AI-powered computer vision accelerates innovation

Few industries are as important to the wellbeing of people than those that produce the medical devices and drugs that enhance and save lives. And few technologies will be as transformative as artificial intelligence as it touches more and more industries, including life sciences.

No doubt, the use of AI in the medical device and pharmaceutical industries is growing. Advances in computer vision and machine learning are helping companies get medical devices and drugs to market in a more reliable and safe way, and sometimes at a reduced cost.

Pharmaceutical companies, such as J&J, GSK, AstraZeneca, Novartis, Pfizer, Sanofi, Eli Lilly, and others, have made significant investments in AI technology, including equity investments, acquisitions of, or partnerships with, AI-focused companies, building internal capabilities, or a combination of approaches, reports BiopharmaTrend.com. Also, as more AI-based tools and devices are approved, providers can use them in their work.

Images in AI

Computer vision, which enables software to analyse images, is a form of AI that will be used in every industry to make products and services better, more quickly. In the life sciences industries, AI and computer vision will be game-changing technologies that will have as many uses as imaging allows.

For instance, by gathering images of manufacturing defects, users can train an AI model to identify defects and negate the need for manual inspections, while improving quality and process speed. That can help life sciences companies identify defects faster and allow for continuous improvement of manufacturing processes.

Companies are already using computer vision platforms to classify pills, inspect vials, conduct quality and assurance for packaging, or find and eradicate defects in medical device components. With a computer vision platform, visual inspections can happen faster and with greater reliability than if done manually. For one thing, an AI system won’t lose focus, as humans sometimes do.

OmniAb, for instance, leverages computational, hardware-based, and genetic technologies to enable rapid development of innovative therapeutics. By automating a manual visual review, it increased its inspection throughput by up to 10 times and found up to 30% more potential objects of interest than by visual inspection.

“Using the latest AI-based technologies will not only reduce the time needed for the products to come to the market, but will also improve the quality of products and the overall safety of the production process, and provide better utilisation of available resources, along with being cost-effective, thereby increasing the importance of automation,” concludes a study published in Drug Discovery Today.

Regulating software tools

These pharmaceutical and medical device companies also face regulations that - while intended to boost safety of medical devices and drugs - make it harder for them to deploy the latest technology advancements in the production of these products.

As such, medical device and pharmaceutical companies constantly need to balance the desire to move fast with the need to meet Good Manufacturing Practices (GMP), which are intended to ensure that products perform as expected, specifically those contained in the Code of Federal Regulations, Title 21, Food and Drugs (21CFR).

The FDA regulates medical devices and drug development. In the interest of safety, it mandates that all software tools be validated, meaning they be checked and tested to ensure that they will perform a certain way all the time. This helps maintain the safe production and delivery of medical devices and drugs.

However, software validation can also take months and require future updates as tools change, which is frequently the case.

In today’s world, software changes too fast for companies to continually validate. As a result, they might:

• Miss innovations in tools because they don’t, or can’t, regularly validate their tools.
• Put software on-premise and then freeze it and miss out on the benefits of the cloud.
• Continually validate, which is time-consuming and costly.

Balancing speed and safety

Validation does not have to occur after each software release update. However, each release has to be judged for impact. If a change affects a regulated function, it has to be validated.

For FDA-regulated companies, this means that the ability to adopt new software advancements presents opportunities to continuously improve what they do. But the frequency of software advances also presents a challenge because of the validation requirements. As more vendors create cloud-based AI solutions that pharma and medical device makers will want to use, due to a number of benefits that come from cloud offerings, validation requirements may rise because cloud-based technologies are often evolving rapidly.

In the MedTech industry, validation costs range between 1 and 1.5 times the cost of implementation of software used to support production, automation, and quality systems, found Axendia, an analyst firm. The “medical device industry lags in implementation of automated systems and new technologies, due to lack of clarity, outdated compliance approaches, and perceived regulatory burden,” Axendia stated.

Companies need to meet FDA regulations to avoid being cited for being out of compliance. Such citations can be costly in terms of remediation and reputation.

Most likely, FDA regulations and guidelines will always be under review and patient safety must remain paramount. However, companies can reduce validation time and cost factors by looking for:

• Products that have controlled release cycles. By knowing when a software tool will be updated, companies can document, test, and validate upcoming features before the new software is launched, so that companies can readily deploy it. Also, by leveraging pre-built validation documentation, teams can focus solely on the execution of the validation and greatly speed up the entire process.
• Partners that know the validation ropes. This can help companies scale more quickly and effectively, reducing validation timeframes from months to weeks because partners have expertise on validation requirements, including documentation. Also, such partners can quickly retrain and revalidate an AI model if drug or medical device makers change their manufacturing processes or make changes to devices.

Democratising access to AI

Complying with upfront and ongoing validation will only get more complex as AI tools and computer vision platforms get more numerous.

But the payoff will be worth it. Every new technology takes time to fold into existing processes. AI, starting with computer vision, is a revolutionary tool for medical device, pharmaceutical, and life sciences industries, and we’ll see rapid innovation in the coming years as access to AI becomes more democratised.

The faster the benefits of computer vision and other AI tools get deep into the pharma and medical device industries, the faster companies and consumers will both benefit.