## 1 Introduction  
In recent literature, mental health has emerged as a topic of paramount concern, with increasing emphasis on its profound impact, especially on young people. There is growing evidence of an upward trend in mental health disorders within this age group, which influences quality of life and contributes to an increase in self-harm incidents and suicide rates. Although there is substantial evidence supporting the effectiveness of psychopharmacological and psychotherapeutic treatments for mental illness, studies show that 60% of individuals with mental illness do not receive any form of treatment. This issue is even more pronounced in developing countries, where 75-80% of serious mental conditions remain untreated [1, 2]. Recently, online platforms for psychological treatments have become an important resource for patients to receive care. Platforms such as BetterHelp (betterhelp.com, accessed on 18 June 2024) and Talkspace (talkspace.com, accessed on 18 June 2024) conduct millions of sessions monthly, becoming one of the main means of accessing psychological treatment. These platforms often use symptom checkers to accurately identify the needs of each user and match them with the most appropriate provider. Symptom checkers are not new; they have been part of applications of artificial intelligence in medicine since the inception of artificial intelligence, with systems such as INTERNIST-I, a pioneering rule-based reasoning system for medical diagnosis [3]. These systems have become increasingly popular, allowing users to enter symptoms and receive information about possible conditions [4; 5]. Research indicates that 80% of people consult online platforms before visiting a healthcare professional [6]. Symptom checking systems can be divided into two broad categories. The first category includes rule-based systems that use predefined rules and logic to derive a diagnosis. Probabilistic systems, such as those based on Bayesian networks, also fall into this category. These methods model conditional probabilities between symptoms, risk factors, and conditions, and use the law of probabilities to derive a diagnosis [3; 7; 8; 9; 10]. The second category consists of models based on machine learning. These models require only raw data or features derived from raw data. The model then learns by itself the important features of the input data and the relationships between the input variables and the output variable, which in this case is a diagnosis [11; 12; 13; 14; 15; 16]. These models have already shown great promise for mental health diagnosis tasks. For a comprehensive review of machine learning models and AI applied to mental health diagnosis, the reader can refer to [17]. Both categories have pros and cons. Rule-based systems have the advantage of being directly linked to proven medical knowledge, as their rules are often defined by medical professionals. They are also more transparent and explainable than machine learning-based models, since the reasoning process leading to a decision can be traced and evaluated by professionals. This facilitates the collaboration between human professionals and AI systems. However, a common limitation of rule-based systems is their diagnostic accuracy. Although these models played an important role in the early development of artificial intelligence, they have been largely replaced by machine learning models, which achieve higher accuracy. Additionally, rule-based systems are difficult to adapt to real raw data, as they typically require input in the form of discrete variable values defined by an expert or provided by the user. On the other hand, machine learning models are generally more accurate than rule-based systems. In addition, they are easier to apply to the kind of unprocessed data found on the Internet and social media, since modern machine learning models can be trained on raw data such as text or images and automatically learn the important features to extract from those data. However, machine learning models lack the transparency of rule-based models.