RELATIONAL DIMENSION VERSUS ARTIFICIAL INTELLIGENCE

Thirty years ago, we proposed the similarity between the functioning of artificial intelligence and the human psyche, suggesting multiple parallels between the Freudian model proposed in the "Project for Psychology for Neurologists" and the connectionist theories applied in the generation of parallel distributed processing systems (PDP), also known as connectionist models. These models have been and continue to be the foundation of general artificial intelligences like ChatGPT, evolving and gaining prominence in everyday life. From the earliest applications in psychiatry, recreating computationally simulated modes of illnesses, to the use of deep learning models, especially in the field of computer vision for tasks such as image recognition, segmentation, and classification. Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) are employed for tasks involving sequences of data, such as natural language processing, or models based on the Transformer architecture, like BERT and GPT (Generative Pre-trained Transformer), which have revolutionized natural language processing. In this present work, we analyze the significance of the emergence and exponential growth of these types of tools in the field of healthcare, from medical diagnosis and patient care to psychological attention and psychotherapeutic treatment, exploring the changes and transformations in the forms of subjective expression that are arising. We also examine and argue for the importance and validity of the relational dimension proposed by our psychoanalytic approach in contrast to the potential use of these tools as treatment models.