Artificial Intelligence-based online platform assists blood cell morphology learning: A mixed-methods sequential explanatory designed research

Abstract

Background

The study aimed to evaluate the effectiveness of learning blood cell morphology by learning on our Artificial intelligence (AI)-based online platform.

Methods

Our study is based on mixed-methods sequential explanatory design and crossover design. Thirty-one third-year medical students were randomly divided into two groups. The two groups had platform learning and microscopy learning in diferent sequences with pretests and posttests, respectively. Students were interviewed, and the records were coded and analyzed by NVivo 12.0.

Results

For both groups, test scores increased significantly after online-platform learning. Feasibility was the most mentioned advantage of the platform. The AI system could inspire the students to compare the similarities and differences between cells and help them understand the cells better. Students had positive perspectives on the online-learning platform.

Conclusion

The AI-based online platform could assist medical students in blood cell morphology learning. The AI system could function as a more knowledgeable other (MKO) and guide the students through their zone of proximal development (ZPD) to achieve mastery. It could be an effective and beneficial complement to microscopy learning. Students had very positive perspectives on the AI-based online learning platform. It should be integrated into the course and curriculum to facilitate the students.

Practice points

• The AI-based online platform could assist medical students in blood cell morphology learning.

• The AI system could function as an MKO and guide the students through their ZPD to achieve mastery.

• The AI-based online platform could be an effective and beneficial complement to microscopy learning.

• The AI-based online platform should be integrated into the curriculum to facilitate the students.

Keywords:

• Artificial intelligence
• online platform
• blood cell morphology learning

Acknowledgements

We thank President Xiao HaiPeng and Vice President Kuang Ming of the First Affiliated Hospital of Sun Yat-sen University for supporting medical education and the development of medical teachers. We thank the Faculty Development Centre for Health Professions Education for assistance with the study. We thank Professor Trevor Gibbs and Professor Hossam Hamdy for their advice and suggestions on the study.

Author contributions

LJX, LM, and DT conceptualized the study. LJX, ZF, WZG, and GX were involved in the smear scanning and cell classification. LJX, OJ, LJ, and ZF were involved in the study’s design. LJX and ZF were responsible for data collection and were involved in coding and data analysis. LJX produced the first draft of the paper. All authors contributed to the iterative drafting and refinement of the manuscript. All authors approved the final version of the manuscript for submission.

Disclosure statement

Zhigang Wang is the CEO of DeepCyto LLC. Xin Guo is a senior engineer at DeepCyto LLC.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Junxun Li

Junxun Li, Master of Medicine, is a hematologist and an associate professor in Medical Laboratory Faculty. He has been actively involved in medical education for various levels of students and trainees for over 18 years. He became an associate fellow of AMEE and was awarded an AMEE Specialist in Medical Education in 2019.

Juan Ouyang

Juan Ouyang, MD, is a vice director of the Medical Laboratory Department at First Affiliated Hospital, Sun Yat-sen University.

Juan Liu

Juan Liu, MD, is an endocrinologist and an associate professor at the First Affiliated Hospital, Sun Yat-sen University. She became an associate fellow of AMEE and was awarded an AMEE Specialist in Medical Education in 2019.

Fan Zhang

Fan Zhang, Master of Medicine, is an attending doctor in Laboratory Medicine. He has been involved in medical education for over 15 years.

Zhigang Wang

Zhigang Wang, MD, is the CEO of DeepCyto LLC.

Xin Guo

Xin Guo, Bachelor of Science, is a senior engineer at DeepCyto LLC.

Min Liu

Min Liu, Bachelor of Medicine, is the director of the Medical Laboratory Faculty and the director of the Medical Laboratory Department of the First Affiliated Hospital, Sun Yat-sen University.

David Taylor

David Taylor, MD, is a professor of medical education and physiology. He is the Director of the Center for Leadership and Innovation in Health Professions Education at Gulf Medical University.