Global citizens will be armed with a deeper understanding of their medical conditions. Credit: Chadchai Krisadapong via Shutterstock.

GlobalData predicts that 2024 will be the year of the GenAI killer app, and it is most likely to be medically related.

The ChatGPT phenomenon is entering its second phase: a shift from a large language model (LLM) focus and investment to the application layer.

Applications in this space are going to become far more complex, reflecting the use of multiple LLM-powered agents that will power advanced apps and automatically route prompts to the most appropriate data sources. As a result, the industry will see an emphasis on new forms of data access to simplify the workflow layer for vastly improving common business processes, and significantly enriching the user experience (UX).

App funding to increase

Silicon Valley based venture capitalists (VCs) have indicated that major funding will now shift to the application. VCs and leading cloud platform providers invested heavily in 2023 for LLMs–infrastructure services that are computational and capital-intensive to build. This year, attention will be turned to the app layer of GenAI innovation.

New compelling applications will support an uncomplicated UX for providing an aging populations and their caregivers with significantly improved access to medical professionals, services, and benefits. Global citizens will be armed with a deeper understanding of their medical conditions and more direct routes towards available services and appropriate care.

The technology will largely be enabled through data integration and management, featuring the consolidation of data from various sources within a unified data model. The concept of access to multiple data systems at this level, coupled with AI-injected automation, has never been realized.

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Numerous indications point to a strong tendency towards the creation of advanced apps, beginning within the medical field. Recent research reveals that following a survey of consumers’ medical questions, a panel of physicians preferred medical answers generated by an LLM over answers provided by their colleagues (source: US Medical Licensing Examination, USMLE).