The medical device industry is undergoing continuous transformation to comply with the ever-changing Regulatory guidelines introduced by global Regulatory authorities to ensure strict controls over device performance and safety. In such a scenario, having a better understanding of the medical devices, Regulatory Affairs, and updated Regulatory information to design and implement a strategy can result in reduced costs, time-to-market, and maximum compliance.

Isn’t it interesting to see Artificial Intelligence (AI) sculpting out to have a thriving position in the Regulatory landscape? One of the most important reasons for this advancement is the ability of AI and Machine Learning (ML) applications to tackle everyday procedural challenges and logical issues that compliance officers put up with.

Sustaining the ever-evolving global Regulatory space of the life sciences industry is strenuous. Therefore, it is necessary for manufacturers to keep abreast with the latest Regulatory information to ensure compliance across all the business functions, such as clinical development, release, and marketing scrutiny of medicines.

As we all aware, COVID-19 pandemic resulted in the suspension of regular on-site inspections and as the world is slowly coming out of COVID-19 restrictions, Health Agencies are eying on resuming the on-site inspections. In the same context, Swissmedic (the Swiss Agency for Therapeutic Goods) has resumed regular on-site inspections considering the measures of hygiene, social distancing, and the tracing of COVID-19 infections.

Life sciences, as an industry, is dynamic in nature due to constant Regulatory enforcement from global health authorities. In such a scenario, as there would be continuous modifications to the safety information that has to be conveyed through drug labels, the label lifecycle management is treated as a mission-critical system. Thus, labeling processes must be streamlined and capable enough to adopt and implement the global changes in real-time. But it’s not that easy as it has been said.

National Medical Products Administration (NMPA), formerly known as China Food and Drug Administration (CFDA), is all set to impose stricter regulations on auditing and reinforcement of clinical trials. To ensure best practices for in vitro diagnostic device (IVD), the agency released “Draft IVD Clinical Trial Guideline for Feedback” on November 22, 2018. The guidelines will come into effect in 2019 (exact timeline is yet to be declared by NMPA), after which all the new/existing devices applying for registration/renewal will have to comply with them.

Artificial Intelligence (AI) is by far the most talked about technological advancement of this decade. Although the idea of AI has been around for years, the processers and computational speed of the previous era were not up to the mark, thus couldn’t support the analysis of huge data as required. But with the evolution of new generation algorithms and systems, the computational speeds have risen by multiple folds, uncovering the scope for real-time analyses of huge data banks.

As a big face lift, Health Authorities of China and South Africa have recently revealed their identifications. The China Food and Drug Administration (CFDA), has recently announced the change related to the agency’s official English name, the National Medical Product Administration (NMPA). Similarly, South Africa’s Medicines Control Council (MCC) has officially concluded that MCC has transferred its supervisory responsibilities to a public entity, the South African Health Products Regulatory Authority (SAHPRA). What led to these changes? Let’s learn in detail.