Applicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology

Background Artificial intelligence (AI) is evolving for healthcare services. Higher cognitive thinking in AI refers to the ability of the system to perform advanced cognitive processes, such as problem-solving, decision-making, reasoning, and perception. This type of thinking goes beyond simple data...

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Bibliographic Details
Published in:Curēus (Palo Alto, CA) CA), 2023-02, Vol.15 (2), p.e35237-e35237
Main Authors: Sinha, Ranwir K, Deb Roy, Asitava, Kumar, Nikhil, Mondal, Himel
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Chatbots
Cognition & reasoning
Confidence intervals
Data collection
Decision making
Educational objectives
Healthcare Technology
Medical Education
Natural language processing
Nervous system
Pathology
Statistical analysis
Taxonomy
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Summary:Background Artificial intelligence (AI) is evolving for healthcare services. Higher cognitive thinking in AI refers to the ability of the system to perform advanced cognitive processes, such as problem-solving, decision-making, reasoning, and perception. This type of thinking goes beyond simple data processing and involves the ability to understand and manipulate abstract concepts, interpret, and use information in a contextually relevant way, and generate new insights based on past experiences and accumulated knowledge. Natural language processing models like ChatGPT is a conversational program that can interact with humans to provide answers to queries. Objective We aimed to ascertain the capability of ChatGPT in solving higher-order reasoning in the subject of pathology. Methods This cross-sectional study was conducted on the internet using an AI-based chat program that provides free service for research purposes. The current version of ChatGPT (January 30 version) was used to converse with a total of 100 higher-order reasoning queries. These questions were randomly selected from the question bank of the institution and categorized according to different systems. The responses to each question were collected and stored for further analysis. The responses were evaluated by three expert pathologists on a zero to five scale and categorized into the structure of the observed learning outcome (SOLO) taxonomy categories. The score was compared by a one-sample median test with hypothetical values to find its accuracy. Result A total of 100 higher-order reasoning questions were solved by the program in an average of 45.31±7.14 seconds for an answer. The overall median score was 4.08 (Q1-Q3: 4-4.33) which was below the hypothetical maximum value of five (one-test median test p
ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.35237