Ahmed Alhassan Ali, Brychcy Agnieszka, Abouzid Mohamed, Witt Martin, Kaczmarek Elżbieta
Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland.
Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland.
J Pers Med. 2023 Jun 7;13(6):962. doi: 10.3390/jpm13060962.
In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted.
We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM SPSS Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351.
Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, = 0.043). Finally, significant differences ( = 0.003) were observed in determining the liability for legal issues used by AI and ML methods.
Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.
在过去的二十年里,已经开发了几种人工智能(AI)和机器学习(ML)模型来辅助医学诊断、决策制定和治疗方案设计。波兰在职病理学家数量较少,这延长了肿瘤患者的诊断和治疗过程。因此,应用人工智能和机器学习可能有助于这一过程。因此,我们的研究旨在调查波兰病理学家在临床领域使用人工智能和机器学习方法的知识。据我们所知,尚未进行过类似的研究。
我们于2022年6月至7月对波兰的病理学家进行了一项横断面研究。问卷包括关于人工智能或机器学习知识、经验、专业、个人想法以及对人工智能和机器学习在医学诊断不同方面的认同程度的自我报告信息。使用IBM SPSS Statistics v.26、PQStat Software v.1.8.2.238和RStudio Build 351对数据进行分析。
总体而言,波兰的68名病理学家参与了我们的研究。他们的平均年龄和经验年限分别为38.92±8.88岁和12.78±9.48年。约42%的人使用人工智能或机器学习方法,这表明从未使用过的人与使用过的人在知识差距上存在显著差异(OR = 17.9,95% CI = 3.57 - 89.79,<0.001)。此外,人工智能使用者在医学诊断过程中对人工智能速度表示满意的几率更高(OR = 4.66,95% CI = 1.05 - 20.78,= 0.043)。最后,在确定人工智能和机器学习方法所涉及法律问题的责任方面观察到显著差异(= 0.003)。
本研究中的大多数病理学家未使用人工智能或机器学习模型,这凸显了提高对在医学诊断中应用人工智能和机器学习的认识及开展相关教育项目的重要性。