AAMI and the British Standards Institute (BSI) have jointly published new guidance documents on performing risk management for machine learning or artificial intelligence incorporating medical devices.
The regulatory landscape for medical devices is rapidly evolving, catapulted most recently by European Union Medical Device Regulations (EU MDR). In this article, Monali Bhansali, Practice Lead of Regulatory Affairs at Tata Elxsi, highlights challenges and advancements in the MedTech regulatory landscape, and what the future has in store.
Regulatory requirements for computer systems validation (CSV) have long been in place, but their compliance requires considerable time and resources. In the life sciences industry, traditional validation processes add to project timelines and costs, affecting time to market and preventing the deployment of newer versions of software. In addition to improving accuracy and coverage, automated CSV processes can create the same artifacts as manual execution and expedite the upgrade timeline.
In spite of continuing delays to implementation deadlines for EU MDR/IVDR, post-market surveillance requirements are currently in force. Hence, manufacturers need to urgently establish a PMS system to identify potential nonconformances and safeguard users and patients.
Business strategy and forecasting influence several functional areas within an organization, including R&D, pipeline planning, revenue planning, inventory, resource and budget allocation, project prioritization, compensation plans, market access efforts, and more. These varied uses reflect the first major challenge of forecasting: meeting the needs of varied and diverse stakeholders. Following are three steps to make your forecasting efforts more effective.
Data integrity issues have plagued the pharma industry since its inception. Blockchain, with its potential to assist in processes from product serialization to data flow tracking, could be the best solution yet.
“The solution provider that builds the device and creates the algorithm should consider integration and accountability among multiple other challenges. But meeting the needs of the third element in the equation, the doctors, is key.”
The chronic, progressive presentation of COPD, symptom overlap, and nature of patient self-reporting make it hard to identify exacerbations. We need more specific guidelines around, as well as tools for, assessing a patient’s progression from day to day. AI-supported diagnostic systems represent a potential breakthrough technology that could help us overcome significant knowledge gaps.
Rama Chellappa, PhD, John Hopkins University Bloomberg Distinguished Professor in electrical, computer, and biomedical engineering, and co-author of “Can We Trust AI?” looks at the promise of AI in health care and how we can best utilize this extraordinary tool to save lives and improve health equity.
Connected sensors are a key component to improving patient access to and patient retention in clinical trials. Following are considerations for developers and sponsors when designing and selecting sensors for use in trials.
