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DIGITAL TRANSFORMATION AS A SEMANTIC SWITCH IN MEDICINE // ΠΡΑΞΗMΑ. Journal of Visual Semiotics. 2023. Issue 1 (35). P. 113-129

The article raises questions about how language, symptomatology, pathology description systems, and doctor-patient relationship will change in connection with the digital transformation of medicine. The effects of digitalization are analyzed using hermeneutic and semiotic approaches, the “signifying” and “understanding” methods are used to create an alternative view instead of the logic and language of information technology that prevails today. Digitalization begins with electronic medical records, transfers monitoring and control of the patient’s condition to personal medical assistants, and forms databases, which are the basis for the creation of neural networks and medical decision support systems. Telemedicine changes communication formats and transforms the subjectivity of the patient and the doctor. The introduction of digital algorithms changes the narratives of patients, the reading of the signs of the disease. The doctor-patient communication tends to have the patient describe their suffering through answers to test questions. The formalization of the language of medical description and patient narrative turns the “text” of the disease into medical data. There is a risk of translating medicine into a language that will largely be created by IT specialists, and the doctor will become the operator of intelligent systems for collecting and analyzing medical data. The digital transformation of medicine is a deep, qualitative transformation of the entire sphere of human health care, both at the individual and the social and institutional levels. Digital transformation occurs when people begin to think and act according to other codes, medicine becomes visually different. Semantic digital switching is reflected in the ethos of medicine. There is an inversion of subjectivity up to the disclaimer of responsibility, which is transferred to digital systems. The semiosis of medicine is reoriented from moral to economic goals. Digitalization generates new types of relationships between the doctor and the patient, strengthens their autonomy, but can also create conditions for a solidarity relationship of care in medicine. In assessing the risks of digitalization in medicine, a point of divergence is fixed: a deepening of a reductionist, digitally mediated view of the symptoms of live suffering humans and further distancing between the doctor and the patient may occur. Also, geneticized and digital personalization will allow taking into account a multilayer system of individual and culture-specific designations, introducing their interpretation into the world of scientific medicine, reformatting the solidary ties between the subjects of medicine through the responsible disposal of information.

Keywords: digital transformation of medicine, personalized medicine, bioethics, medical semiotics, hermeneutic approach, medical data, medical decision support systems, personal medical assistant, doctor-patient interaction

PERCEPTION IMAGES AND CONCEPTUALIZATION OF ANTHROPOLOGICAL CHALLENGES OF ARTIFICIAL INTELLIGENCE // ΠΡΑΞΗMΑ. Journal of Visual Semiotics. 2024. Issue 1 (39). P. 102-119

The challenges of artificial intelligence are considered from the methodological basis of bioethical analysis of anthropological risks and threats posed by new technologies. Society exhibits a cautious attitude towards artificial intelligence technology. Anthropological challenges of artificial intelligence represent a problematic situation regarding the complexity of assessing the benefits and harms, adequate awareness of the risks and threats of new technology to humans. It is necessary to conceptually outline the anthropological challenges of AI, drawing on images of AI perception represented in art and cinema, in ethical rules, philosophical reflection, and scientific concepts. In the projection of various definitions, artificial intelligence becomes a metaphor that serves as a source of creative conceptualizations of new technology. Images of AI are identified through conceptualization, visualization, and institutionalization of risks and correspond to specific types of attitudes towards innovation in society. The peculiarity of AI perception images, both in the forms of conceptualization and in the visual or institutional objectification of these images in ethical codes, is their active and purposeful formation. Analogous to the regulation of biotechnologies, normatively conceptualized positions regarding new technologies are divided into conservative - restrictive and prohibitive; liberal - welcoming innovations; and moderate - compromising, which often becomes the basis for ethical and legal regulation. However, sociological surveys show that those who welcome the emergence of neural networks, the widespread use of artificial intelligence, also exhibit caution and uncertainty in assessing the human future. A three-part typology of perception images of anthropological challenges is proposed, in which non-linear opposition of positions towards AI is fixed, but vectors of possible ways of habituating and semiotization of the future are outlined. The first, alarmist type, is distinguished based on an emotionally evaluative attitude. New technologies are seen as redundant, causing alarm and fear. The second type of perception, instrumentalist, is characteristic of AI actors within a professionally formed worldview. Some concepts of the professional thesaurus become common parlance. The third type is user-oriented. For this type, it is important how the interaction between AI and humans unfolds. The collective response to the anthropological challenges of AI is more likely to be formed on a utilitarian-pragmatic basis. Effective responses may be based on an individual self-preservation strategy, which, for example, may require adherence to cognitive hygiene in the field of education. In the context of AI development, the task arises of developing rules and procedures for such a preservation strategy.

Keywords: artificial intelligence, neural networks, AI concept, anthropological challenges, perception images, AI ethical code, humanistic expert evaluation