Background/Case Studies: The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Cell therapy also holds the potential to address chronic diseases without current treatment options. Moreover, despite ongoing clinical trials, a fully developed cure through stem cell therapy is yet to be realized and AI can help.
Study
Design/Methods: The aims are to explore the utilization of AI in the field of regenerative medicine, discussing outcomes and proposing new research directions. One of the key advantages of employing AI in stem cell therapy is its capability to predict the most effective cell types by analyzing patients’ genetic information and medical records.
Results/Findings: Personalized medicine aims to provide tailored medical treatments to individual patients based on their genetic, environmental, and lifestyle factors. However, accurately predicting a patient’s response to a particular treatment remains a significant challenge due to the system’s complexity. AI can help overcome this challenge by analysing patient information and identifying patterns and associations that can predict treatment outcomes. One way AI can assist in personalized medicine is by analysing a patient’s genomic data. AI algorithms can identify genetic variations linked to specific diseases or treatment responses, enabling the development of personalized treatment plans based on the patient’s genetic profile. However, issues related to data privacy, bias, and regulatory challenges still must be addressed. This field has emerged as a promising alternative to traditional approaches. AI algorithms can analyse large amounts of data on different materials and fabrication techniques to identify suitable combinations for a specific tissue engineering application. Additionally, AI can assist in quality control by monitoring the fabrication process in real-time and detecting any deviations from the desired parameters.
Conclusions: One of the key benefits of using AI in cell therapy is its ability to help identify the best cells for a particular patient. AI can also help determine the optimal dose and timing of cell delivery to maximize therapeutic benefits. Additionally, it can assist in tracking the cells after delivery, monitoring their migration and survival, and detecting any adverse effects. This can aid in adjusting the treatment plan and improving patient outcomes. Despite its potential benefits, there are also limitations. FDA and EMA acknowledge that AI may be used in de novo design of product variants in precision medicine, and in personalised treatment, but do not yet consider the design of a ATMP/receptor for an individual patient. Internationally, it currently looks as though the regulatory approach applied will be a combination of inflexible frameworks.
