%0 Journal Article
%T Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4¡ªPreliminary Results
%A Savvas Hirides
%A Petros Hirides
%A Kouloufakou Kalliopi
%A Constantinos Hirides
%J Surgical Science
%P 169-181
%@ 2157-9415
%D 2024
%I Scientific Research Publishing
%R 10.4236/ss.2024.153017
%X <b><span style=\"font-family:Verdana;\">Introduction: </span></b><span style=\"font-family:Verdana;\">Ultrafast latest developments in artificial
intelligence (¦¡¦©) have recently multiplied concerns regarding the future of
robotic autonomy in surgery. However, the literature on the topic is still
scarce.</span><span style=\"font-family:&quot;\"> </span><b><span style=\"font-family:Verdana;\">Aim:</span></b><span style=\"font-family:Verdana;\"> To test a novel AI commercially available tool for image analysis on a
series of laparoscopic scenes.</span><span style=\"font-family:&quot;\"> </span><b><span style=\"font-family:Verdana;\">Methods:</span></b><span style=\"font-family:Verdana;\"> The research tools included OPENAI CHATGPT 4.0 with its corresponding
image recognition plugin which was fed with a list of 100 laparoscopic selected
snapshots from common surgical procedures. In order to score reliability of
received responses from image-recognition bot, two corresponding scales were
developed ranging from 0</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">-</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">5. The set of images was
divided into two groups: unlabeled (Group A) and labeled (Group B), and
according to the type of surgical procedure or image resolution.</span><span style=\"font-family:&quot;\"> </span><b><span style=\"font-family:Verdana;\">Results:</span></b><span style=\"font-family:Verdana;\"> AI was
able to recognize correctly the context of surgical-related images in 97% of
its reports. For the labeled surgical pictures, the image-processing bot scored
3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of
the procedure were commented in detail, after all successful interpretations.
With rates 4</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">-</span><span style=\"font-family:&quot;\"> </span><span style=\"font-family:Verdana;\">5/5, the </span><span style=\"font-family:Verdana;\">c</span><span style=\"font-family:&quot;\"><span style=\"font-family:Verdana;\">hatbot was able to talk in
detail about the indications, contraindications, stages, instrumentation,
complications and outcome rates of the operation discussed. </span><b><span style=\"font-family:Verdana;\">Conclusion:</span></b> <span style=\"font-family:Verdana;\">Interaction between surgeon and chatbot appears to be an
interesting frontend for further research by clinicians in parallel with
evolution of its complex underlying infrastructure. In
%K Artificial Intelligence
%K Surgery
%K Image Recognition
%K Autonomous Surgery
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=132196