At LSI Europe '23, the premier executive-focused medtech conference in Europe, the transformative potential of large language models (LLMs) and generative AI in healthcare was a major topic of discussion. One panel, titled “Hype or Hope: Opportunities, Challenges, & Potential Impact of LLMs & Foundation Models in Medicine,” featured Manish Kothari, President of First Spark Ventures; Shabbi Khan, Partner at Foley and Lardner LLP; and Lu Zhang, Founder and Managing Partner of Fusion Fund. In this article, we reflect on that conversation and key insights shared on the state of generative AI, its applications in medtech, and future prospects.

Generative AI: Hype or Reality?

Shabbi Khan opened the discussion by questioning whether generative AI is merely the latest centerpiece of a media-driven hype cycle, or poised for true impact. Manish Kothari responded with a demonstration of its widespread usage, particularly among younger generations. He highlighted that while healthy skepticism exists, the broad adoption of tools like ChatGPT indicates that generative AI is becoming an integral part of various sectors, soon to include healthcare. "Clearly, it's not purely a hype cycle," Kothari noted. He emphasized that generative AI is fundamentally transforming the way humans interact with technology, starting with the chat and search interface and, which will extend to medtech and healthtech as well.

Generational Differences in AI Adoption

Kothari pointed out a significant generational gap in the usage of AI technologies. He shared an anecdote about his 15-year-old daughter using ChatGPT to generate multiple essay drafts, which she then edits. This contrasts with the limited use of AI in medicine, where seasoned practitioners are still wrestling with its potential. Kothari emphasized the need to bridge this generational gap to harness the full potential of AI in medtech.

The Role of AI in Medical Workflows

One of the most pressing needs in healthcare, according to Kothari, is the automation of administrative tasks. He explained that doctors spend significant time responding to emails and managing electronic health records (EHRs) – tasks that could be streamlined with AI. "The number one use case doctors seem to be asking for is actually to answer their emails," he stated. By automating these tasks, healthcare professionals may be empowered and unburdened to focus more on patient care.

Beyond Clinical Workflow: Advanced Applications

Lu Zhang highlighted the broader applications of generative AI beyond simple clinical tasks. She cited its use in medical imaging, where AI can enhance low-resolution scans to high-resolution images quickly and accurately. Zhang also discussed AI's role in life sciences, aiding in designing clinical trials and improving digital biology. "AI could be a super effective tool to help us solve the efficiency issues out there," she said, emphasizing the potential for AI to revolutionize healthcare beyond care delivery.

Data Privacy and Regulatory Challenges

The panel also discussed the importance of data privacy and the need for regulation. Zhang stressed that the integration of AI into healthcare must consider data security and patient privacy. She advocated for federated learning as a solution to data sensitivity issues, allowing AI to process data without compromising privacy. "We have to think about it earlier than later," she urged, highlighting the necessity for proactive measures in data regulation.

The Future of Personalized Medicine

One of the most exciting prospects of AI in healthcare is its potential to enable personalized medicine. Both Kothari and Zhang emphasized that AI could provide highly individualized diagnostics and treatment plans based on comprehensive data analysis. Kothari illustrated this by mentioning new biomarkers identified through AI, which can lead to earlier and more accurate diagnoses. "We should be pretty aggressive about adopting new markers that never existed before," he stated.

Addressing Communication Gaps in Healthcare

Kothari and Zhang also discussed how AI can address fundamental communication mismatches between patients and healthcare providers. AI tools can help bridge the knowledge gap, making it easier for patients to understand their conditions and for doctors to provide personalized care. "Wearables and other communication devices can help bridge this fundamental challenge," Kothari noted.

The Consumer-Enterprise Dynamic

The panel highlighted a fascinating dynamic where consumer adoption of AI technologies often precedes enterprise adoption. Zhang noted that consumer familiarity with tools like ChatGPT has pushed enterprises, including healthcare providers and systems, to integrate AI into their operations. "The consumer push is now going to be followed by a massive enterprise push," Kothari predicted, marking an opportune time for investment in AI-driven healthcare solutions.

Embracing AI for a Better Future

The panel concluded with a consensus that generative AI holds transformative potential for the medtech industry. By addressing workflow inefficiencies, enhancing medical imaging, and enabling personalized medicine, AI can significantly improve healthcare delivery. However, this requires careful consideration of data privacy and proactive regulatory measures. As Kothari aptly summarized, "The best 10 years to invest in this is now after everybody has accepted it and understood it."

For more panels like this and content on AI in medtech and healthtech, visit the Resource Hub on our website. To participate in medtech market discussions like this with hundreds of medical device investors, innovators, strategics, and more, join us at LSI Europe ‘24 in Sintra, Portugal this September.