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Leadership and Sustainability in a Globalized World: Emerging   A systematic review of machine learning for digital stain
 Trends, Global Contexts, Key Challenges, and Effective Strategies  processing in pathology








 Author(s):                                            Author(s):


 Javed, M. (Rabat Business School, Department of Management Sciences, International University of Rabat; COMSATS   Al-Qudah, R. (Abu Dhabi University, College of Engineering, Department of Computer Science and Information
 University Islamabad, Sahiwal Campus), Bilal, B. (Durham University Business School, Durham University; Department   Technology), Bala, A. (Monash University Malaysia, School of Business), AlMuhajiri, M. (Saudi Electronic University,
 of Financial Intelligence, University of South Africa), Mouri, N. (Rabat Business School, Department of Management   College of Computing and Informatics), Zakaria, K. (Abu Dhabi University, College of Engineering, Department of
 Sciences, International University of Rabat), Faisal, M. (College of Business, Abu Dhabi University)  Computer Science and Information Technology), Suen, C.Y. (Concordia University, Gina Cody School of Engineering
                                                  and Computer Science)
 Index Terms:
                                                     Index Terms:
 climate action; gender equality; grand societal challenges; innovation; leadership; sustainability
        Chemical equipment; E-learning; Generative adversarial networks; Learning systems; Pathology; Reagents; Adversarial
 Abstract:  networks; Chemical method; Chemical staining; Digital pathologies; Digital staining; Machine-learning; Skilled personnel;
        Systematic Review; Toxic reagents; Virtual staining; artificial intelligence; augmentation index; chemical analysis; dark-

 This systematic review analyzes 109 studies from top business journals, exploring the connection between leadership   field microscopy; deep learning; generative adversarial network; Giemsa stain; histopathology; human; image analysis;
 and sustainability, including key leadership styles, emerging trends, and challenges in integrating sustainability into   immunohistochemistry; machine learning; nonhuman; pathology; perception; Review; signal noise ratio; stain; systematic
 organizational strategies and operations. Our analysis reveals responsible leadership, followed by transformational   review; taxonomy; training; workflow; Surveying
 and female leadership, as the most common styles used to drive the integration of sustainability into organizational
 strategies  and  operations.  Core  sustainability  practices  are  more  closely  aligned  with  addressing  grand  societal   Abstract:
 challenges, particularly “climate action” followed by “quality education,” “innovation,” and “gender equality.” The study
 highlights key challenges facing the leadership in driving sustainability include stakeholders’ resistance to change,
 financial constraints, capacity building, and regulatory hurdles. Overcoming these requires raising stakeholder   Digital staining involves using methods such as Machine Learning (ML) to replace chemical staining in pathology.
 awareness,  aligning  sustainability  with  business  goals,  leveraging  cross-sectoral  collaboration,  and  regulatory   Staining adds contrast that makes cell details more visible under the microscope. However, chemical methods are
 harmonization. This study highlights the transformative role of leadership in promoting sustainability, emphasizing   slow, use toxic reagents, and require skilled personnel. In contrast, digital staining can generate images faster, reduce
 cross-sectoral learning to drive meaningful change.  the need for reagents and specialized equipment, and minimize plastic and chemical waste, making the workflow more
        sustainable. This paper systematically reviews papers published on ML-based digital stain processing. We propose a
 Read the paper  new taxonomy that divides existing studies into five groups: stain normalization, stain augmentation, virtual staining,
        stain transformation, and hybrid approaches. In addition, we observed several trends from the reviewed papers.
                                         Finally, we outline open research directions.


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