Artificial Intelligence in Pathology

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.

Utilizing Artificial Intelligence Application for Diagnosis of Oral Lesions and Assisting Young Oral Histopathologist in Deriving Diagnosis from Provided Features - A Pilot study

Background

AI in healthcare services is advancing every day, with a focus on uprising cognitive capabilities. Higher cognitive functions in AI entail performing intricate processes like decision-making, problem-solving, perception, and reasoning. This advanced cognition surpasses basic data handling, encompassing skills to grasp ideas, understand and apply information contextually, and derive novel insights from previous experiences and acquired knowledge. ChatGPT, a natural language processing model, exemplifies this evolution by engaging in conversations with humans, furnishing responses to inquiries.

Objective

We aimed to understand the capability of ChatGPT in solving doubts pertaining to symptoms and histological features related to subject of oral pathology. The study's objective is to evaluate ChatGPT's effectiveness in answering questions pertaining to diagnoses.

Methods

This cross-sectional study was done using an AI-based ChatGPT application that provides free service for research and learning purposes. The current version of ChatGPT3.5 was used to obtain responses for a total of 25 queries. These randomly asked questions were based on basic queries from patient aspect and early oral histopathologists. These responses were obtained and stored for further processing. The responses were evaluated by five experienced pathologists on a four point liekart scale. The score were further subjected for deducing kappa values for reliability.

Result & Statistical Analysis

A total of 25 queries were solved by the program in the shortest possible time for an answer. The sensitivity and specificity of the methods and the responses were represented using frequency and percentages. Both the responses were analysed and were statistically significant based on the measurement of kappa values.

Conclusion

The proficiency of ChatGPT in handling intricate reasoning queries within pathology demonstrated a noteworthy level of relational accuracy. Consequently, its text output created coherent links between elements, producing meaningful responses. This suggests that scholars or students can rely on this program to address reasoning-based inquiries. Nevertheless, considering the continual advancements in the program's development, further research is essential to determine its accuracy levels in future versions.

Artificial Intelligence in pathology: current applications, limitations, and future directions

PURPOSE

Given AI's recent success in computer vision applications, majority of pathologists anticipate that it will be able to assist them with a variety of digital pathology activities. Massive improvements in deep learning have enabled a synergy between Artificial Intelligence (AI) and deep learning, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to eliminate errors and save pathologists time.

AIMS

In this paper, we will discuss the components that went into the use of Artificial Intelligence in Pathology, its use in the medical profession, the obstacles and constraints that it encounters, and the future possibilities of AI in the medical field.

CONCLUSIONS

Based on these factors, we elaborate upon the use of AI in medical pathology and provide future recommendations for its successful implementation in this field.

Establishing priorities for implementation of large language models in pathology and laboratory medicine

Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.

[Artificial intelligence in pathological anatomy]

The review presents key concepts and global developments in the field of artificial intelligence used in pathological anatomy. The work examines two types of artificial intelligence (AI): weak and strong ones. A review of experimental algorithms using both deep machine learning and computer vision technologies to work with WSI images of preparations, diagnose and make a prognosis for various malignant neoplasms is carried out. It has been established that weak artificial intelligence at this stage of development of computer (digital) pathological anatomy shows significantly better results in speeding up and refining diagnostic procedures than strong artificial intelligence having signs of general intelligence. The article also discusses three options for the further development of AI assistants for pathologists based on the technologies of large language models (strong AI) ChatGPT (PathAsst), Flan-PaLM2 and LIMA. As a result of the analysis of the literature, key problems in the field were identified: the equipment of pathology institutions, the lack of experts in training neural networks, the lack of strict criteria for the clinical viability of AI diagnostic technologies.

ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios

BACKGROUND

Artificial intelligence (AI)-based tools can reshape healthcare practice. This includes ChatGPT which is considered among the most popular AI-based conversational models. Nevertheless, the performance of different versions of ChatGPT needs further evaluation in different settings to assess its reliability and credibility in various healthcare-related tasks. Therefore, the current study aimed to assess the performance of the freely available ChatGPT-3.5 and the paid version ChatGPT-4 in 10 different diagnostic clinical microbiology case scenarios.

METHODS

The current study followed the METRICS (Model, Evaluation, Timing/Transparency, Range/Randomization, Individual factors, Count, Specificity of the prompts/language) checklist for standardization of the design and reporting of AI-based studies in healthcare. The models tested on December 3, 2023 included ChatGPT-3.5 and ChatGPT-4 and the evaluation of the ChatGPT-generated content was based on the CLEAR tool (Completeness, Lack of false information, Evidence support, Appropriateness, and Relevance) assessed on a 5-point Likert scale with a range of the CLEAR scores of 1-5. ChatGPT output was evaluated by two raters independently and the inter-rater agreement was based on the Cohen's κ statistic. Ten diagnostic clinical microbiology laboratory case scenarios were created in the English language by three microbiologists at diverse levels of expertise following an internal discussion of common cases observed in Jordan. The range of topics included bacteriology, mycology, parasitology, and virology cases. Specific prompts were tailored based on the CLEAR tool and a new session was selected following prompting each case scenario.

RESULTS

The Cohen's κ values for the five CLEAR items were 0.351-0.737 for ChatGPT-3.5 and 0.294-0.701 for ChatGPT-4 indicating fair to good agreement and suitability for analysis. Based on the average CLEAR scores, ChatGPT-4 outperformed ChatGPT-3.5 (mean: 2.64±1.06 vs. 3.21±1.05, P=.012, t-test). The performance of each model varied based on the CLEAR items, with the lowest performance for the "Relevance" item (2.15±0.71 for ChatGPT-3.5 and 2.65±1.16 for ChatGPT-4). A statistically significant difference upon assessing the performance per each CLEAR item was only seen in ChatGPT-4 with the best performance in "Completeness", "Lack of false information", and "Evidence support" (P=0.043). The lowest level of performance for both models was observed with antimicrobial susceptibility testing (AST) queries while the highest level of performance was seen in bacterial and mycologic identification.

CONCLUSIONS

Assessment of ChatGPT performance across different diagnostic clinical microbiology case scenarios showed that ChatGPT-4 outperformed ChatGPT-3.5. The performance of ChatGPT demonstrated noticeable variability depending on the specific topic evaluated. A primary shortcoming of both ChatGPT models was the tendency to generate irrelevant content lacking the needed focus. Although the overall ChatGPT performance in these diagnostic microbiology case scenarios might be described as "above average" at best, there remains a significant potential for improvement, considering the identified limitations and unsatisfactory results in a few cases.

H&E image analysis pipeline for quantifying morphological features

Detecting cell types from histopathological images is essential for various digital pathology applications. However, large number of cells in whole-slide images (WSIs) necessitates automated analysis pipelines for efficient cell type detection. Herein, we present hematoxylin and eosin (H&E) Image Processing pipeline (HEIP) for automatied analysis of scanned H&E-stained slides. HEIP is a flexible and modular open-source software that performs preprocessing, instance segmentation, and nuclei feature extraction. To evaluate the performance of HEIP, we applied it to extract cell types from ovarian high-grade serous carcinoma (HGSC) patient WSIs. HEIP showed high precision in instance segmentation, particularly for neoplastic and epithelial cells. We also show that there is a significant correlation between genomic ploidy values and morphological features, such as major axis of the nucleus.

How to use AI in pathology

AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still need to transition to a digital workflow to be able to enjoy the benefits of AI. In this perspective, we explain the major benefits of AI in pathology, highlight key requirements that need to be met and example how to use it in a typical workflow.

Digital Versus Conventional Teaching of Surgical Pathology: A Comparative Study

OBJECTIVE

To compare the digital method and the conventional method of teaching surgical pathology to medical students.

METHODS

A prospective case-control study was conducted on second-year students during the period of August 20, 2022, through January 15, 2023. Students, divided into two groups of 45 each, were taught surgical pathology via both conventional and digital methods. Four specimens and four slides were taught in total to the same set of students. A pre-test and a post-test were used to evaluate students' performance and the impact of the teaching method. The answers were analyzed using a paired t-test. In the end, students' responses were obtained regarding their views on a better method of teaching on a Likert scale.

RESULTS

To study gross pathology, 50.7% of students were in favor of the digital method, and 21% were not in favor. For the microscopic examination of tissues, 56.92% of students were in favor of the digital method, and 15% were not in favor. There was a significant increase in post-test scores (12.54-9.79 = 2.75, p=0.007) when digital methods for teaching surgical pathology were applied.

CONCLUSION

The Likert scale demonstrated that the digital method of teaching surgical pathology not only improved student performance but also resulted in a better understanding of the subject.

Digital Pathology: Transforming Diagnosis in the Digital Age

In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.

Applicability of ChatGPT in Assisting to Solve Higher Order Problems in Pathology

Background Artificial intelligence (AI) is evolving for healthcare services. Higher cognitive thinking in AI refers to the ability of the system to perform advanced cognitive processes, such as problem-solving, decision-making, reasoning, and perception. This type of thinking goes beyond simple data processing and involves the ability to understand and manipulate abstract concepts, interpret, and use information in a contextually relevant way, and generate new insights based on past experiences and accumulated knowledge. Natural language processing models like ChatGPT is a conversational program that can interact with humans to provide answers to queries. Objective We aimed to ascertain the capability of ChatGPT in solving higher-order reasoning in the subject of pathology. Methods This cross-sectional study was conducted on the internet using an AI-based chat program that provides free service for research purposes. The current version of ChatGPT (January 30 version) was used to converse with a total of 100 higher-order reasoning queries. These questions were randomly selected from the question bank of the institution and categorized according to different systems. The responses to each question were collected and stored for further analysis. The responses were evaluated by three expert pathologists on a zero to five scale and categorized into the structure of the observed learning outcome (SOLO) taxonomy categories. The score was compared by a one-sample median test with hypothetical values to find its accuracy. Result A total of 100 higher-order reasoning questions were solved by the program in an average of 45.31±7.14 seconds for an answer. The overall median score was 4.08 (Q1-Q3: 4-4.33) which was below the hypothetical maximum value of five (one-test median test p <0.0001) and similar to four (one-test median test p = 0.14). The majority (86%) of the responses were in the "relational" category in the SOLO taxonomy. There was no difference in the scores of the responses for questions asked from various organ systems in the subject of Pathology (Kruskal Wallis p = 0.55). The scores rated by three pathologists had an excellent level of inter-rater reliability (ICC = 0.975 [95% CI: 0.965-0.983]; F = 40.26; p < 0.0001). Conclusion The capability of ChatGPT to solve higher-order reasoning questions in pathology had a relational level of accuracy. Hence, the text output had connections among its parts to provide a meaningful response. The answers from the program can score approximately 80%. Hence, academicians or students can get help from the program for solving reasoning-type questions also. As the program is evolving, further studies are needed to find its accuracy level in any further versions.

Diabetic wounds and artificial intelligence: A mini-review

Diabetic wound takes longer time to heal due to micro and macro-vascular ailment. This longer healing time can lead to infections and other health complications. Foot ulcers are one of the most common diabetic wounds. These are one of the leading cause of amputations. Medical science is continuously striving for improving quality of human life. A recent trend of amalgamation of knowledge, efforts and technological advancement of medical science experts and artificial intelligence researchers, has made tremendous success in diagnosis, prognosis and treatment of a variety of diseases. Diabetic wounds are no exception, as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner. The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science, for diabetic wound healing. Framework for diabetic wound assessment using artificial intelligence is presented. Moreover, this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients. The article also discusses the future directions for the betterment of the field that can lead to facilitate both, clinician and patients.

Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls

The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.

Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis

Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI.

Diagnostic Value of MAML2 Rearrangements in Mucoepidermoid Carcinoma

Mucoepidermoid carcinoma (MEC) is often seen in salivary glands and can harbor MAML2 translocations (MAML2+). The translocation status has diagnostic utility as an objective confirmation of the MEC diagnosis, for example, when distinction from the more aggressive adenosquamous carcinoma (ASC) is not straightforward. To assess the diagnostic relevance of MAML2, we examined our 5-year experience in prospective testing of 8106 solid tumors using RNA-seq panel testing in combinations with a two-round Delphi-based scenario survey. The prevalence of MAML2+ across all tumors was 0.28% (n = 23/8106) and the majority of MAML2+ cases were found in head and neck tumors (78.3%), where the overall prevalence was 5.9% (n = 18/307). The sensitivity of MAML2 for MEC was 60% and most cases (80%) were submitted for diagnostic confirmation; in 24% of cases, the MAML2 results changed the working diagnosis. An independent survey of 15 experts showed relative importance indexes of 0.8 and 0.65 for "confirmatory MAML2 testing" in suspected MEC and ASC, respectively. Real-world evidence confirmed that the added value of MAML2 is a composite of an imperfect confirmation test for MEC and a highly specific exclusion tool for the diagnosis of ASC. Real-world evidence can help move a rare molecular-genetic biomarker from an emerging tool to the clinic.

[Artificial Intelligence: Infrastructures and Prerequisites at European Level]

The application of artificial intelligence (AI) is often associated with the use of large amounts of data for the construction of AI models and algorithms. This data should ideally comply with the FAIR Data principles, i.e. being findable, accessible, interoperable and reusable. However, the handling of health data poses a particular challenge in this context. In this article, we highlight the challenges of the data usage for AI in medicine using the example of anaesthesia and intensive care medicine. We discuss the current situation but also the obstacles for a wider application of AI in medicine in Europe and give suggestions how to solve the different issues. The article covers different subjects like data protection, research data infrastructures and approval of medical products. Finally, this article shows how it can nevertheless be possible to establish a secure and at the same time effective handling of data for use in AI at the European level despite its unneglectable difficulties.

[EMPAIA-ecosystem for pathology diagnostics with AI assistance]

Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have already been established (analysis of prostate sections, ER, PR, and Her2 in breast cancer). Besides the still low use of virtual microscopy in practice, there are a number of hurdles that stand in the way of a rapid diffusion of AI applications. The EMPAIA project has a goal of removing these hurdles. The path taken to build an ecosystem for this purpose is intended to exemplify that the introduction of AI applications in image-based diagnostics is possible on a broad basis if the existing hurdles are removed in a joint, moderated process. The components of the EMPAIA ecosystem and its strategy will be described, and reference will be made to the technical solution approaches.

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.