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Streamlining a Patchwork - Exploring the Challenges of Digital Transformation in Pathology: Ethnographic Study

Streamlining a Patchwork - Exploring the Challenges of Digital Transformation in Pathology: Ethnographic Study

Pathology departments worldwide are moving toward digital pathology due to improvements in storage and scanner technology as well as in the field of artificial intelligence (AI) [1,2] (refer to Textbox 1 [3-5] for definitions). A key analogue practice in pathology that is now being digitized via whole-slide imaging is diagnosis.

Birte Linny Geisler, Ourania Amperidou, Sina Patricia Pauly, Sven Mattern, Christian M Schürch, Claudia Hermann, Christiane Stoffregen, Ilona Steinleitner, Natali Paigin, Claudia Lamß, Falko Fend, Monika A Rieger, Esther Rind, Christine Preiser

J Med Internet Res 2025;27:e63366

Public Awareness of and Attitudes Toward the Use of AI in Pathology Research and Practice: Mixed Methods Study

Public Awareness of and Attitudes Toward the Use of AI in Pathology Research and Practice: Mixed Methods Study

In 2021, the first AI-based pathology software was approved by the US Food and Drug Administration to assist with the detection of prostate cancer [6].

Claire Lewis, Jenny Groarke, Lisa Graham-Wisener, Jacqueline James

J Med Internet Res 2025;27:e59591

Instagram as a Tool to Improve Human Histology Learning in Medical Education: Descriptive Study

Instagram as a Tool to Improve Human Histology Learning in Medical Education: Descriptive Study

The literature shows that this social network is being used for educational purposes in medical schools, predominantly in imaging-related subjects such as radiology [11], ophthalmology [12], dermatology [13], anatomy [14], fertility [15], pathology [16], plastic surgery [17], dentistry [18], and (with very few proposals) in histology [19].

Alejandro Escamilla-Sanchez, Juan Antonio López-Villodres, Carmen Alba-Tercedor, María Victoria Ortega-Jiménez, Francisca Rius-Díaz, Raquel Sanchez-Varo, Diego Bermúdez

JMIR Med Educ 2025;11:e55861

Artificial Intelligence in Lymphoma Histopathology: Systematic Review

Artificial Intelligence in Lymphoma Histopathology: Systematic Review

However, the advent of digital pathology has seen a shift toward the use of computers for reviewing and analyzing scanned whole slide images (WSIs). This transition is not only driven by the potential for increased efficiency but also opens new avenues for the development of automated diagnostic tools [7]. These tools have the potential to enhance the accuracy, efficiency, objectivity, and consistency of diagnoses, which is crucial in addressing the global shortage of pathologists.

Yao Fu, Zongyao Huang, Xudong Deng, Linna Xu, Yang Liu, Mingxing Zhang, Jinyi Liu, Bin Huang

J Med Internet Res 2025;27:e62851

Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study

Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study

A randomized, web-based, cross-sectional study was conducted among medical students and pathology residents in Jordan from 5 public universities—Jordan University of Science and Technology (JUST), The University of Jordan, Yarmouk University, Hashemite University, and Mutah University, including about 19,000 medical students—in addition to 4 pathology residency programs: JUST, King Hussein Cancer Center, Jordan University Hospital, and Royal Medical Services, including about 80 pathology residents.

Anwar Rjoop, Mohammad Al-Qudah, Raja Alkhasawneh, Nesreen Bataineh, Maram Abdaljaleel, Moayad A Rjoub, Mustafa Alkhateeb, Mohammad Abdelraheem, Salem Al-Omari, Omar Bani-Mari, Anas Alkabalan, Saoud Altulaih, Iyad Rjoub, Rula Alshimi

JMIR Med Educ 2025;11:e62669

The Promise of AI for Image-Driven Medicine: Qualitative Interview Study of Radiologists’ and Pathologists’ Perspectives

The Promise of AI for Image-Driven Medicine: Qualitative Interview Study of Radiologists’ and Pathologists’ Perspectives

Image-driven specialisms such as radiology and pathology are at the forefront of technological innovation in medicine, and many believe that artificial intelligence (AI) is the next innovation to reshape these fields [1-3]. AI refers to a broad range of machine-based systems designed to influence the environment by producing an output (predictions, recommendations, or decisions) for a given set of objectives [4].

Jojanneke Drogt, Megan Milota, Wouter Veldhuis, Shoko Vos, Karin Jongsma

JMIR Hum Factors 2024;11:e52514

AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review

AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review

This article presents the current state of knowledge on AI implementation in pathology in high-end laboratories, highlighting different approaches regarding datasets, preprocessing of images, and different approaches to image analysis. However, to our knowledge, no scoping review has been performed to compile the present knowledge and evidence on AI-supported digital microscopy diagnostics in PHC settings.

Joar von Bahr, Vinod Diwan, Andreas Mårtensson, Nina Linder, Johan Lundin

JMIR Res Protoc 2024;13:e58149

Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis

Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis

If the determined densities of the enamel, dentin, and pulp of the tooth do not correspond to the range of values for healthy tooth tissues, then it may indicate a pathology.

Aleksey Reshetnikov, Natalia Shaikhattarova, Margarita Mazurok, Nadezhda Kasatkina

JMIRx Med 2024;5:e56759