Since its inception in 1987, in vitro fertilization (IVF) has constituted a significant medical achievement in the field of fertility treatment, offering a viable solution to the challenge of infertility. The continuous evolution of assisted reproductive technology (ART) has brought its relationship with the rapidly developing field of artificial intelligence (AI), in particular with techniques such as machine learning (ML), a rapidly evolving area of specialization. In fact, it is responsible for significant developments in the field of precision medicine, as well as in preventive and predictive medicine. The analysis focuses on a large volume of clinical data and patient characteristics of those who underwent assisted reproduction treatments. Concurrently, the utilization of machine learning algorithms has facilitated the development and implementation of predictive models. The objective is to predict the success of treatments for external fertilization based on processed clinical data. This study encompasses statistical analysis techniques and artificial intelligence algorithms for the correlation of variables, such as patient characteristics, the history of pregnancies, and the clinical and embryological parameters. The analysis and creation of prognostic models were compared with the objective of identifying factors that influence the outcome of IVF treatments. The potential for implementing a predictive model in routine clinical practice was also investigated. The findings revealed trends and factors that warrant attention. Such findings may prompt questions regarding the impact of the patient’s age on treatment efficacy. Moreover, the value of developing a predictive model based entirely on patient data prior to the commencement of treatment was also investigated. This approach to the processing and utilization of clinical data demonstrates the potential for optimization and documentation of therapeutic procedures. The objective is to reduce costs and the emotional burden while increasing the success rate of IVF treatments.

Medical technology has made significant advances in the 21^st century and, at present, medicine makes use of information technology, telecommunications, and state-of-the-art engineering to provide the best possible healthcare services. Electronic sensors provide health practitioners with the ability to constantly monitor their patients’ health, to streamlines a number of medical processes, and to increase patients' access to health services. Mobile phones also empower patients and play a major role in their health's monitoring. The use of cybernetics technology can now help patients overcome even serious disabilities, enabling many disabled patients to live their lives similarly to their non-disabled fellow men through the use of artificial organs and implants. All these advances have paved the way for a more personalized type of healthcare that provides individualized solutions to each patient. Once a number of hurdles are overcome, medical technology will bring forth a new era of more precise and enabling medicine.

The evolutionary conserved Notch signaling pathway functions as a mediator of direct cell–cell communication between neighboring cells during development. Notch plays a crucial role in various fundamental biological processes in a wide range of tissues. Accordingly, the aberrant signaling of this pathway underlies multiple genetic pathologies such as developmental syndromes, congenital disorders, neurodegenerative diseases, and cancer. Over the last two decades, significant data have shown that the Notch signaling pathway displays a significant function in the mature brains of vertebrates and invertebrates beyond neuronal development and specification during embryonic development. Neuronal connection, synaptic plasticity, learning, and memory appear to be regulated by this pathway. Specific mutations in human Notch family proteins have been linked to several neurodegenerative diseases including Alzheimer’s disease, CADASIL, and ischemic injury. Neurodegenerative diseases are incurable disorders of the central nervous system that cause the progressive degeneration and/or death of brain nerve cells, affecting both mental function and movement (ataxia). There is currently a lot of study being conducted to better understand the molecular mechanisms by which Notch plays an essential role in the mature brain. In this study, an in silico analysis of polymorphisms and mutations in human Notch family members that lead to neurodegenerative diseases was performed in order to investigate the correlations among Notch family proteins and neurodegenerative diseases. Particular emphasis was placed on the study of mutations in the Notch3 protein and the structure analysis of the mutant Notch3 protein that leads to the manifestation of the CADASIL syndrome in order to spot possible conserved mutations and interpret the effect of these mutations in the Notch3 protein structure. Conserved mutations of cysteine residues may be candidate pharmacological targets for the potential therapy of CADASIL syndrome.

Open access peer-reviewed chapter