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Aiforia Technologies CEO Kaisa Helminen talks about the benefits of pharma embracing digital technologies, especially AI-assisted image analysis in the field of histopathology.
Kaisa Helminen, MSc, CEO of Aiforia Technologies, is focused on driving forward her company’s mission to revolutionize healthcare across the globe with artificial intelligence (AI)-powered image analysis. Here she speaks to Pharm Exec about the benefits of pharma embracing digital technologies, especially AI-assisted image analysis in the field of histopathology.
Kaisa Helminen: The challenges of adopting digital technologies really depends on which phase you’re in along the drug discovery and development process. For example, the closer you get to the clinical trial phase, the more challenges you’ll face in terms of compliance and regulation. Any changes to the resources and tools leveraged during this stage becomes a more complicated, controlled approval and implementation process. Conversely, those in the preclinical phase, where speed and efficiency are highly coveted, experience fewer hurdles because they’re researching and analyzing animal samples. However, if digital technologies were more widely adopted at the beginning of the development cycle, there would be pressure to go through the regulatory process early on before reaching the clinical trial phase, where the same technology could then be used.
The lack of internal resources to research, implement and manage digital technologies can be a limiting factor as well. For international companies, these limitations can feel further exacerbated by geographical and language barriers. However, many of today’s tools and technologies take the administrative burden off pharma, allow for remote access and analysis and help make collaboration across the globe easy and attainable.
Adding automation to drug development workflows can significantly shorten the time it takes to move a new drug into market; the amount of resources needed is reduced as manual tasks are condensed or eliminated, and with many digital technologies, there’s a minimized need for IT, computer science, informatics hardware and expertise. When pharma researchers and medical experts are supported by technology, their time frees up to focus on new tasks and projects that can ultimately lead to new scientific discoveries.
New technologies can also provide greater accuracy and consistency. When researchers are armed with quantitative visual tools, for example, their analysis becomes less manual and more sophisticated because they’re validating results from trained algorithms. This is especially beneficial during the R&D process, where you need to identify the smallest differences down to the tissue level-this is what ultimately leads to novel research.
Digital tools are ever-changing. Only a few years ago, with traditional machine learning for example, it was difficult to fine-tune or modify code in trained image-analysis algorithms. Today, as machine learning evolves into various forms like deep learning AI, it’s much easier to re-train algorithms as insights are uncovered or if some parameters change in samples.
Let’s say thousands of samples, with multiple sections from each, need to be analysed. Each person analysing those sample images will have a unique interpretation and will likely capture something different. This subjectivity is natural, of course, but when it comes to the discovery process, it might mean missing out on valuable data and insights. Today, when images come into the lab that identify new molecules or sample types, deep learning AI is adaptable and capable of being quickly trained on patterns to take new learnings into account. Having trained algorithms that mimic observers’ complex processes helps eliminate inter- and intra-observer variability and ensures greater consistency and accuracy.
Changing complex workflows takes time, investment and caution, but those hurdles shouldn’t delay the race toward innovation. There are digital tools, solutions and services available that can not only help achieve the “pie in the sky” goals that all pharma companies have, but they can also help improve practical, day-to-day tasks immediately. Digital innovation does not need to be frightening, overwhelming or difficult, and these companies don’t need to tackle it alone.
Plus, this isn’t just “talk” anymore. We’re seeing that those who embrace digital technologies like AI are quick to realize the benefits; in pharma, the benefits are vast: more efficient and accurate toxicology and pathology studies, greater quantitative analysis of samples, a reduction in R&D spend, a faster drug-to-market process, improved patient safety and much more.