An exploratory survey about using ChatGPT in education, healthcare, and research
Hosseini, M.; Gao, C. A.; Liebovitz, D. M.; Carvalho, A. M.; Ahmad, F. S.; Luo, Y.; MacDonald, N.; Holmes, K. L.; Kho, A.
Show abstract
ObjectiveChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by the population at large has sparked a wide range of discussions regarding its acceptable and optimal integration in different areas. In a hybrid (virtual and in-person) panel discussion event, we examined various perspectives regarding the use of ChatGPT in education, research, and healthcare. Materials and MethodsWe surveyed in-person and online attendees using an audience interaction platform (Slido). We quantitatively analyzed received responses on questions about the use of ChatGPT in various contexts. We compared pairwise categorical groups with Fishers Exact. Furthermore, we used qualitative methods to analyze and code discussions. ResultsWe received 420 responses from an estimated 844 participants (response rate 49.7%). Only 40% of the audience had tried ChatGPT. More trainees had tried ChatGPT compared with faculty. Those who had used ChatGPT were more interested in using it in a wider range of contexts going forwards. Of the three discussed contexts, the greatest uncertainty was shown about using ChatGPT in education. Pros and cons were raised during discussion for the use of this technology in education, research, and healthcare. DiscussionThere was a range of perspectives around the uses of ChatGPT in education, research, and healthcare, with still much uncertainty around its acceptability and optimal uses. There were different perspectives from respondents of different roles (trainee vs faculty vs staff). More discussion is needed to explore perceptions around the use of LLMs such as ChatGPT in vital sectors such as education, healthcare and research. Given involved risks and unforeseen challenges, taking a thoughtful and measured approach in adoption would reduce the likelihood of harm.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Ethical review of clinical research with generative AI: Evaluating ChatGPT’s accuracy and reproducibility 95%
- Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study 95%
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 95%
Similar papers in this journal
- The potential for digital patient symptom recording through symptom assessment applications to optimize patient flow and reduce waiting times in Urgent Care Centers: a simulation study 94%
- Exploring Patient and Staff Experiences of Video Consultations During COVID-19 in an English Outpatient Care Setting: Secondary Data Analysis of Routinely Collected Feedback Data 94%
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 94%
Similar papers in this journal
- Validating a Clinical Decision Support System for Palliative Care using healthcare professionals’ insights 95%
- The experiences of 33 national COVID-19 dashboard teams during the first year of the pandemic in the WHO European Region: a qualitative study 94%
- A digital self-care intervention for Ugandan patients with heart failure and their clinicians: User-centred design and usability study 93%
Similar papers in this journal
- Clinical code sets and the problem of redundancy in code set repositories 95%
- Introducing the 4Ps Model of Transitioning to Distance Learning: a convergent mixed methods study conducted during the COVID-19 pandemic 94%
- Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis 94%
Similar papers in this journal
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 95%
- Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway 94%
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.