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The world is changing rapidly, driven by the application of new information technologies in virtually all areas of our lives. The field of mental health has not been one of the pioneers in this digital transformation, but research has advanced a lot in recent years, and more and more studies and practical applications of IT in the diagnosis and treatment of mental disorders are beginning to appear.

Of course, there are still many gaps and many (well-founded) concerns when we talk about connecting mental health with information technologies. Many of these concerns are related to the handling of sensitive patient data and the privacy and security with which this data will be treated. The development of an ethical code by the companies and entities involved, as well as a legal framework that guarantees the rights of patients, seem essential. On the other hand, the limitations of a technology that was beginning to appear, coupled with the lack of trusted studies in a field as complicated as mental health, could stop at first the use of IT in psychiatry.

Today this has changed, and there’s little doubt that big data, IoT, artificial intelligence, 5G networks, and other technologies offer immense potential, capable of revolutionizing the diagnosis and treatment of a multitude of disorders and, therefore, capable of improving the lives of millions of people.

The diagnosis of mental disorders depends fundamentally on the doctor having all the possible information about the patient, in an organized way so that he can analyze and interpret it to arrive at an accurate diagnosis. It is therefore about collecting, organizing, and interpreting data, and that is something that new technologies can do well.



Traditionally, mental health professionals have obtained their data mainly from three sources: direct observation, interviews with the patient and people in their environment, and the patient’s medical history. With the new technologies, you can reach much further, not only by the amount of information you can get but mainly by the quality and immediacy of it, as well as the ease with which it can be organized and managed.

Currently, we are connected to the network through our smartphones and other devices, such as smartwatches and biometric wristbands. Can all these data that we are continually sending be collected and used in a way that is useful for diagnosing a mental problem, or for predicting or detecting a crisis in an already diagnosed person? What kind of information could be helpful? In an article published in Nature published in February 2018, Laura Weiss Roberts, Steven Chan and John Torous point to three types of data: data provided by the patient, behavioral data, and physiological data.



Interviews with patients – and with people in their environment – are fundamental in the diagnostic process. However, they have some drawbacks.

On the one hand, it is possible that the person interviewed does not answer what they think, but what they think the doctor wants to hear; It is up to the medical professional and his experience to detect this bias. The interviews are time-consuming and have to be done by a qualified professional. Using an app can make the interviewee feel less inclined to answer “what they want to hear”; Besides, it is quite simple to repeat the interviews to validate and confirm the information.

On the other hand, in patients suffering from certain mental disorders, which affect memory, they may not remember well what they are being asked and could give incorrect answers. An app that asks questions continuously, practically in “real time,” can help solve this problem.

Some studies seem to indicate that the results of a personal interview differ and may be less precise than those obtained through information provided by a patient using a smartphone app. For example, a study conducted in 2015 by John Torous and other researchers concludes that, in patients with depressive disorders, “the results recorded by the app may potentially be more perceptive and more capable of capturing suicidal tendencies than the traditional PHQ-9.”



In addition to the information provided directly by the patient, a device can capture a vast amount of information about the behavior of a particular person. To give some examples: a smartphone can record how quickly a patient moves from one app to another; the manner and speed at which she writes; how much you communicate with other people through chats or calls; where you are going and if you change places too often, or if on the contrary you always stay at home. Above all, you can register patterns and detect changes in the patient’s habitual behavior, which can potentially be useful in a diagnostic process or predict the onset of a psychotic crisis or the worsening of a depressive disorder.

The analysis of the behavior through new technologies is not something simple and can even lead to errors. For example, the lack of online activity for a whole day could be attributed to the worsening of a depression, when in reality maybe it’s because the patient lost the smartphone. Also, there are aspects related to patient privacy that must be taken into account both ethically and legally. However, it is evident that smartphones and wearables can play an essential role in the behavioral analysis of patients very shortly.



The clinical history of a patient is a useful tool for the diagnosis of many mental disorders. Though, it is equally or more beneficial to have physiological data updated in real time. Currently, registering this data is very simple thanks to devices such as smartwatches and biometric wristbands, which are available to anyone.

Data such as cardiac and respiratory activity, sweating or sleep quality, combined with information provided by the patient and with behavioral data, can be instrumental in evaluating and finally diagnosing a patient. As was the case with behavioral data, the current state of technology allows us to collect all this information quite simply. However, there are still many problems arising from the way we must manage and interpret the data.

It should also be noted that obtaining biometric data through new technologies is not limited to the tracking of information through devices used by the patient. It also uses more sophisticated and expensive equipment designed for mental health clinics, such as the ocular vergence analysis system developed by Braingaze, which has demonstrated its effectiveness in the diagnosis of disorders such as ADHD.



In the previous paragraphs, we have seen how new technologies can help mental health professionals collect objective and relevant data to diagnose a patient. This massive amount of data, however, would not be of any help without a system that organizes it and places it within reach of professionals in a clear and straightforward way.

The first objective of the organization of the collected data is to ensure that the mental health professional can access and consult it as quickly and efficiently as possible. An example is the BGaze ACE interviews: people in a patient’s environment make reports on it through a personalized app. Later, when interviewing the patient, the medical professional can have the results of all those reports on a computer, quickly consult the results and use them as she considers most appropriate to obtain an accurate diagnosis.

Another way to manage and organize data is to use an artificial intelligence system that tracks and detects patterns in the behavior or physiological constants of a patient and can notice when one is altered from those patterns. Presented to the medical professional clearly, these patterns and especially the alterations that may occur in them can be of great help to diagnosing a disorder, controlling the evolution of a disease or even predicting a crisis.

It is also possible to use new technologies to interrelate different sets of data with each other. The information obtained in personal interviews is fundamental in the diagnostic process of a mental disorder; The same can be said about the behavioral patterns and physiological data of the patient. Still, on many occasions, each of these data sets, separately, are not sufficient to arrive at an accurate diagnosis. The use of data management software, equipped with artificial intelligence, can significantly facilitate the task of interrelating these three areas -for example, showing their temporal relationship-, so that the medical professional receives the information as a whole, instead of as three separate sets of data.

Regardless of the treatment given to the data collected and how it is organized, the objective is always the same: if the mental health professional has all the available information, presented with the highest clarity and possible simplicity, it will be much easier to interpret these data and reach an accurate diagnosis. But could it reach a point where artificial intelligence can understand the data and reach a diagnosis by itself?



The answer to the previous question is that at this point, although theoretically possible, it’s far from the capabilities of current technology. The diagnosis of a mental disorder is a complicated process, which depends on a series of factors – many of them derived from direct contact with the patient and the experience and knowledge of the medical professional; it is not a result that can arise automatically from entering a data set in a mathematical algorithm.

However, artificial intelligence software developers are taking steps in this direction. Not to replace the doctor, but to facilitate tasks and improve the diagnosis. To achieve this, BGaze ACE uses artificial intelligence algorithms to interpret the data under the criteria of DSM-5 and ICD-10 and generate an objective and accurate report, which can be of great help for the doctor to make the best diagnostic decision.

We do not know for sure what the future will bring us. Although there are still some serious technological, ethical and legal obstacles, the current scenario seems to indicate that we are on the threshold of a technological revolution in the field of diagnosis and treatment of mental disorders. As the 5G networks are implemented, and the artificial intelligence systems perfected, the research and development of mental health applications and technological equipment will be strengthened.