Over the next decade, there will be a shift in digital pathology and artificial intelligence (AI) applications from the pharmaceutical sector to clinical diagnostics, according to a new report.
Once they are established as primary and secondary diagnostic tools, clinicians will be able to decrease turnaround time, prioritize critical cases, and improve overall patient outcomes. However, for AI tools to gain regulatory approval for primary diagnosis in digital pathology, it is imperative to adopt a parallel workflow to showcase the superior benefits of pathology diagnoses that amass data, a new report issued by Frost & Sullivan said.
“AI has the potential to analyse big data and find patterns and insights that could enhance patient outcomes in the field of pathology,” said Deepak Jayakumar, senior research analyst, TechVision. “It can serve as a supplementary or a validation tool in imaging analytics for pathologists, and help process more slides in a shorter duration.”
So far, AI-based medical diagnostic tools like OsteoDetect have already been approved for use by the US Food and Drug Administration (FDA) for the detection of distal radius fracture. Furthermore, AI-powered tools can better identify skin lesions and the presence of invasive breast cancer.
Titled Digital Pathology: Roadmap to the Future of Medical Diagnosis’, the study covers the solutions of digital whole slide scanning, digital imaging solutions, and digital data repository. The study identifies technology advancements, applications, and potential, as well as presents the most successful business models and strategies currently dominating the market.
“Hospitals and diagnostic labs will be the largest adopters of digital pathology over the next decade,” noted Jayakumar. “Technology innovators can ensure greater commercialisation by focusing on improving cost optimisation for end users through pay-per-use or Software-as-a-Service (SaaS) models. Additionally, they could map service gaps, expand product portfolios, and use the information obtained from clinical data to develop cutting-edge solutions.”
A few highlighted growth opportunities for digital pathology-enabling technology companies within this analysis include:
• Responsible use of datasets owned by medical institutions. A case in point is the creation of an imaging research warehouse by Mount Sinai Health System, which equips researchers with imaging and clinical data of more than a million patients.
• Collaboration with hospitals, diagnostic labs, and medical institutes to generate datasets comparing digital pathology and traditional methods to present a solid case for the regulatory approval of their product.
• Approval of AI-based tools needs to be a top priority for AI-based digital pathology-focused companies as low to medium risk diagnostic avenues are also an innovative way to transition from pharmaceutical to mainstream clinical applications in the digital pathology segment.
