized by core theme

Theme	Elements not reaching consensus	% Consensus to include or exclude
Legal
L12	List the key issues surrounding the intellectual property of AI.	54% in favor of inclusion.
L13	Analyze the implications of intellectual property issues related to the use of AI in healthcare.	69% in favor of inclusion.
L14	Apply appropriate strategies to protect and manage copyright issues when using AI in healthcare.	54% in favor of inclusion.
Theory
T33	Develop skills in programming languages commonly used in healthcare, such as Python and R.	62% in favor of exclusion.
T34	Identify and differentiate between different types of deep learning, including convolutional neural networks and recurrent neural networks.	54% in favor of inclusion.
T35	Identify and differentiate between different types of models in deep learning, including autoencoders and generative adversarial networks.	54% in favor of exclusion.
Application
A21	Standardize data to ensure consistency and comparability for AI research purposes.	69% in favor of inclusion.
A22	Develop and implement AI models for research purposes.	54% in favor of exclusion.
A23	Train AI models using appropriate techniques and algorithms, and fine-tune them as needed.	69% in favor of inclusion.
A24	Perform dimensionality reduction techniques such as PCA for feature selection and visualization.	62% in favor of exclusion.
A25	Use Keras to build and train deep learning models.	69% in favor of exclusion.
A26	Perform hyperparameter tuning to optimize model performance.	62% in favor of exclusion.