Performance Gains of LLMs With Humans in a World of LLMs Versus Humans
This addresses the problem of unsafe LLM integration in healthcare for patients and clinicians, but it is incremental as it shifts focus rather than introducing new methods.
The paper argues that current research comparing LLMs to human experts is flawed and potentially harmful, especially in clinical settings, and instead proposes focusing on strategies for humans and LLMs to work together symbiotically.
Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases. Without proper safeguards in place, LLMs will threaten to cause harm to the established structure of safe delivery of patient care which has been carefully developed throughout history to keep the safety of the patient at the forefront. A key driver of LLM innovation is founded on community research efforts which, if continuing to operate under "humans versus LLMs" principles, will expedite this trend. Therefore, research efforts moving forward must focus on effectively characterizing the safe use of LLMs in clinical settings that persist across the rapid development of novel LLM models. In this communication, we demonstrate that rather than comparing LLMs to humans, there is a need to develop strategies enabling efficient work of humans with LLMs in an almost symbiotic manner.