ABSTRACT
The tribal health system in India faces unique challenges in comparison to non-tribal health in the nation and global healthcare systems. The tribal health issues are distinct due to the diverse socio-cultural practices, rituals, customs, and languages of the tribal communities. Despite commendable efforts, there are several obstacles that hinder the successful delivery of healthcare services to these underserved populations. These challenges include geographical remoteness and limited infrastructure, language, and cultural barriers; scarcity of healthcare professionals; socioeconomic disparities; and the need for cultural sensitivity and integration of traditional healing practices. Overcoming these challenges requires collaborative efforts between the government, medical specialists, and the indigenous tribes themselves. By addressing these obstacles, it is possible to enhance the accessibility, quality, and cultural appropriateness of healthcare services for tribal groups, leading to improved health outcomes and reduced health inequalities.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Background Dermatophytosis is a public health concern in tropical countries. In India, a scalable number of dermatophytosis cases from multiple states are reported. In the eastern part of India, very few studies were published assessing the clinicomycological profiles of patients. Hence, we conducted this study to ascertain the clinicomycological profile of patients suffering from dermatophytosis with special reference to associated socio-environmental factors. Materials and methods This cross-sectional observational study was conducted in a tertiary care hospital situated in Bihar state of India from January 2021 to December 2021. We included a total of 330 patients of all age groups who were clinically diagnosed with superficial mycosis from the Department of Dermatology and sent for investigations to the Department of Microbiology. The collected specimens from the lesions were prepared with wet potassium hydroxide and examined under the microscope. Then, the specimens were inoculated and incubated at 25°C for up to four weeks. Fungal isolates were identified by gross appearance and microscopy if growth was observed. Results Among the 330 patients, 186 (56.4%) were males and 144 (43.6%) were females. The majority of the patients (54.5%) were from the low socioeconomic group and living in overcrowded places. Direct microscopy was positive in 198 (60%) patients, and culture was positive in 68 (20.61%) patients. The majority of the patients who were found positive in direct microscopy were from the age group of 21-30 years (39.9%), followed by 1-10 years (25.25%). A total of 92 (46.4%) cases were of tinea capitis, followed by 68 (34.3%) patients of tinea corporis. Trichophyton was the predominant fungus isolated, and Trichophyton mentagrophytes was the most common species (52.6%). Conclusion Tinea capitis was the most common provisionally diagnosed dermatophytosis in our tertiary care hospital in Bihar, an Indian state in its eastern zone. Low socioeconomic status and poor personal hygiene were the factors associated with the high prevalence of dermatophyte infections in this region of India. A detailed analysis of all these epidemiological factors is needed to limit the prevalence of dermatophytosis in tropical regions.
ABSTRACT
Background Syringoma is a benign adnexal neoplasm and is considered safe with very low malignant potential. However, multiple tiny lesions typically affect the face and exposed area, which may cause a cosmetic concern for the patient. After a clinical diagnosis, there are two methods to diagnose syringoma: fine needle aspiration cytology (FNAC) and histopathology. FNAC is generally used for the initial evaluation of syringoma, while histopathology is used as a confirmatory test to diagnose syringoma. In developing and resource-limited settings, the combination of FNAC and histopathology would cause a financial and logistics burden. Objective This study aimed to observe the cytological and histopathological features of cases clinically diagnosed as syringomas in a tertiary care hospital to suggest the use of either FNAC or histopathology for diagnosing syringoma. Materials and Methods This cross-sectional observational study was conducted in the Department of Dermatology and Department of Pathology of a tertiary care hospital in eastern India from November 2021 to April 2022. Any clinically provisionally diagnosed case of syringoma was recruited for the study after obtaining informed consent for voluntary participation. With aseptic precautions, the tissue aspirates and punch biopsy were obtained in the Department of Dermatology and the samples were sent to the Department of Pathology. Cytological and histological examination was conducted by a single expert pathologist. Result A total of 50 cases (36 female, 14 male) with a median age of 23 years (range 10-40 years) were included in the study. A total of 43 cases were presented with papular lesions and seven with nodules. In the majority of the cases (40%), the lesion was in the eyelid followed by 26% in the arm. In FNAC, 22 cases were found to be benign adnexal lesions, 16 were suggestive of syringoma, eight were diagnosed as xanthoma, two were diagnosed as warts, and two cases were inadequate for opinion. Histologically, 42 cases were confirmed as syringoma, six were diagnosed as xanthoma, and two cases were diagnosed as warts. There was a significant difference between diagnosis by FNAC and histopathology (McNemar χ2 = 24.038, p-value = 0.0001). Conclusion We found that FNAC and histopathological diagnosis of syringoma may not be corroborative. Benign adnexal lesions are difficult to categorize by FNAC. Histopathological examination of clinically diagnosed cases of syringoma is of help for definitive diagnosis. Hence, FNAC may be avoided for saving time and discomfort for the patients and clinically diagnosed cases may be diagnosed by histopathological examination.
