De-risking regulatory submissions for medical technology companies – Med-Tech Innovation

by Josh Miller
29 September 2022
14:00
Josh Miller, co-founder & CEO of Gradient Health focuses on the regulatory hurdles medtech companies face and the different options for overcoming these from a data perspective. 
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Medical technology concept
Introduction
Following the record high of investments in biotech Q1 2021, it is becoming increasingly difficult to secure the funding necessary to bring a healthtech product to market, with a 61% reduction in venture funding in biotech according to Bay Bridge Bio. Investors are seeking to reduce risk and time to return on their investments. Furthermore, it can often be significantly more challenging to raise money prior to receiving a green light from regulators than afterward. Regulatory bodies define criteria that promote responsible development and distribution of safe and effective medical devices. Meeting regulatory milestones is thus a necessary challenge for device developers to meet to achieve commercial success. Altogether, this highlights the importance of understanding what regulators are looking for and best practices for meeting their criteria in a cost and time-effective manner. This article discusses some key qualities of good datasets for developing artificial intelligence (AI) and machine learning (ML) health technology as well as tips for evaluating tools that can accelerate development, validation, and regulatory clearance for such technologies.
Qualities of a strong dataset
The FDA, MHRA, and Health Canada have jointly defined 10 Good Machine Learning Practices (GMLP) that act as guidance for developers of medical devices that leverage AI/ML technology:
In particular, the criteria numbered 3, 4, and 5 define characteristics of high-quality datasets used for training and validation of AI/ML models.
Bias in medical AI may emerge when models lack diversity in patient demographics and disease presentation. Developers must objectively evaluate the performance of their technology in the intended patient population. Thoroughly and accurately labeled data can be incredibly useful to evaluate the representativeness of datasets for given patient populations.
Fast-tracking AI/ML device development and regulatory clearance with access to on-demand, quality data
Fortunately for innovators in the health tech space, there is an increasingly greater quantity and quality of options available to gain near-instantaneous access to curated data at their fingertips. Below are some routes to obtain data:
Healthtech innovators may wish to look for the following qualities in data sets:
There is a tremendous opportunity to progress healthcare by leaps and bounds by leveraging artificial intelligence and machine learning. Data intermediaries and partners can help innovators of medical technology to more easily overcome barriers to accessing large amounts of diverse data that may have previously represented nearly insurmountable obstacles for small, lightweight start-ups to overcome.
by
29 September 2022
14:00
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