Paper Contents
Abstract
Abstract AI and 3D printing are separately gaining momentum, but the connectivity of both these technologies has opened up new horizons in the field of personalized medicine. Hence it has facilitated the improvement of drug development, diagnostics, and therapy among other aspects. The purpose of the article is to assess the disruptive role of virtual simulations and the 3D printing processes in addressing the challenges posed by conventional systems such as design and production mesh losses. Other causes of delays in development could include the use of AI devices predictive modeling for predicting disease or drug targeting. At the same time, 3D printing allows for the production of specific designs of dosage forms, drug delivery systems, and methods of treatment, ranging from a combination pill to at home drug dispensing. Phenotypic personalized medicine PPM takes a different approach in AI usage, it is more data-sparing and centers on the specifics of the patients genes. Developments like CURATE.AI or PPD Parabolic Personalized Dosing help solve design of experiments for drug agencies and reap the maximum outcome in interventions where these problems are encountered including organ transplants and ARDS. Where the two technologies are most effective include organizing treatment strategies for advanced stage cancers like hepatocellular carcinoma HCC, where mHealth and gaming technologies structural and 3D bioprinting are being incorporated into the approach. The challenges experienced however include legal issues, privacy issues, and business processes. Moreover, accelerated developments in the processes of digital image classification and computation have led to the improved precision in 3D printing processes, but challenges such as lack of adequate annotated data, unbalanced classes, and limitations of computation in real time still present. The convergence of many fields such as AI, machine learning, 3D printing conveys the future which is characterized by customized medicine and this also solves the challenges of slow processes in drug discovery and health care systems. Nevertheless, equal distribution, ethical consideration and improvement of existing structure are among the many challenges which need to be solved in order to fully benefit from these incredible innovations. This synthesis illustrates when those challenges are overcome, there well be no turning back in progress towards using AI and 3D bioprinting in medicine, hence news ways of healthcare will be developed.
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Copyright © 2024 UDIT CHATURVEDI. This is an open access article distributed under the Creative Commons Attribution License.
