A branch of computer science focused on creating systems that simulate human intelligence, including learning and problem-solving.

Medical conditions affecting a small percentage of the population, often leading to diagnostic challenges and limited treatment options.

A set of rules or instructions designed to solve a specific problem or perform a task, particularly in data processing.

The process of combining data from different sources to provide a unified view, essential for accurate AI modeling.

A subset of AI that enables systems to learn from data and improve their performance over time without explicit programming.

A specialized form of machine learning using neural networks with multiple layers to analyze complex data patterns.

The process of optimizing the parameters of a machine learning model to improve its performance on a given task.

The process of evaluating a model's performance using a separate dataset to ensure its accuracy and reliability.

The study of moral implications and responsibilities associated with the use of AI technologies, especially in healthcare.

The ability of a diagnostic test or algorithm to correctly identify the presence or absence of a disease.

Working together across disciplines to leverage diverse expertise, crucial for innovative healthcare solutions.

The graphical representation of data to identify trends, patterns, and insights, aiding in decision-making.

An early sample or model of a product used to test and validate concepts before full-scale development.

Actual medical data obtained from patients, used to train and validate AI algorithms for diagnostic purposes.

Collaborative research that integrates knowledge and methods from different fields to address complex problems.

A measure of how well an algorithm achieves its intended purpose, often evaluated through accuracy, precision, and recall.

The process of involving individuals or groups with an interest in a project to ensure their perspectives are considered.

The process of correcting or removing inaccurate, incomplete, or irrelevant data from a dataset to improve quality.

Choosing the most appropriate machine learning model for a specific task based on the nature of the data and objectives.

A system where outputs of a process are used as inputs for future iterations, essential for continuous improvement.

The systematic evaluation of an algorithm's performance against predetermined criteria to ensure its effectiveness.

The end results of healthcare practices and interventions, particularly concerning the health status of patients.

The development and implementation of new ideas, services, or products aimed at improving patient care and outcomes.

The scientific investigation aimed at improving knowledge about health and disease, often leading to new treatments.

An AI field that enables computers to interpret and understand visual information from the world, often used in diagnostics.