They review the AI challenge of organizing disarrayed informational islands such as technology, clinical protocols, and digital solutions into cohesive, well-developed systems and offer insight into the medtech industry’s progress in this area.
The AI Holy Grail for decision support
AI technologies emerged quickly over the last several years in medical imaging and oncology, encountering skepticism and resistance from clinicians concerned about accuracy, job security, and malpractice implications. Fast forwarding to today, those factors are still present, though the need for collaboration with physicians and human minds is clear: the AI is only as good as its inputs.
Ben Newton explains GE Healthcare’s investment in AI, with the goals of alleviating clinician burnout, workflow issues, and individual disease treatment and prognoses. He shares that generative AI and large language models (LLMs) promise to facilitate connectivity between disparate databases from pathology to medical records and genomic data. Confronted with the vast volume and complexity of patient information, clinical studies, and evolving treatment protocols, AI is emerging as a tool to help physicians make sense of otherwise overwhelming data streams. This goes beyond generating reports, moving towards enhanced clinical decision support and patient stratification, paving the way for more personalized medicine approaches in oncology and related disciplines
This potential for AI goes much further than the original skill of creating reports and dives right into clinical decision support and patient stratification, ultimately contributing to truly personalized medicine.
Unpacking AI’s promise in radiology and oncology
For example, radiology is a key focus as traditional imaging has shown what is present, while AI has prognostic power. By partnering with radiologists, large academic centers, and other entities, GE Healthcare is positioning its capability as a game changer in image interpretation, unlocking the most optimal therapies for patients. As a result of the pandemic, a significant backlog of patients emerged with postponed oncology screenings and delayed treatment. More advanced cancers are more difficult to treat, therefore again giving AI an opportunity to shine, given its potential to help clinicians triage patients with more advanced, harder-to-treat cancer stages. In addition, AI technologies can help streamline the interpretation of complex imaging data, enhance early detection through improved analysis of screening results, and assist in identifying effective treatment pathways more swiftly.
Read more about GE Healthcare’s acquisition of MIM software, a global provider of medical imaging analysis and AI solutions for the practice of radiation oncology, molecular radiotherapy, diagnostic imaging, and urology at imaging centers, hospitals, specialty clinical, and research organizations worldwide.
Managing AI’s imperfections on the path to the Holy Grail
All of this of course raises questions around data security, integrity, and generalizability given that AI is an imperfect tool capable of making mistakes. The good news is the data already exists and more is being collected everyday. As digital solutions continue to advance, so too will the regulations and oversight. For more information about the FDA’s involvement, check out the newly developed Digital Health Center of Excellence.
Haley Schwartz also takes an interesting approach to the emergence of AI technologies in healthcare. She shares that while AI enables access to more data, it is now imperative to focus on the quality of data and noise reduction. Consumers have allowed this tech into many aspects of their lives including wearable devices such as smart watches, which has created a stepping stone for more advanced AI to become adopted within hospital walls. As market appetite and demand are already present, commercializing AI in healthcare is now rooted in clinician trust to drive adoption, alongside logistics including liability, reimbursement, and regulatory clearance.
Haley Schwartz is a passionate commercialization expert with over 10 years of sales and marketing experience in medtech. She started Catalyze Healthcare to work with medical device companies struggling to commercialize in the U.S. by focusing on developing marketing plans, launch plans, go-to-market strategies, competitive analysis, pricing models and more. Contact her at firstname.lastname@example.org or via LinkedIn for guidance on your next product launch.
Filed Under: AI Meets Life Sci, MDO, Podcast