Translation
Original language
16.06.2025
Investing in Technologies: Toward Healthcare of the Future
Today’s technological trends in developing the healthcare system involve accumulating and using big data; developing and introducing artificial intelligence both for handling medical tasks and for general health conservation purposes; moving away from head-on healthism propaganda toward changing the environment that will promote the necessary self-preservation behaviors; recognizing the key role peo- ple themselves play in preserving their health; emphasis on human-centricity, and, consequently, more intensive introduction of socio-humanitarian approaches to healthcare such as using Behavioral and Cultural Insights (BCI) approach proposed by the WHO to understand the subjective motivations behind people’s attitude to their health. Proceeding from these trends, we can predict the image of the health- care of the future that the system is progressing toward today. This essay will briefly outline the author’s vision of the further course these trends will follow.
First, it is big data. Their volume will certainly continue to grow as will capacities for their efficient processing. We will be able to collect increasingly large amounts of data both about population at large and about individuals. Health-re- lated gadgets people use to monitor their physical and emotional states should evolve toward providing more compatible data; these data can also be augmented
by additional self- observation data from people’s diaries and notes. Speech-totext sohware and other artificial intelligence apps will make it easier to collect indicators and structure the data about a person into a single dataset. Greater stan- dardization of algorithms for collecting health data will help the health sector prog- ress in that direction.
These trends make the already-urgent issue of the value of personal information all the more pressing. Today, there is a shortage of data for machine learning, while using large amounts of synthetic data in machine learning has an adverse effect on the quality of resulting models. Should these trends continue, we can agree with experts that rare and rich human health data will become increasingly more valuable. Such data can easily become a new “currency,” and granting access to such data to be used to train specialized neural networks will become a major source of revenues for the individuals granting such access. We can suppose that the future will see the emergence of data owner panels similar to today’s online survey panels; instead of their time, these owners will be selling access to the required data on their health. Blockchain technologies may become important in this process as they record data authorship and data transfer chains. There will be other mechanisms for exchanging such data, such as voluntary transfer of deper- sonalized information in exchange for using AI products including those that use these data to learn, etc.
This is not the only function that artificial intelligence could perform in health- care of the future. In addition to systematizing data and transferring them between modalities, AI can be used in communications. For instance, even today, com- bining large language models and additional databases using the RAG approach allows users to “communicate” with data: when a person queries the system, it can use the data on the person’s health and a cutting-edge information from medical reference books to give a correct, well-founded, and personalized answer to any query concerning that person’s health. LLMs can play an equally important role in psychodiagnostics: following communications with a person, such an LLM can “read” their psychological profile and therefore personalize communication. Understanding what is “correct” healthwise and pleasant for a specific person makes it possible to develop recommendations that make healthy lifestyle more pleasant. Essentially, LLMs can serve as basis for “health coaches” guiding people’s health-related behavior.
Today, as many researchers state, we, indeed, can see the birth of full-fledged “technosubjects” represented by AI. AI is increasingly learning to think like a human being (or at least imitate such thinking). AT is becoming increasingly skilled at (imitating) empathy and emotionality provided it is set the appropriate role. This development paves the way for the “digital doubles” technology that is being developed today: we can create a full-fledged digital model of a person for testing those social hypotheses that could not be tested on real people for ethical reasons.
On the other hand, the “technosubject” can far exceed people in performing calculations with far greater speed and completeness, which is paving the way for using AI as a full-fledged researcher that independently generates and tests hypotheses. This is a trend we see today already. These developments posit urgent questions of ethics and of drawing boundaries between the work of AI and human beings.
Another key application of neural networks is prediction and “alerting” systems when consequences of certain decisions concerning health may be predicted and current state of health can be extrapolated to provide recommendations or, in emergencies, to alert the medical services overseeing that person’s health. The predictive function improves the efficiency of continuous monitoring systems that could be launched even today, yet in the future, they should become multimodal.
Multimodal expansion could logically result in integrating the infosystems designed to care for an individual’s health and for the environment; such an integrated system should become individual-friendly. For instance, while developing recommendations, the system managing a person’s health could interact with the environment information about nearby green spaces, the state of the environment, availability of required foods, sports infrastructure, etc. It would also be logical to build reverse links when environmental planning for a specific territory would be tailored to its population’s health needs both short-term and with account for sea- sonal changes and predictions for the future (such as the aging predictions for a given territory’s current population).
Another possible trend in health integration involves moving toward general well-being, integral health. The World Health Organization rightly notes that, in addition to one’s physical condition, health also includes mental and social health. It would therefore be logical to integrate data on one’s physical state with data on their mental state that could also be collected as part of interaction with mental health services, gadgets, diaries, and other sources of information about mental well-being. The same is true for social well-being. Therefore, medicine’s move- ment toward biopsychosocial approach should logically be reflected in the future of the healthcare system. It can be institutionalized, among other things, through establishing a social well-being ministry similar to Russia’s former Ministry of Healthcare and Social Development, except the new ministry would function on a different level and on a different fundamental basis.
The above-described opportunity growth will likely create certain imperatives mandating that people care about their own health and the health of those around them, which may take certain legal forms.
Previously, we described healthcare system transformation as related to patients. Yet, healthcare system transformation as related to healthcare providers will be no less important.
Transforming the functions of the healthcare system, which entails, in particular, large language models and other neural networks “taking over” certain On the other hand, the “technosubject” can far exceed people in performing calculations with far greater speed and completeness, which is paving the way for using AI as a full-fledged researcher that independently generates and tests hypotheses. This is a trend we see today already. These developments posit urgent questions of ethics and of drawing boundaries between the work of AI and human beings.
Another key application of neural networks is prediction and “alerting” systems when consequences of certain decisions concerning health may be predicted and current state of health can be extrapolated to provide recommendations or, in emergencies, to alert the medical services overseeing that person’s health. The predictive function improves the efficiency of continuous monitoring systems that could be launched even today, yet in the future, they should become multimodal.
Multimodal expansion could logically result in integrating the infosystems designed to care for an individual’s health and for the environment; such an integrated system should become individual-friendly. For instance, while developing recommendations, the system managing a person’s health could interact with the environment information about nearby green spaces, the state of the environment, availability of required foods, sports infrastructure, etc. It would also be logical to build reverse links when environmental planning for a specific territory would be tailored to its population’s health needs both short-term and with account for sea- sonal changes and predictions for the future (such as the aging predictions for a given territory’s current population).
Another possible trend in health integration involves moving toward general well-being, integral health. The World Health Organization rightly notes that, in addition to one’s physical condition, health also includes mental and social health. It would therefore be logical to integrate data on one’s physical state with data on their mental state that could also be collected as part of interaction with mental health services, gadgets, diaries, and other sources of information about mental well-being. The same is true for social well-being. Therefore, medicine’s move- ment toward biopsychosocial approach should logically be reflected in the future of the healthcare system. It can be institutionalized, among other things, through establishing a social well-being ministry similar to Russia’s former Ministry of Healthcare and Social Development, except the new ministry would function on a different level and on a different fundamental basis.
The above-described opportunity growth will likely create certain imperatives mandating that people care about their own health and the health of those around them, which may take certain legal forms.
Previously, we described healthcare system transformation as related to patients. Yet, healthcare system transformation as related to healthcare providers will be no less important.
Transforming the functions of the healthcare system, which entails, in particular, large language models and other neural networks “taking over” certain
functions will lead to changes in medical personnel’s training. Medical personnel will have to have better “digital” literacy, be able to interact with neural networks that will be their aides taking on routine tasks and helping in difficult cases. We can also agree with the statement that as neural networks take over some of medical personnel’s functions, the role of medical personnel’s “soh skills,” service and communication skills, will grow both in private and in public medical care. Medical personnel can be largely responsible for creating the “human” atmosphere that is still largely beyond the abilities of neural networks. Humanitarian aspects in doctor-patient communication will become ever more important, and that will include legal, ethical, and psychological matters.
As professionals’ functions change, statuses may be redistributed between medical occupations; compared to the current situation, the roles of such “quiet” medical professions as a nurse or a paramedic can gain importance.
This real transformation of a medic’s functions should be accompanied by changes to their image that would be part of the overall transformation of healthcare system perception by both employees and population. Work on shaping the new image of the healthcare system should be started in advance since social perceptions are usually quite entrenched.
The developments outlined in this essay appear to be not so much fantasies as predictions of AI development in medicine. The greater part is already being implemented, even if in incipient forms. However, much work still lies ahead, including the work to be done in the humanities: working through the issues of law, responsibility, ethics, and psychology. Ultimately, progress is not the goal in itself; the main measure of a successful healthcare system today and tomorrow is the human being and their well-being.
First, it is big data. Their volume will certainly continue to grow as will capacities for their efficient processing. We will be able to collect increasingly large amounts of data both about population at large and about individuals. Health-re- lated gadgets people use to monitor their physical and emotional states should evolve toward providing more compatible data; these data can also be augmented
by additional self- observation data from people’s diaries and notes. Speech-totext sohware and other artificial intelligence apps will make it easier to collect indicators and structure the data about a person into a single dataset. Greater stan- dardization of algorithms for collecting health data will help the health sector prog- ress in that direction.
These trends make the already-urgent issue of the value of personal information all the more pressing. Today, there is a shortage of data for machine learning, while using large amounts of synthetic data in machine learning has an adverse effect on the quality of resulting models. Should these trends continue, we can agree with experts that rare and rich human health data will become increasingly more valuable. Such data can easily become a new “currency,” and granting access to such data to be used to train specialized neural networks will become a major source of revenues for the individuals granting such access. We can suppose that the future will see the emergence of data owner panels similar to today’s online survey panels; instead of their time, these owners will be selling access to the required data on their health. Blockchain technologies may become important in this process as they record data authorship and data transfer chains. There will be other mechanisms for exchanging such data, such as voluntary transfer of deper- sonalized information in exchange for using AI products including those that use these data to learn, etc.
This is not the only function that artificial intelligence could perform in health- care of the future. In addition to systematizing data and transferring them between modalities, AI can be used in communications. For instance, even today, com- bining large language models and additional databases using the RAG approach allows users to “communicate” with data: when a person queries the system, it can use the data on the person’s health and a cutting-edge information from medical reference books to give a correct, well-founded, and personalized answer to any query concerning that person’s health. LLMs can play an equally important role in psychodiagnostics: following communications with a person, such an LLM can “read” their psychological profile and therefore personalize communication. Understanding what is “correct” healthwise and pleasant for a specific person makes it possible to develop recommendations that make healthy lifestyle more pleasant. Essentially, LLMs can serve as basis for “health coaches” guiding people’s health-related behavior.
Today, as many researchers state, we, indeed, can see the birth of full-fledged “technosubjects” represented by AI. AI is increasingly learning to think like a human being (or at least imitate such thinking). AT is becoming increasingly skilled at (imitating) empathy and emotionality provided it is set the appropriate role. This development paves the way for the “digital doubles” technology that is being developed today: we can create a full-fledged digital model of a person for testing those social hypotheses that could not be tested on real people for ethical reasons.
On the other hand, the “technosubject” can far exceed people in performing calculations with far greater speed and completeness, which is paving the way for using AI as a full-fledged researcher that independently generates and tests hypotheses. This is a trend we see today already. These developments posit urgent questions of ethics and of drawing boundaries between the work of AI and human beings.
Another key application of neural networks is prediction and “alerting” systems when consequences of certain decisions concerning health may be predicted and current state of health can be extrapolated to provide recommendations or, in emergencies, to alert the medical services overseeing that person’s health. The predictive function improves the efficiency of continuous monitoring systems that could be launched even today, yet in the future, they should become multimodal.
Multimodal expansion could logically result in integrating the infosystems designed to care for an individual’s health and for the environment; such an integrated system should become individual-friendly. For instance, while developing recommendations, the system managing a person’s health could interact with the environment information about nearby green spaces, the state of the environment, availability of required foods, sports infrastructure, etc. It would also be logical to build reverse links when environmental planning for a specific territory would be tailored to its population’s health needs both short-term and with account for sea- sonal changes and predictions for the future (such as the aging predictions for a given territory’s current population).
Another possible trend in health integration involves moving toward general well-being, integral health. The World Health Organization rightly notes that, in addition to one’s physical condition, health also includes mental and social health. It would therefore be logical to integrate data on one’s physical state with data on their mental state that could also be collected as part of interaction with mental health services, gadgets, diaries, and other sources of information about mental well-being. The same is true for social well-being. Therefore, medicine’s move- ment toward biopsychosocial approach should logically be reflected in the future of the healthcare system. It can be institutionalized, among other things, through establishing a social well-being ministry similar to Russia’s former Ministry of Healthcare and Social Development, except the new ministry would function on a different level and on a different fundamental basis.
The above-described opportunity growth will likely create certain imperatives mandating that people care about their own health and the health of those around them, which may take certain legal forms.
Previously, we described healthcare system transformation as related to patients. Yet, healthcare system transformation as related to healthcare providers will be no less important.
Transforming the functions of the healthcare system, which entails, in particular, large language models and other neural networks “taking over” certain On the other hand, the “technosubject” can far exceed people in performing calculations with far greater speed and completeness, which is paving the way for using AI as a full-fledged researcher that independently generates and tests hypotheses. This is a trend we see today already. These developments posit urgent questions of ethics and of drawing boundaries between the work of AI and human beings.
Another key application of neural networks is prediction and “alerting” systems when consequences of certain decisions concerning health may be predicted and current state of health can be extrapolated to provide recommendations or, in emergencies, to alert the medical services overseeing that person’s health. The predictive function improves the efficiency of continuous monitoring systems that could be launched even today, yet in the future, they should become multimodal.
Multimodal expansion could logically result in integrating the infosystems designed to care for an individual’s health and for the environment; such an integrated system should become individual-friendly. For instance, while developing recommendations, the system managing a person’s health could interact with the environment information about nearby green spaces, the state of the environment, availability of required foods, sports infrastructure, etc. It would also be logical to build reverse links when environmental planning for a specific territory would be tailored to its population’s health needs both short-term and with account for sea- sonal changes and predictions for the future (such as the aging predictions for a given territory’s current population).
Another possible trend in health integration involves moving toward general well-being, integral health. The World Health Organization rightly notes that, in addition to one’s physical condition, health also includes mental and social health. It would therefore be logical to integrate data on one’s physical state with data on their mental state that could also be collected as part of interaction with mental health services, gadgets, diaries, and other sources of information about mental well-being. The same is true for social well-being. Therefore, medicine’s move- ment toward biopsychosocial approach should logically be reflected in the future of the healthcare system. It can be institutionalized, among other things, through establishing a social well-being ministry similar to Russia’s former Ministry of Healthcare and Social Development, except the new ministry would function on a different level and on a different fundamental basis.
The above-described opportunity growth will likely create certain imperatives mandating that people care about their own health and the health of those around them, which may take certain legal forms.
Previously, we described healthcare system transformation as related to patients. Yet, healthcare system transformation as related to healthcare providers will be no less important.
Transforming the functions of the healthcare system, which entails, in particular, large language models and other neural networks “taking over” certain
functions will lead to changes in medical personnel’s training. Medical personnel will have to have better “digital” literacy, be able to interact with neural networks that will be their aides taking on routine tasks and helping in difficult cases. We can also agree with the statement that as neural networks take over some of medical personnel’s functions, the role of medical personnel’s “soh skills,” service and communication skills, will grow both in private and in public medical care. Medical personnel can be largely responsible for creating the “human” atmosphere that is still largely beyond the abilities of neural networks. Humanitarian aspects in doctor-patient communication will become ever more important, and that will include legal, ethical, and psychological matters.
As professionals’ functions change, statuses may be redistributed between medical occupations; compared to the current situation, the roles of such “quiet” medical professions as a nurse or a paramedic can gain importance.
This real transformation of a medic’s functions should be accompanied by changes to their image that would be part of the overall transformation of healthcare system perception by both employees and population. Work on shaping the new image of the healthcare system should be started in advance since social perceptions are usually quite entrenched.
The developments outlined in this essay appear to be not so much fantasies as predictions of AI development in medicine. The greater part is already being implemented, even if in incipient forms. However, much work still lies ahead, including the work to be done in the humanities: working through the issues of law, responsibility, ethics, and psychology. Ultimately, progress is not the goal in itself; the main measure of a successful healthcare system today and tomorrow is the human being and their well-being.
Read full text
