Article Figures & Data
Figures
- Figure 1.
The difference in familiarity with ChatGPT among CLS and DCLS learners. Clinical laboratory science (CLS), n =13; Doctorate in Clinical Laboratory Science (DCLS), n = 10.
- Figure 2.
The difference in the usefulness of ChatGPT for future careers between CLS and DCLS learners. Clinical laboratory science (CLS), n = 11; Doctorate in Clinical Laboratory Science (DCLS), n = 10.
Tables
- Table 1.
Descriptive analysis of knowledge about ChatGPT across various educational programs
Questions Program (n) Score Mean (SD) U P CI 1. Familiarity with ChatGPT CLS (13) 2.4 (0.9) 106 .008* (0.48–1.98) DCLS (10) 1.2 (1.0) Program (n) Correct Response, n (%) U P CI 2. Use of ChatGPT beyond academic paper writing CLS (11) 5 (45) 36 .12 (−0.70 to 0.12) DCLS (10) 8 (80) 3. Use of ChatGPT beyond academic paper writing CLS (10) 8 (80) 55 .65 (−0.29 to 0.50) DCLS (10) 7 (70) 4. Limitations of ChatGPT CLS (10) 2 (20) 55 .58 (−.20 to 0.43) DCLS (10) 1 (10) 5. Capabilities of ChatGPT CLS (9) 1 (11) 23 .04* (−0.81 to −0.06) DCLS (10) 6 (60) ↵* Statistically significant at 0.05 level. CI: 95% bias-corrected and accelerated bootstrap confidence interval.
- Table 2.
Descriptive analysis of attitudes toward ChatGPT across various educational programs
Questions Program (n) Yes, n (%) U P CI 1. Is CLS education currently the most impacted by ChatGPT? CLS (11) 5 (45) 47 .54 (−0.58 to 0.29) DCLS (10) 6 (60) 2. Should ChatGPT be integrated into? CLS (12) 2 (17) 37.5 .04* (−0.41 to −0.03) DCLS (10) 5 (50) ↵* Statistically significant at 0.05 level. CI: 95% bias-corrected and accelerated bootstrap confidence interval.
- Table 3.
The primary concerns and major challenges associated with integrating ChatGPT in CLS/DCLS education
Questions CLE DCLS n % n % What are the greatest concerns? Inaccurate outputs 11 100 10 50 Academic integrity 11 55 10 80 Negative impacts on learning 11 36 10 60 Legal/ethical issues 11 18 10 40 Diversity, equity, and inclusions 11 9 10 10 What are the biggest challenges? Technical expertise 10 70 10 90 Limited availability of training data 10 80 10 30 Transparency 10 60 10 50 Bias and fairness 10 50 10 50 - Table 4.
Descriptive analysis of ChatGPT use in CLS/DCLS education across various educational programs
Questions Program (n) Yes, n (%) U P CI 1. Have you used ChatGPT in your coursework? CLS (13)
DCLS (10)5 (38)
0 (0)90 .03* (0.13–0.67) ↵* Statistically significant at 0.05 level. CI: 95% bias-corrected and accelerated bootstrap confidence interval.
- Table 5.
What MLS/DCLS functions in the clinical laboratory could ChatGPT or similar software be most useful for?
Functions n % MLS functions (n = 8) Identifying abnormal patient results 4 50 Recognizing trends in QC data 7 87.5 Interpreting test results 5 62.5 Monitoring patient outcomes 3 37.5 Reducing human errors 5 62.5 Preventing use of unnecessary tests 5 62.5 DCLS function (n = 10) Developing patient education materials 8 80 Developing algorithms 6 60 Doing literature searches 6 60 Laboratory use projects 5 50 Developing presentations 4 40 Developing SOAP notes 4 40 - Table 6.
Focus group discussion: advantages and disadvantages of using ChatGPT/AI—feedback from CLS and DCLS participants
CLS Participants (n = 13) Advantages • User-friendly interface
• Prompt responses to queries
• Ability to generate both textual content and images
Disadvantages • Accuracy may vary
• Information provided need further verification
DCLS Participants (n = 5) Advantages • Efficiency: rapid generation of outline or algorithms
• Research support: valuable for synthesizing and summarizing large volumes of information
• Consultation aid: serves as a tool for exploring ideas or gathering initial insights
• Inclusivity: multilingual capabilities make it accessible to diverse users
• Innovation: promotes integration of emerging technologies in education and practice
Disadvantages • Accuracy concerns: potential for incorrect or misleading information, especially in complex scenarios.
• Critical thinking limitations: lacks the ability to evaluate context or consider interferences in lab tests.
• Algorithm inadequacy: may recommend tests or solutions without adequate reasoning
• Overuse risks: can lead to unnecessary tests or reliance on AI without focused direction
• Reliability issues: misinterpretation of results could lead to unreliable outcomes in decision-making
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