Perception of Medical Laboratory Professionals on the Role of Artificial Intelligence in Advancing Hematology Diagnostics.

Abstract

Background: Artificial Intelligence (AI) has grown quickly in healthcare and has had a big effect on medical laboratory diagnostics, especially when it comes to diagnosing blood disorders. To figure out the pros and cons of using AI in hematology diagnostics, it is important to understand the perspective of medical laboratory workers instead of specialists toward AI use in hematology laboratory diagnostics. The goal of this study was to investigate what medical lab workers now know about AI and how they think it affects the accuracy of diagnostic hematology and patient outcomes.

Methods: Methods involved 113 participants, mostly laboratory technologists who filled out a standardized questionnaire as part of a quantitative exploratory research design. The data collection phase lasted 6 months, during which time-informed consent was sought and reminders were provided to boost response rates. Statistical tests were used including t-tests and regression analysis to determine the links between demographic factors and workers opinions on AI in hematological diagnoses.

Results: The primary findings of this study dwell within the role of job skill and gender influence on "attitude", specifically, the study found that most of the participants believe that their professional attitude to adoption of AI in diagnoses could make their job more accurate and faster. However, there were several concerns about the quality of data, how easy it will be to understand the model, and the moral implications. There were positive relationships between knowledge, attitudes, and practices linked to AI. The secondary outcomes showed that most of the participants were young, with 45.1% being between the ages of 30 and 39. In addition, 65.5% of them had bachelor's degree, which suggests that they were comfortable with technology. Some of the participants had less than ten years of work experience, while others had more than ten years. Statistical tests demonstrated that demographic characteristics are quite important in how medical laboratory workers think about AI.

Conclusions: Medical laboratory workers believe that the use of AI in hematology diagnostics has its benefits, but they need more training and assistance to deal with their fears and create a space where human expertise and AI technologies can work together. By taking these views into account, healthcare organizations may better educate their staff for the changing role of AI in diagnostics, which will lead to improved patient outcomes and satisfaction.