  With the introduction of ChatGPT, Large Language Models (LLMs) have received
enormous attention in healthcare. Despite their potential benefits, researchers
have underscored various ethical implications. While individual instances have
drawn much attention, the debate lacks a systematic overview of practical
applications currently researched and ethical issues connected to them. Against
this background, this work aims to map the ethical landscape surrounding the
current stage of deployment of LLMs in medicine and healthcare. Electronic
databases and preprint servers were queried using a comprehensive search
strategy. Studies were screened and extracted following a modified rapid review
approach. Methodological quality was assessed using a hybrid approach. For 53
records, a meta-aggregative synthesis was performed. Four fields of
applications emerged and testify to a vivid exploration phase. Advantages of
using LLMs are attributed to their capacity in data analysis, personalized
information provisioning, support in decision-making, mitigating information
loss and enhancing information accessibility. However, we also identifies
recurrent ethical concerns connected to fairness, bias, non-maleficence,
transparency, and privacy. A distinctive concern is the tendency to produce
harmful misinformation or convincingly but inaccurate content. A recurrent plea
for ethical guidance and human oversight is evident. Given the variety of use
cases, it is suggested that the ethical guidance debate be reframed to focus on
defining what constitutes acceptable human oversight across the spectrum of
applications. This involves considering diverse settings, varying potentials
for harm, and different acceptable thresholds for performance and certainty in
healthcare. In addition, a critical inquiry is necessary to determine the
extent to which the current experimental use of LLMs is necessary and
justified.
