ChatGPT in pathology.

No abstract available.

Pathology students' perceptions of virtual learning: A case study of students in Saudi Arabia.

BACKGROUND: Pathology laboratory classes are traditionally conducted using a conventional light microscope. The Coronavirus Disease 2019 (COVID-19) pandemic and recent technological advances necessitated remote learning through online classes using virtual slides (VS) instead of glass slides (GS). AIM: The purpose of this study was to gauge the perception of learning pathology using virtual slides (VS) as opposed to glass slides (GS) for medical students in Saudi Arabia. This study would help modify teaching methods with the advancement of the application of newer methods in online teaching. METHODS: This two-phased study evaluated learning outcomes and perceptions in pathology online education for medical students. Using a questionnaire, Phase one analyzed second and third-year students' perceptions of the teaching methods after an online pathology course. Phase Two assessed the learning outcomes of third-year students during online practical sessions using a pretest and post-test design. Statistical data were collected using a simple additive approach. Statistical tools were used to determine the factors affecting students' perceptions. RESULTS: The accessibility of VS at any possible time, location, or device was the most advantageous trait of virtual learning (mean = 2.94±0.9). Students agreed the least with virtual slides as the only optimal method of learning pathology (mean = 2.25±0.9). Most enjoyed the virtual lab experience (51.7%) but still prefer both laboratory-GS and virtual-VS classes (83.5%). CONCLUSIONS: VS had the benefit of accessibility and efficiency. The acceptance of VS was significantly affected by the orientation prior to the online class. Findings showed that VS cannot completely replace GS and more aspects such as technical difficulties and prior VS experience should be explored.

ChatGPT for digital pathology research.

The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.

Peer creation and sharing of mnemonics in collaborative documents for pathology education: a pilot study.

BACKGROUND: Mnemonic techniques are memory aids that could help improve memory encoding, storage, and retrieval. Using the brain's natural propensity for pattern recognition and association, new information is associated with something familiar, such as an image, a structure, or a pattern. This should be particularly useful for learning complex medical information. Collaborative documents have the potential to revolutionize online learning because they could increase the creativity, productivity, and efficiency of learning. The purpose of this study was to investigate the feasibility of combining peer creation and sharing of mnemonics with collaborative online documents to improve pathology education. METHODS: We carried out a prospective, quasi-experimental, pretest-posttest pilot study. The intervention group was trained to create and share mnemonics in collaborative documents for pathological cases, based on histopathological slides. The control group compared analog and digital microscopy. RESULTS: Both groups consisted of 41 students and did not reveal demographic differences. Performance evaluations did not reveal significant differences between the groups' pretest and posttest scores. Our pilot study revealed several pitfalls, especially in instructional design, time on task, and digital literacy, that could have masked possible learning benefits. CONCLUSIONS: There is a gap in evidence-based research, both on mnemonics and on CD in pathology didactics. Even though, the combination of peer creation and sharing of mnemonics is very promising from a cognitive neurobiological standpoint, and collaborative documents have great potential to promote the digital transformation of medical education and increase cooperation, creativity, productivity, and efficiency of learning. However, the incorporation of such innovative techniques requires meticulous instructional design by teachers and additional time for students to become familiar with new learning methods and the application of new digital tools to promote also digital literacy. Future studies should also take into account validated high-stakes testing for more reliable pre-posttest results, a larger cohort of students, and anticipate technical difficulties regarding new digital tools.

Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology.

The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.

Science, Medicine, and Cytology: an educational program of the Diversity, Equity, and Inclusion Committee of the American Society of Cytopathology.

INTRODUCTION: As our field of pathology continues to grow, our trainee numbers are on the decline. To combat this trend, the ASC Diversity, Equity, and Inclusion Committee established the Science, Medicine, and Cytology SumMer Certificate program to improve exposure to pathology/cytopathology with a focus on diversity, equity, and inclusion. Herein, we report our findings of the first 2 years of the program. MATERIALS AND METHODS: An online course was developed targeting students who are underrepresented in medicine at the high school and college level. It consisted of several didactic sessions, presenting the common procedures involving cytopathologists and cytologists. Interviews with cytopathologists were also included. Participants were surveyed for demographic information and provided course evaluations. RESULTS: In the first year of the program (2021), 34 participants completed the program, which increased to 103 in 2022. In both years there was a diversity in participant demographic backgrounds; however, only a minority of participants self-identified as being underrepresented in medicine. A vast majority (>85%) of participants in both years were high school or college students. In 2021, 100% of participants stated that the program format was effective and 94% thought the content was appropriate for their level of education; in 2022 the results were similar. In 2021, 66% considered health care as a potential career; this value increased in 2022 to 83%. In 2021 and 2022, 31% and 38%, respectively, considered cytology as a career. CONCLUSIONS: Evaluations were excellent, generating interest in cytopathology. Barriers in reaching underrepresented minorities exist and additional work is needed. Expansion to a wider audience may increase outreach.

[Developing pathology guidelines and consensus: methods and considerations].

Pathological diagnosis is vital in medicine. Developing and implementing high-quality pathology guidelines and consensus can enhance disease diagnosis accuracy and reduce unnecessary misdiagnosis and missed diagnoses. This article will cover the current status of pathology guidelines and consensus, methods for high-quality development, and the distinctions between them. Additionally, it will provide thoughts and suggestions for promoting their development in China.

[Scientific, transparent and applicable rankings of Chinese pathological guidelines and consensus published in the medical journals in 2022].

The STAR tool was used to evaluate and analyze the science, transparency, and applicability of Chinese pathology guidelines and consensus published in medical journals in 2022. There were a total of 18 pathology guidelines and consensuses published in 2022, including 1 guideline and 17 consensuses. The results showed that the guideline score was 21.83 points, lower than the overall guideline average (43.4 points). Consensus ratings scored an average of 27.87 points, on par with the overall consensus level (28.3 points). Areas that scored above the overall level were "conflict of interest" and "working groups", while areas that scored below the overall level were "proposals", "funding", "evidence", "consensus approaches" and "accessibility". To sum up, the formulation of pathology guidelines and consensuses in 2022 is not standardized, and the evidence retrieval process, evidence evaluation methods and grading criteria for recommendations on clinical issues are not provided in the formulation process; the process and method for reaching consensus are not provided, the plan is lacking, and registration is not carried out. It is therefore suggested that guidelines/consensus makers in the field of pathology should attach importance to evidence-based medical evidence, strictly follow guideline formulation methods and processes, further improve the scientific, applicable and transparent guidelines/consensuses in the field, and better provide support for clinicians and patients.

Scientific and Regulatory Policy Committee Points to Consider: Review of the United States Food and Drug Administration (FDA) Guidance on Pathology Peer Review in Nonclinical Toxicology Studies.

In December 2021, the United States Food and Drug Administration (FDA) issued the final guidance for industry titled Pathology Peer Review in Nonclinical Toxicology Studies: Questions and Answers. The stated purpose of the FDA guidance is to provide information to sponsors, applicants, and nonclinical laboratory personnel regarding the management and conduct of histopathology peer review as part of nonclinical toxicology studies conducted in compliance with good laboratory practice (GLP) regulations. On behalf of and in collaboration with global societies of toxicologic pathology and the Society of Quality Assurance, the Scientific and Regulatory Policy Committee (SRPC) of the Society of Toxicologic Pathology (STP) initiated a review of this FDA guidance. The STP has previously published multiple papers related to the scientific conduct of a pathology peer review of nonclinical toxicology studies and appropriate documentation practices. The objectives of this review are to provide an in-depth analysis and summary interpretation of the FDA recommendations and share considerations for the conduct of pathology peer review in nonclinical toxicology studies that claim compliance to GLP regulations. In general, this working group is in agreement with the recommendations from the FDA guidance that has added clear expectations for pathology peer review preparation, conduct, and documentation.

Complex In Vitro Model Characterization for Context of Use in Toxicologic Pathology: Use Cases by Collaborative Teams of Biologists, Bioengineers, and Pathologists.

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.

Synthetic Data and Its Utility in Pathology and Laboratory Medicine.

In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic data and how it may soon shape the landscape within our field. Using synthetic data presents many advantages but also introduces a milieu of new obstacles and limitations. This review aimed to provide pathologists and laboratory professionals with a primer on the general concept of synthetic data and its potential to transform our field. By leveraging synthetic data, we can help accelerate the development of various machine learning models and enhance our medical education and research/quality study needs. This review explored the methods for generating synthetic data, including rule-based, machine learning model-based and hybrid approaches, as they apply to applications within pathology and laboratory medicine. We also discussed the limitations and challenges associated with such synthetic data, including data quality, malicious use, and ethical bias/concerns and challenges. By understanding the potential benefits (ie, medical education, training artificial intelligence programs, and proficiency testing, etc) and limitations of this new data realm, we can not only harness its power to improve patient outcomes, advance research, and enhance the practice of pathology but also become readily aware of their intrinsic limitations.