Next-Generation Healthcare: Merging AI, ML, and Big Data for Accelerated Disease Diagnosis and Personalized Treatment

Abstract

The rapid growth of information technologies generated unprecedented data, offering scientific discoveries not conventionally accessible. The integration of Big Data into daily aspects of life has enabled holistic functioning in various sectors. However, the potential for faster and fractionated individual care and individualized treatments needs to be materialized in a broadly adaptable manner. Disease prevention and amelioration is the pinnacle of care, and new therapeutic interventions and individualized approaches tailored to patients and their lifestyle can help health systems cope with the challenges they face. Currently, management largely involves one or more medicines following established treatment guidelines and protocols. New therapeutic interventions, including gene and cell therapies as well as pharmacogenetic treatments informed by AI and ML predictions, offer the promise of turning medicine into a far more proactive and individualized practice. However, their application is limited mainly to population-positive methodologies and sites of expedient integration. There are numerous approaches to designing medicines and conceiving of treatments. Development, approval, and delivery processes are challenging and tedious, which limits deeply integrated designs and turns therapies into largely population-positive methodologies. Moreover, tailorable solutions are limitedly available at the facility level. Both clinician and administrative knowledge sources are ill-equipped to integrate badly needed treatments into the system. Moreover, individual care and rapid interventions are thought to be mostly infeasible, and ADAPT is needed. There is a conceptually unlimited space within the input domains of care where predictions of new drugs can be made on the timescale of hours. However, effective care addressing individual complexities is a conceptually intractable task demanding the development of unexplored advanced predictive methodologies. Similar efforts are underway in personalized healthcare, on the opposite end of the quality spectrum. Knowledge-intensive complex networks are being comprehensively modeled by advanced AI and ML technologies to offer faithful modelling of patients and hence precise predictions of their responses to treatments. Information technology-enhanced ways of extracting data from deliverables can vastly improve the quality of modeling done by machines of large systems involving complex inputs. Although actively pursued, taken individually, there are little couplings between the two approaches shaping drastically different and non-compatible solutions to addressing the existing and resisted challenges of health systems.