Generative AI Applications in the Health and Well-Being Domain: Virtual and Robotic Assistance and the Need for Niche Language Models (NLMs)

The global AI in Healthcare market is expected to grow from USD14.6 billion in 2023 to 102.7 billion by 2028 with a compound annual growth rate over that period of 47.6%. There is universal agreement that there is a rising demand for AI services as population ageing throughout the developed world causes the number of patients to increase faster than the healthcare workforce. Hence the healthcare sector presents many varied opportunities for generative AI in research, clinical, operational, and behavioural applications. This chapter investigates the complex challenges in the social context of behavioural applications. In this domain, analytics examine large data sets (including large language models or LLMs) for client behaviour patterns that increase the probability of actions taken to improve engagement, well-being and health outcomes. Natural language processing is an emerging field that can offer psychological counselling and assistance for social and mental health both outside and within care facilities. Virtual and embodied bots have a growing role in the management of emotions, stress and outcomes at the interface between doctors, psychologists, nurses and patients. Yet at the current state of technology in both virtual and embodied socially assistive robots (SARs), the user interfaces with an artificial system that operates based on pre-trained databases which give only the temporary illusion of a relational agent. At present SARs are able to sustain only brief engagements, presenting major challenges for broader user-acceptance. Such databases may also contain situational, cultural, gender, racial and other biases. There are also significant transparency and privacy issues. The future is therefore not in general LLMs but in carefully trained and constantly-updating niche language models (NLMs). In designing and applying such vital systems all stakeholders, including regulatory bodies, medical professionals, nursing professionals and patient advocacy groups must be involved. Finally, assistive technologies must become part of the core curriculum of health professional education.