Systematic Analysis of Artificial Intelligence in Pathology

Introduction: Artificial intelligence (AI) is an emerging modern technology within the health care sectors in the current era and it is the ability of computer software to mimic human judgment. Artificial intelligence (AI)-based modern image analysis methods have significant promise for enhancing the accuracy and efficacy of pathology diagnostic processes as well as for the discovery of new biomarkers. Objectives: In this article, we will discuss Artificial Intelligence, its usage in pathology in various ways such as for screening of various diseases, detection of prognostic markers or biomarkers, and various treatment modalities. Materials and Methods: Data were collected and analyzed from the recently published literature and electronic database searches of Cochrane and included the articles the year 2017 to 2021 by reading the title and the abstract. Artificial intelligence (AI), has a lot of potential for aiding in diagnosis with the advancement of information technology. For this purpose, few machine learning algorithms have been created to date. Given their capacity to evaluate complicated data in a quantitative and standardised manner to further improve the precision and scope of diagnoses, artificial intelligence (AI) or machine learning technologies hold great promise for the field of pathology. Conclusion: The application of Artificial Intelligence tools in pathology has sharply increased in this era and it is anticipated to revolutionize the pathology field in the years ahead and can change the way the field of pathology is managed and make them not only more systematic but also effective in meeting the needs of the current age of precision medicine.

Perceptions of First-Year Medical Students on the Use of Whole SlideImaging in Learning Histology

Background: Traditional practice in histology teaching is to use the optical microscope for examination of the slides. Whole slide imaging (WSI) or virtual microscopy is an innovation that uses the scanned images of the histology slides that can be seen in any device that can be connected to the internet. WSI allows the user to pan and zooms the slide just like in a microscope, and the quality of the image is also reported to be superior to an optical microscope. The aim of the study was to assess the first-year medical students’ perceptions on the use of whole slide imaging in learning histology slides. Settings and Design is Cross-sectional, questionnaire-based survey. Subjects and Methods : Students of phase I MBBS were the study participants. Practical sessions on the histology of the gastrointestinal tract were conducted using the whole slide imaging. Using a 10 item questionnaire, feedback was obtained at the end of the teaching sessions. Statistical analysis used Descriptive statistics were used to explain the data.Results: The students showed a positive response in embracing this new mode of histology teaching. There was uniform support to the fact that the image quality and ease of use of the pan and zoom feature were useful in identifying details of the tissues.Conclusions:WSI was accepted with enthusiasm as a much-needed innovation in histology learning. If not a supplant, WSI can be used as an adjunct to traditional glass slide teaching using an optical microscope

Randomized cross-over study and a qualitative analysis comparing virtual microscopy and light microscopy for learning undergraduate histopathology

Background: Virtual microscopy (VM) use in teaching and learning is increasing worldwide. However, there is a paucity of information comparing it to light microscopy (LM) in learning undergraduate histopathology. We investigated whether VM or LM had a higher impact on student learning and performance in histopathology. In addition, we investigated whether students preferred VM over LM, and whether VM use provided a platform to fulfill the Accreditation Council for Graduate Medical Education core competencies. Materials and Methods: We used a sequential exploratory mixed method study design. A qualitative phase inquiring about student preference for VM or LM was followed by a randomized cross-over study. Student preference was measured by an online survey based on a Likert scale. In the cross-over study, students were randomized to either the VM or the LM arm, and their mean scores in standardized exams were compared after using VM and LM. Results: A total of 152 students completed the qualitative study and a total of 64 students participated in the cross-over study. Eighty-three percent (83%) of the students preferred to use VM over LM. Students who used VM scored significantly (P < 0.001) higher [(87.1% vs. 72.4%) and (85.3% vs. 76.1%)], respectively, in both phases of the cross-over study compared to those who used LM. Conclusions: Using VM to learn histopathology has significantly increased student learning and performance compared to using LM.