LLMs are not magic bullets
LLMs are looking especially promising, but definitive empirical validation isn’t out there yet. Additionally, enthusiasm must be tempered by realism about current limitations. LLMs are not magic bullet – they sometimes fail in the exact scenarios where IPC cannot afford errors (complex, high-stakes decisions). The literature urges an augmented approach: use LLMs to handle workload and crunch data, but keep humans in the loop for oversight, validation, and the nuanced decision-making that machines can’t replicate (Wiemken & Carrico 2024; Schwartz et al.2024)

With this approach, the IPC community can harness LLMs as “co-pilots” in infection control: the AI handles the mundane and provides preliminary analyses, while IPC teams remain the ultimate decision-makers ensuring patient safety. Going forward, we clearly can’t rest on our laurels – continued research, real-world evaluations, and interdisciplinary collaboration (between, e.g., AI developers, IPC experts, policy-makers, ethicists) will be crucial.

LLMs have arrived and, even if they are soon to be subsumed by more general artificial intelligence models, the linguistic component will remain front and centre. At every turn, however, it’s our prerogative to step back and question the co-pilot status of these “knowledge companions”: