Table 3. Cancer diagnosis and monitoring, identifying at-risk populations, classifying genetic variations, and predicting patient ancestry are examples of the diverse and impactful applications of genetics and AI in healthcare and genetics.

AI, artificial intelligence

Applications	How AI is applied	Impact
Cancer diagnosis and monitoring	Genomic data can be analyzed by machine learning models to find patterns linked to cancer. These models can help in cancer recurrence probability prediction, subtype categorization, and early diagnosis.	AI-assisted early diagnosis and monitoring lead to more individualized and efficient treatment plans, which enhance patient outcomes.
Identifying at-risk populations	Large-scale genetic databases can be analyzed by AI algorithms to determine which people are more susceptible to a given disease, such as inherited disorders or complicated disease susceptibility.	Public health initiatives can be strengthened by implementing screening programs, preventive measures, and targeted interventions for populations that are at risk.
Classifying genetic variations	Genetic variants can be categorized and interpreted by machine learning algorithms, which can differentiate between potentially hazardous and benign mutations. Understanding the genetic foundation of diseases requires knowledge of this.	Precise categorization of genetic variants facilitates the diagnosis of hereditary illnesses, directs therapeutic choices, and expands our comprehension of the genetic foundations of ailments.
Predicting ancestry of a patient	AI systems are able to predict an individual's ancestral ancestry by analyzing genetic markers. To do this, the genetic profile is compared to reference datasets made up of various demographic groups.	Because various genetic variants and susceptibilities might be associated with particular populations, ancestry prediction holds potential implications in personalized medicine. It also helps with customized healthcare planning.