Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

Abstract

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

Keywords: artificial intelligence; disease sequelae; gynaecology; machine learning; mental health; obstetrics; women’s health.

Figure 1.

A schematic representation of the classification of AI-based methods

Figure 2.

Multi-faceted application on AI in healthcare

Figure 3.

A schematic representation of multiple ML algorithms, which are a subset of AI methods that are commonly used in the development of healthcare AI applications. This hierarchy of ML algorithms is composed of three primary techniques of supervised, unsupervised and reinforcement learning. Supervised and unsupervised techniques are primary categories that use classification and regression models that could focus on qualitative and quantitative data sets, respectively, to provide clear outputs.

Figure 4.

The non-linear SVM classifier with the kernel trick.

Figure 5.

A representation of the ANN with a 16-dimensional input layer and two hidden layers; each one with 12 and 10 neurons. Each of the two hidden layers may represent a specific type of features that need to be detected. The interaction between two nodes is represented by coloured edges, where positive interaction is shown by red and negative interaction is shown by blue. The edge width and edge opacity are proportional to edge weights.

Figure 6.

Treating multi-morbid conditions: traditional approach (left) versus AI-supported integrated approach (right).