Evaluating ChatGPT's Ability to Solve Higher-Order Questions on the Competency-Based Medical Education Curriculum in Medical Biochemistry.

Background Healthcare-related artificial intelligence (AI) is developing. The capacity of the system to carry out sophisticated cognitive processes, such as problem-solving, decision-making, reasoning, and perceiving, is referred to as higher cognitive thinking in AI. This kind of thinking requires more than just processing facts; it also entails comprehending and working with abstract ideas, evaluating and applying data relevant to the context, and producing new insights based on prior learning and experience. ChatGPT is an artificial intelligence-based conversational software that can engage with people to answer questions and uses natural language processing models. The platform has created a worldwide buzz and keeps setting an ongoing trend in solving many complex problems in various dimensions. Nevertheless, ChatGPT's capacity to correctly respond to queries requiring higher-level thinking in medical biochemistry has not yet been investigated. So, this research aimed to evaluate ChatGPT's aptitude for responding to higher-order questions on medical biochemistry. Objective In this study, our objective was to determine whether ChatGPT can address higher-order problems related to medical biochemistry.​​​​​​ Methods​​​ This cross-sectional study was done online by conversing with the current version of ChatGPT (14 March 2023, which is presently free for registered users). It was presented with 200 medical biochemistry reasoning questions that require higher-order thinking. These questions were randomly picked from the institution's question bank and classified according to the Competency-Based Medical Education (CBME) curriculum's competency modules. The responses were collected and archived for subsequent research. Two expert biochemistry academicians examined the replies on a zero to five scale. The score's accuracy was determined by a one-sample Wilcoxon signed rank test using hypothetical values. Result The AI software answered 200 questions requiring higher-order thinking with a median score of 4.0 (Q1=3.50, Q3=4.50). Using a single sample Wilcoxon signed rank test, the result was less than the hypothetical maximum of five (p=0.001) and comparable to four (p=0.16). There was no difference in the replies to questions from different CBME modules in medical biochemistry (Kruskal-Wallis p=0.39). The inter-rater reliability of the scores scored by two biochemistry faculty members was outstanding (ICC=0.926 (95% CI: 0.814-0.971); F=19; p=0.001)​​​​​​ Conclusion The results of this research indicate that ChatGPT has the potential to be a successful tool for answering questions requiring higher-order thinking in medical biochemistry, with a median score of four out of five. However, continuous training and development with data of recent advances are essential to improve performance and make it functional for the ever-growing field of academic medical usage.

Ghosh A ，Bir A 《Cureus》

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 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 <0.0001) and similar to four (one-test median test p = 0.14). The majority (86%) of the responses were in the "relational" category in the SOLO taxonomy. There was no difference in the scores of the responses for questions asked from various organ systems in the subject of Pathology (Kruskal Wallis p = 0.55). The scores rated by three pathologists had an excellent level of inter-rater reliability (ICC = 0.975 [95% CI: 0.965-0.983]; F = 40.26; p < 0.0001). Conclusion The capability of ChatGPT to solve higher-order reasoning questions in pathology had a relational level of accuracy. Hence, the text output had connections among its parts to provide a meaningful response. The answers from the program can score approximately 80%. Hence, academicians or students can get help from the program for solving reasoning-type questions also. As the program is evolving, further studies are needed to find its accuracy level in any further versions.

Sinha RK ，Deb Roy A ，Kumar N ，Mondal H ... -  《Cureus》

Assessing the Capability of ChatGPT in Answering First- and Second-Order Knowledge Questions on Microbiology as per Competency-Based Medical Education Curriculum.

Background and objective ChatGPT is an artificial intelligence (AI) language model that has been trained to process and respond to questions across a wide range of topics. It is also capable of solving problems in medical educational topics. However, the capability of ChatGPT to accurately answer first- and second-order knowledge questions in the field of microbiology has not been explored so far. Hence, in this study, we aimed to analyze the capability of ChatGPT in answering first- and second-order questions on the subject of microbiology. Materials and methods Based on the competency-based medical education (CBME) curriculum of the subject of microbiology, we prepared a set of first-order and second-order questions. For the total of eight modules in the CBME curriculum for microbiology, we prepared six first-order and six second-order knowledge questions according to the National Medical Commission-recommended CBME curriculum, amounting to a total of (8 x 12) 96 questions. The questions were checked for content validity by three expert microbiologists. These questions were used to converse with ChatGPT by a single user and responses were recorded for further analysis. The answers were scored by three microbiologists on a rating scale of 0-5. The average of three scores was taken as the final score for analysis. As the data were not normally distributed, we used a non-parametric statistical test. The overall scores were tested by a one-sample median test with hypothetical values of 4 and 5. The scores of answers to first-order and second-order questions were compared by the Mann-Whitney U test. Module-wise responses were tested by the Kruskall-Wallis test followed by the post hoc test for pairwise comparisons. Results The overall score of 96 answers was 4.04 ±0.37 (median: 4.17, Q1-Q3: 3.88-4.33) with the mean score of answers to first-order knowledge questions being 4.07 ±0.32 (median: 4.17, Q1-Q3: 4-4.33) and that of answers to second-order knowledge questions being 3.99 ±0.43 (median: 4, Q1-Q3: 3.67-4.33) (Mann-Whitney p=0.4). The score was significantly below the score of 5 (one-sample median test p<0.0001) and similar to 4 (one-sample median test p=0.09). Overall, there was a variation in median scores obtained in eight categories of topics in microbiology, indicating inconsistent performance in different topics. Conclusion The results of the study indicate that ChatGPT is capable of answering both first- and second-order knowledge questions related to the subject of microbiology. The model achieved an accuracy of approximately 80% and there was no difference between the model's capability of answering first-order questions and second-order knowledge questions. The findings of this study suggest that ChatGPT has the potential to be an effective tool for automated question-answering in the field of microbiology. However, continued improvement in the training and development of language models is necessary to enhance their performance and make them suitable for academic use.

Das D ，Kumar N ，Longjam LA ，Sinha R ，Deb Roy A ，Mondal H ，Gupta P ... -  《Cureus》

Is ChatGPT's Knowledge and Interpretative Ability Comparable to First Professional MBBS (Bachelor of Medicine, Bachelor of Surgery) Students of India in Taking a Medical Biochemistry Examination?

Introduction ChatGPT is a large language model (LLM)-based chatbot that uses natural language processing to create humanlike conversational dialogue. It has created a significant impact on the entire global landscape, especially in sectors like finance and banking, e-commerce, education, legal, human resources (HR), and recruitment since its inception. There have been multiple ongoing controversies regarding the seamless integration of ChatGPT with the healthcare system because of its factual accuracy, lack of experience, lack of clarity, expertise, and above all, lack of empathy. Our study seeks to compare ChatGPT's knowledge and interpretative abilities with those of first-year medical students in India in the subject of medical biochemistry. Materials and methods A total of 79 questions (40 multiple choice questions and 39 subjective questions) of medical biochemistry were set for Phase 1, block II term examination. Chat GPT was enrolled as the 101st student in the class. The questions were entered into ChatGPT's interface and responses were noted. The response time for the multiple-choice questions (MCQs) asked was also noted. The answers given by ChatGPT and 100 students of the class were checked by two subject experts, and marks were given according to the quality of answers. Marks obtained by the AI chatbot were compared with the marks obtained by the students. Results ChatGPT scored 140 marks out of 200 and outperformed almost all the students and ranked fifth in the class. It scored very well in information-based MCQs (92%) and descriptive logical reasoning (80%), whereas performed poorly in descriptive clinical scenario-based questions (52%). In terms of time taken to respond to the MCQs, it took significantly more time to answer logical reasoning MCQs than simple information-based MCQs (3.10±0.882 sec vs. 2.02±0.477 sec, p<0.005). Conclusions ChatGPT was able to outperform almost all the students in the subject of medical biochemistry. If the ethical issues are dealt with efficiently, these LLMs have a huge potential to be used in teaching and learning methods of modern medicine by students successfully.

Ghosh A ，Maini Jindal N ，Gupta VK ，Bansal E ，Kaur Bajwa N ，Sett A ... -  《Cureus》

Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study.

ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.

Yu P ，Fang C ，Liu X ，Fu W ，Ling J ，Yan Z ，Jiang Y ，Cao Z ，Wu M ，Chen Z ，Zhu W ，Zhang Y ，Abudukeremu A ，Wang Y ，Liu X ，Wang J ... -  《-》