The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Relatively high milk-producing cows in Morocco are culled at an elevated rate due to poor fertility. This situation is due to genetic, environmental, and management-related factors. To remedy this situation or prevent an additional decline in reproductive performance, using locally produced replacement heifers with good fertility and yield traits was proposed as a solution. Identifying these animals requires estimating the genetic parameters of the relevant reproduction and production traits in order to develop a genetic evaluation. Three reproduction traits (number of inseminations per conception, success of first insemination, and days open) and 305-d milk yield were used in this study. Two datasets of 4,186 records (first parity cows) and 5,511 records (first and multi-parity cows) were used. The pedigree files for both datasets consisted of 8,758 and 9,935 animals, respectively. A threshold-linear model was used for the analyses. For the first parity, estimates of heritability for 305-day milk yield (MY), days open (DO), number of inseminations per conception (NIC), and success of first insemination (SFI) were 0.26 ± 0.04, 0.17 ± 0.04, 0.10 ± 0.03, and 0.10 ± 0.04, respectively. For multi-parity data, the estimates were 0.19 ± 0.03, 0.12 ± 0.02, 0.10 ± 0.02, and 0.09 ± 0.02 for MY, DO, NIC, and SFI, respectively. The genetic correlations between MY and reproduction traits were 0.15 ± 0.11, 0.38 ± 0.12, and -0.43 ± 0.11 for DO, NIC, and SFI respectively. Overall, the heritability estimates of fertility traits were low. The genetic correlations of DO, NIC, and SFI with MY were moderately negative allowing for further selection for milk production without an additional deterioration in reproductive performance. The relative impact of using high fertility bulls compared to low fertility bulls on the success of first insemination ranged between 1.2% and 6.3% depending on the production environment. Collectively, these results confirm the possibility of implementing a viable selection program based on an appropriate weighted selection index.

Reproductive efficiency is a key element of profitability in dairy herds. However, the genetic evaluation of fertility traits is often challenged by the presence of high censorship rates due to various reasons. An easy approach to address this challenge is to remove the censored data from the dataset. However, removing data might bias the genetic evaluation. Therefore, addressing this issue is crucial, particularly for small populations and populations with limited size. This study uses a Moroccan Holstein dataset to compare two Gaussian linear models and a threshold linear model to handle censored records of days open (DO). Data contained 8646 records of days open across the first three parities of 6337 Holstein cows. The pedigree file comprised 11,555 animals and 14.51% of the dataset was censored. The genetic parameters and breeding values of DO were computed using three different methods: a linear model where all censored records were omitted (LM), a penalty method in which a constant equal to one estrus cycle in cattle was added to the maximum value of DO in each contemporary group to impute the censored records (PLM), and a bivariate threshold model with a penalty (PTM). The heritability estimates were equal to 0.021 ± 0.01 (PLM), 0.029 ± 0.01 (LM), and 0.033 ± 0.01 (PTM). The penalty method and the threshold linear model with a penalty showed better prediction accuracy calculated using the LR method (0.21, and 0.20, respectively). PLM and PTM had a high Spearman correlation (0.99) between the estimated breeding values of the validation dataset, which explains the high percentage of common animals in the top 20% of selected animals. The lack of changes in the ranking of animals between PLM and PTM suggests that both methods can be used to address censored data in this population.