Dynamic Neural Architectures and AI-Augmented Platforms for Personalized Direct-to-Practitioner Healthcare Engagements

Abstract

Rapid advances in medically-compliant artificial intelligence broadened the possibilities to shape the modes of direct-to-practitioner healthcare engagement, expanding the traction beyond pre-appointment case information exchange and never-before-seen tailoring of discussed decision problems and underlying evidence automatically prepared by AI-augmented systems at the behest of the visiting patient or his general practitioner. From both information exchange and decision problem preparation angles, ubiquitous text entry fields are conceived that enable natural language interaction with the proposed systems so that even the weakest-information-literate healthcare service consumer can leverage highly-complex expert systems that go vastly beyond the capabilities of commonly available AI-powered solutions. To urge rapid adoption of the presented novel modes of AI-assisted healthcare engagement, an innovative reward system is proposed that randomly selects cases of proactive patient behavior positively affecting the efficiency of healthcare service delivery and refunds a part of the costs incurred at such moments. This perspective is extended onto the dynamics of the pricing policies of the engaged healthcare practitioners, presenting a model that accounts for the jump of available financial reserves resulting from the presented benefits of using the sophisticated direct-to-practitioner engagement facilities. Personalized medicine is about pathologizing the individual, uncovering and addressing, in an individual-specific manner, the intrinsic factors and environments that activate or exacerbate diseases. Despite already being the direction shepherded by the WHO as early as in 1998, only recently it could fully benefit from the modern ICT revolution, given that routine DNA sequencing became gradually available as of the first decade of the 21st c., and the rise of increasingly more efficient computational algorithms. The present juxtaposition reviews the inception and the current state of personalized treatment with a focus on cancer, aiming, inter alia, at determining its further direction and the potential consequences for the prevailing paradigm of care. There are several remaining ethical and societal dilemmas. For instance, we, as patients, may consult our general practitioners twice as often as an average person, though with no particular reason, or we might abruptly change our medical problems, also for made-up reasons, during an appointment mostly to get an antibiotic we suspect won't be prescribed this time. Therefore, hashing strictly around deterministic reactive policies, we would encourage the emergence of abuse-resistant GP engagement practices and corresponding shielding mechanisms.