The biotechnology and pharmaceutical industries are heavily reliant on collecting, storing, and analyzing data for both R&D as well as production purposes. The large, countless, and rapidly growing sets of data are critical for researchers and scientists to accelerate progress in the medical industry. As our technologies advance and our capacity to store data continue to increase, we must continue to find new ways to efficiently analyze data. Researchers at the European Bioinformatics Institute (EMBL-EBI) have determined that nucleotide and proteomics data is growing at an exponential rate, with the amount of data stored on their servers doubling each year 1. Although larger data sets may indicate an abundance of opportunities for researchers, the challenge is in effectively analyzing and processing the data. This is where Artificial Intelligence and Machine Learning (AI/ML) can be utilized to take on the challenge. AI is a concept in which we create intelligent machines and systems, and ML is an application of AI in which machines and systems can learn and analyze data without being ‘told to do so’ 2.
As software and applications utilizing AI/ML are continuously being implemented into different medical devices and systems, the need to regulate these software’s is a pressing issue. The FDA is changing its stance on how it reviews and regulates biomedical devices employed with AI/ML software. We can expect a shift in the FDA’s stance on AI/ML software-based devices as the software will be the critical functioning aspect of a device – rather than its physical mechanics or chemistry. 3 The growing need for regulatory affairs and intervention in these devices requires a high level of quality assurance to ensure that any issued software for systems and devices is compliant with the FDA and cGMPs.
EMMA International is here to provide full-circle solutions for all aspects of the MedTech industry. Give us a call at 248-987-4497 or email us at info@emmainternational.com to learn more about how EMMA International can take the stress out of quality and regulatory compliance!
1 Oliveira, A.L. (2019), Biotechnology, Big Data and Artificial Intelligence. Biotechnol. J., 14: 1800613. https://doi.org/10.1002/biot.201800613
2 UIC (Nov. 2020) Machine Learning in Healthcare: Examples, Tips & Resources for Implementing into Your Care Practice. https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/
3 FDA (Jan. 2021) Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
