Organic versus Artificial Intelligence: Which is Going to Last in Justiciable Healthcare Delivery System?

1990s to late 2000s was the era of the Internet, both in terms of primary source of information as well as augmentative knowledge delivery in the healthcare delivery system. Right from the inception of an individual in the medical stream to the evolution of a specialist or a subspecialist, the Internet was both a delivery system of information as well as a knowledge enhancer. However, the forecasting and prediction of the health system of an individual were still in the domain of organic human intelligence. The current era is the dominance of artificial intelligence (AI) both as a primary model and as generative AI (predictive as well as forecasting issues). The use of AI in clinical delivery systems has yielded a precision-guided and digital healthcare delivery to an individual. Personalized AI in the form of ChatGPT or other similar models has enhanced the capacity of organic intelligence and institutions. Harvard Medical School has incorporated AI-augmented health education at the very elementary level of health education. It is pertinent to clarify at the very outset that AI in the healthcare delivery system should be utilized only as an augmented or auxiliary source, keeping human organic intelligence as the primary source of computing medical diagnosis and delivering therapeutics. This will not only greatly enhance the capacity of healthcare delivery but also help in reducing the errors due to the human element. The revolutionization of healthcare delivery by AI, enhancing diagnosing capabilities, personalizing treatment, smoothing out of operations, and remote delivery, is an outstanding achievement. Refinement of surgical procedures through robotic surgery and bot-enhanced precision delivery is the future of the clinical health module. AI generative tools not only improve efficiency but also give an accurate and accessible treatment protocol. Various algorithmic modules in diagnostics, such as medical image analyzers, speed enhancers, and accuracy detailers, have vastly improved the interpretational aspect of radiodiagnosis. AI can see the minutest details and subtle patterns in the patient data, which might have been overlooked by human interpretations. Various AI tools for the diagnosis of diabetes and complications, for example, retinopathy, both as a screening tool as well as a generative tool of automated images, have vastly improved the classification and stratification of the disease. AI has the capability to analyze and stratify through a vast amount of patient data and can make tailor-made treatment plans. Sometimes, patients have their own whimsical approach in terms of treatment selection, and in such cases, AI can do a clinically precise enhancement of decision-making capacity. The emphasis on optimization of a treatment protocol and minimization of adverse outcomes can be achieved through AI-integrated approach. Currently, healthcare administration and operations are as important as clinical delivery of health care to the end user. AI can augment as well as enhance various administrative tasks such as clinician appointment scheduling, queuing, billing, as well as management and maintenance of digital health records. This, in turn, frees up clinical time for enhanced patient care. The current usage of robotic process automation can be augmented by AI. This can boost the overall healthcare process, improve overall efficiency, and reduce risks. One of the greatest achievements during the COVID-19 pandemic was the optimal utilization and enhancement of telemedicine. The telemedicine platforms can be synchronized with AI tools to give leverage to accurate remote consultation, especially in areas with severe mobility restrictions. The latest trend of digital communication with patients through generative AI communicative tools can vastly improve the point delivery system of clinical health care. Predictive AI has the capability to analyze the vast patient data and its relation with the external factor influencers like environment, to give an accurate prediction of potential health hazards. Thus, the prediction of a possible outbreak of an epidemic or its potentiality to progress onto a pandemic can be forecasted. Similarly, the sudden spread of a genetically modified organism or its target population can be assessed well in time. There is a large and potentially usable database of drugs based on the increasing number of patents filed every day. AI generative tools can summarize and strategize such a database into potential and workable solutions for usage on a priority basis. This can greatly enhance the pharmaceutical output and decrease the cost of drugs.[1] AI in healthcare also carries a lot of setbacks on its shoulder. The biggest being a breach of data privacy. Since the healthcare delivery system is primarily focused on human beings, a lot of tangible data are generated, and the co-option of AI integration has the potentiality of data breach or data hack.[2] Since AI has access to a vast array of human databases, the potential risk of algorithmic miscalculation and bias may pose difficulty in interpreting AI models. This is all the more relevant given that currently, regulatory checks are deficient and the legal harness which is supposed to act as an overall safeguard is quite generic and loose in nature. However, AI overtaking human organic intelligence is far-fetched imagination, and at present, human intelligence is still in the saddle and well entrenched for time being.