Impact of artificial intelligence on pathologists' decisions: an experiment

Computational pathology: an evolving concept

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial

BACKGROUND

Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics.

OBJECTIVES

To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion.

METHODS

This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data.

RESULTS

In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively.

CONCLUSIONS

The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.

Harnessing artificial intelligence for prostate cancer management

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

ChatGPT as an aid for pathological diagnosis of cancer

Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.

[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.

Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.

Organizational preparedness for the use of large language models in pathology informatics

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges.

Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study

BACKGROUND

In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted.

METHODS

We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM^® SPSS^® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351.

RESULTS

Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods.

CONCLUSION

Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.

Machine learning in renal pathology

Introduction

When assessing kidney biopsies, pathologists use light microscopy, immunofluorescence, and electron microscopy to describe and diagnose glomerular lesions and diseases. These methods can be laborious, costly, fraught with inter-observer variability, and can have delays in turn-around time. Thus, computational approaches can be designed as screening and/or diagnostic tools, potentially relieving pathologist time, healthcare resources, while also having the ability to identify novel biomarkers, including subvisual features.

Methods

Here, we implement our recently published biomarker feature extraction (BFE) model along with 3 pre-trained deep learning models (VGG16, VGG19, and InceptionV3) to diagnose 3 glomerular diseases using PAS-stained digital pathology images alone. The BFE model extracts a panel of 233 explainable features related to underlying pathology, which are subsequently narrowed down to 10 morphological and microstructural texture features for classification with a linear discriminant analysis machine learning classifier. 45 patient renal biopsies (371 glomeruli) from minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) were split into training/validation and held out sets. For the 3 deep learningmodels, data augmentation and Grad-CAM were used for better performance and interpretability.

Results

The BFE model showed glomerular validation accuracy of 67.6% and testing accuracy of 76.8%. All deep learning approaches had higher validation accuracies (most for VGG16 at 78.5%) but lower testing accuracies. The highest testing accuracy at the glomerular level was VGG16 at 71.9%, while at the patient-level was InceptionV3 at 73.3%.

Discussion

The results highlight the potential of both traditional machine learning and deep learning-based approaches for kidney biopsy evaluation.

How does the artificial intelligence-based image-assisted technique help physicians in diagnosis of pulmonary adenocarcinoma? A randomized controlled experiment of multicenter physicians in China

OBJECTIVE

Although artificial intelligence (AI) has achieved high levels of accuracy in the diagnosis of various diseases, its impact on physicians' decision-making performance in clinical practice is uncertain. This study aims to assess the impact of AI on the diagnostic performance of physicians with differing levels of self-efficacy under working conditions involving different time pressures.

MATERIALS AND METHODS

A 2 (independent diagnosis vs AI-assisted diagnosis) × 2 (no time pressure vs 2-minute time limit) randomized controlled experiment of multicenter physicians was conducted. Participants diagnosed 10 pulmonary adenocarcinoma cases and their diagnostic accuracy, sensitivity, and specificity were evaluated. Data analysis was performed using multilevel logistic regression.

RESULTS

One hundred and four radiologists from 102 hospitals completed the experiment. The results reveal (1) AI greatly increases physicians' diagnostic accuracy, either with or without time pressure; (2) when no time pressure, AI significantly improves physicians' diagnostic sensitivity but no significant change in specificity, while under time pressure, physicians' diagnostic sensitivity and specificity are both improved with the aid of AI; (3) when no time pressure, physicians with low self-efficacy benefit from AI assistance thus improving diagnostic accuracy but those with high self-efficacy do not, whereas physicians with low and high levels of self-efficacy both benefit from AI under time pressure.

DISCUSSION

This study is one of the first to provide real-world evidence regarding the impact of AI on physicians' decision-making performance, taking into account 2 boundary factors: clinical time pressure and physicians' self-efficacy.

CONCLUSION

AI-assisted diagnosis should be prioritized for physicians working under time pressure or with low self-efficacy.