Impact of Artificial Intelligence-Assisted Pathology on Patient

Healthcare: Literature Review

Achref Miry

, Mohammed Tbouda

, Kenza Oqbani

and Sanae Abbaoui

Pathology Department, Souss-Massa University Hospital, Agadir, Morocco

Faculty of Medicine and Pharmacy of Agadir, Agadir, Morocco

Keywords: Artificial Intelligence, Pathology, Digital Pathology.

Abstract: Artificial Intelligence (AI) in pathology, especially in immunohistochemistry (IHC), exceeds human visual

capabilities, enhancing precision and incorporating geographical aspects. Our research reviews the impact of

AI, focusing on improved diagnostic accuracy and speed. Digital pathology emerges as an innovative solution,

employing AI algorithms for tasks like segmentation and prognosis. Despite their benefits, challenges arise

in adopting AI and digital pathology in pathology labs. In conclusion, these advancements offer significant

benefits, enabling spatial analysis and providing accurate, impartial results, accessible remotely by

pathologists.

1 INTRODUCTION

Artificial intelligence (AI)–based pathology has

enabled us to investigate and obtain information that

goes beyond what humans can see with their eyes. It

enhances the precision of measurements and makes it

possible to consider the geographical aspects of data

by using special mathematical techniques (Jubb, A.

M., et al., 2014). Including spatial measurements in

immunohistochemistry (IHC) can enhance the

practical significance of methods for identifying

biomarkers (Carbone, D. P. et al., 2017).

2 METHODOLOGY

Our research consists of a thorough examination of

existing literature to explore the outcomes of

integrating artificial intelligence into pathology. Our

goal is to clarify how artificial intelligence affects the

accuracy and speed of diagnosing and predicting

outcomes in pathology.

https://orcid.org/0000-0001-5955-1614

https://orcid.org/0000-0002-9231-8774

https://orcid.org/0000-0002-0670-5939

https://orcid.org/0000-0009-4005-7566

3 RESULTS AND DISCUSSION

3.1 Digital Pathology Era

Efforts to address the difficulties encountered with

conventional pathology techniques have given rise to

the creation and acceptance of digital pathology, an

innovative imaging system in the digital age. Recent

advancements in pathology address challenges

through innovative imaging technologies and whole

slide image (WSI) scanners. These systems,

exemplified by Philips IntelliSite Pathology Solution

and Leica Aperio AT2 DX System, facilitate the

digitalization of pathology. WSI scanners rapidly

capture and digitally stitch multiple tissue section

images, allowing pathologists to review

comprehensive WSIs on computer monitors, marking

a transformative shift toward digital pathology

(Pantanowitz, L. et al., 2013) - (Food and Drug

Administration, 2019).

3.2 Quantitative Analysis of WSI

Digital image analysis in pathology swiftly and

accurately identifies and quantifies cell types,

Miry, A., Tbouda, M., Oqbani, K. and Abbaoui, S.

Impact of Artiﬁcial Intelligence-Assisted Pathology on Patient Healthcare: Literature Review.

DOI: 10.5220/0012709300003854

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International e-Health Forum (IeHF 2023), pages 5-7

ISBN: 978-989-758-711-5

evaluating histological features, morphological

patterns, and biologically relevant regions. This

surpasses manual assessments, saving time and

reducing human errors associated with tasks like

counting fatigue (Barisoni, L., et al., 2020), Neltner,

J. H. et al., 2012).

3.3 Artificial Intelligence and Machine

Learning

The increasing demand for data reproducibility and

the complexity of pathology analyses have driven the

integration of AI (Artificial Intelligence). AI,

employing algorithms, extracts information beyond

human visual perception (Aeffner, F. et al., 2019).

It rapidly analyzes extensive data, expediting the

discovery of novel histopathology features. In breast

cancer, unsupervised learning models differentiate

between tumor grades and assess prognostically

relevant morphological features. Deep learning,

applied to IHC- and H&E-stained specimens, impacts

various tumor types, including prostate cancer

grading, biomarker identification in melanoma,

breast cancer region detection, prediction of response

to therapy, and recurrence prediction in lung cancer.

It enhances interpretability through graph neural

networks, providing comprehensive tissue

representations (Aeffner, F. et al., 2019)-

Vamathevan, J. et al., 2019).

To assess AI's utility, comparisons with

pathologists are crucial. In classifying melanoma, a

CNN outperformed histopathologists. In detecting

breast cancer metastases, deep learning matched top

pathologists and excelled in finding micrometastases.

Machine learning aids tumor purity assessment,

surpassing visual estimates in accuracy. Combining

WSI with machine learning offers insights in

translational research, but requires vast, high-quality

data. Challenges arise from variability in histological

data across labs, impacting algorithm development.

Robust AI algorithms could mitigate inter-reader

variability, but further research is needed to

understand their impact fully (Tizhoosh, H. R. &

Pantanowitz, L., 2018).

3.3.1 Stratification of Patients

Digital pathology aids in predicting treatment

response and identifying responsive patients. Spatial

analysis, utilizing machine learning models, extracts

morphological details to distinguish responders to

therapies like nivolumab in NSCLC. Digital image

analysis quantifies CD8 and PD-L1 positive cell

densities, revealing potential predictive composite

biomarkers. AI and machine learning assist in tumor

classification and staging, with deep learning models

predicting patient survival more accurately than

traditional pathology practices (Wang X. et al., 2018),

(Barrera C. et al., 2018).

3.3.2 Applications in Diagnosis

Biomarker research in immune-oncology explores

predictive values in solid tumors, particularly the role

of PD-L1 expression in response to immune

checkpoint inhibitors (ICIs). Challenges in accurate

patient stratification using visual interpretation lead

to a shift towards digital scoring, aiding pathologists

with standardized metrics. Studies reveal AI and

digital techniques outperform or equal manual

assessments across tumor types. AI's role extends to

morphological assessments in nonalcoholic liver

diseases. FDA-approved platforms support

quantitative image analysis, showcasing potential in

molecular epidemiology studies and expanding

beyond HER2 detection to classify ER- and PR-

positive breast cancer subtypes (Langer, C. J. et al.,

2016).

3.3.3 Applications in Clinical Practice

Several resources provide guidelines for

pathologists and physicians in digital pathology,

covering image handling, WSI device standards,

validation, AI concepts, and reimbursement

considerations. The College of American

Pathologists offers comprehensive guidelines for

WSI system validation, emphasizing emulation,

sample size, concordance, and documentation. AI

performance relies on dataset size and quality,

requiring diverse, well-curated images for training

and validation, with pathologists playing a crucial

role in ensuring accuracy and relevance to clinical

applications (Pantanowitz, L. et al., 2014),

(Janowczyk, A. & Madabhushi, A., 2016).

4 COPYRIGHT FORM

The authors ensure that the publisher has the

authorization to publish the Contribution.

5 CONCLUSIONS

Digital pathology advancements provide practical

benefits, enhancing accuracy and enabling remote

image review. In immuno-oncology, AI and machine

IeHF 2023 - International e-Health Forum

learning contribute to a comprehensive understanding

of the tumor microenvironment (TME) by

consistently analyzing and validating large datasets.

This has implications for drug development and

clinical trial design. These technologies, applied in

clinical settings, describe features across patient

samples, facilitating more precise immune-oncology

therapies and enhancing diagnostic, prognostic, and

predictive decision-making in cancer treatment,

ultimately improving patient care.

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Impact of Artiﬁcial Intelligence-Assisted Pathology on Patient Healthcare: Literature Review