Dorothy Wilson, Abigail Kusi-Amponsah Diji, Richard Marfo, Paulina Amoh, Precious Adade Duodu, Samuel Akyirem, Douglas Gyamfi, Hayford Asare, Jerry Armah, Nancy Innocentia Ebu Enyan, Joana Kyei-Dompim
Dietary adherence among persons with type 2 diabetes: A concurrent mixed methods study
PloS one
background large language models llms chatgpt4 gemini microsoft copilot instrumental various domains including healthcare enhance health literacy aid patient decisionmaking given complexities involved breast imaging procedures accurate comprehensible information vital patient engagement compliance study aims evaluate readability accuracy information provided three prominent llms chatgpt4 gemini microsoft copilot response frequently asked questions breast imaging assessing potential improve patient understanding facilitate healthcare communication methodology collected common questions breast imaging clinical practice posed llms evaluated responses terms readability accuracy responses llms analyzed readability using flesch reading ease fleschkincaid grade level tests accuracy radiologistdeveloped likerttype scale results study found significant variations among llms gemini microsoft copilot scored higher readability scales p 0001 indicating responses easier understand contrast chatgpt4 demonstrated greater accuracy responses p 0001 conclusions llms chatgpt4 show promise providing accurate responses readability issues may limit utility patient education conversely gemini microsoft copilot despite less accurate accessible broader patient audience ongoing adjustments evaluations models essential ensure meet diverse needs patients emphasizing need continuous improvement oversight deployment artificial intelligence technologies healthcare