OBJECTIVE: This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application's impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text.
METHODS: A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers.
RESULTS: When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing.
CONCLUSIONS: AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.
STUDY DESIGN: An observational study.
PLACE AND DURATION OF STUDY: Pediatric Oncology Ward, Shaukat Khanum Cancer Hospital, Lahore, from January 2015 to July 2017.
METHODOLOGY: Patients aged 1-15 years, diagnosed with ALL, were included. Studied variables were cytogenetic type and MRD outcome in patients with ALL. Patients under one year of age and more than 15 years, or those having comorbidities, were excluded.
RESULTS: Total 150 patients' data were retrieved from the Hospital database. One hundred and thirty-three belonged to age 1 to 5 years group (89%) and 17 (11%) were in 5 to 10 years group. The mean age of the patient was 4.3 +3.1 years. One hundred and two (68%) were males; whereas, 48 (32%) were females. Pre B acute lymphoblastic leukemia was diagnosed in 139 (93%) patients and 11(7%) were diagnosed with Pre T acute lymphoblastic leukemia. Standard risk was observed in 120 (80%) patients and 30 (20%) patients were on high risk as per National Cancer Institute (NCI) Guidelines. Regimen A was used in 125 (83.3%), Regimen B in 16 (10.7%), and Regimen C in 9 (6%) patients. BCR-ABL was positive in 2 (1.30%), TEL-AML in 68 (45%), MLL in 5 (3.30%), and normal in 54 (36%). MRD at day 29 was negative in 40 (93%) and positive in 3 (7%). The karyotyping was done in 128 (85%) patients, out of which 68 (53%) were hyperploids, 41 (32%) euploid, and 19 (15%) were hypoploid. Death was observed in 22 (15%) patients. Nineteen (86%) deaths were due to fungal and bacterial sepsis; and disease-related deaths were noted in 3 (14%) patients.
CONCLUSION: The role of MRD and cytogenetics in risk assessment has improved in the early prognosis determination.