Attitudes toward artificial intelligence in pathology: a survey-based study of pathologists in northern India

Article information

J Pathol Transl Med. 2025;59(6):382-389

Publication date (electronic) : 2025 October 2

doi : https://doi.org/10.4132/jptm.2025.07.10

Manupriya Sharma^,1, Kavita Kumari², Navpreet³, Sushma Bharti⁴, Rajneesh Kumari²

¹Department of Pathology and Lab Medicine, All India Institute of Medical Sciences, Bilaspur, India

²Department of Pathology, Dr. Radhakrishnan Government Medical College, Hamirpur, Himachal Pradesh, India

³Department of Community and Family Medicine, All India Institute of Medical Sciences, Bilaspur, India

⁴Tata Main Hospital, Jamshedpur, Jharkhand, India

Corresponding Author: Manupriya Sharma, MD, DNB Department of Pathology and Lab Medicine, All India Institute of Medical Sciences, Bilaspur 174001, India Tel: +91-8628000105, Fax: E-mail: manupriya.priyasharma@gmail.com

Received 2025 February 16; Revised 2025 June 11; Accepted 2025 July 10.

Abstract

Graphical abstract

INTRODUCTION

Artificial intelligence (AI), defined as the simulation of human intelligence by machines, has become a transformative force in medicine, particularly in diagnostic disciplines such as pathology [1]. Historically, pathology has relied on light microscopy and manual interpretation of glass slides, making diagnostic processes labor-intensive and subjective. However, the advent of whole slide imaging (WSI) scanners has revolutionized the field by enabling digital pathology. WSI technology allows the digitization of glass slides into high-resolution images, providing enhanced opportunities for data storage, retrieval, and computational analysis that were not possible with traditional microscopy [2,3].

The transition from analog to digital workflows has been further supported by the widespread availability of high-speed internet, cloud storage, and a growing interest in telepathology, enabling remote diagnostic practices and collaboration. These advancements have laid the foundation for the integration of AI into pathology workflows, improving the precision and efficiency of diagnosis [4-6].

Among AI technologies, automated analysis of histopathological images, applications such as tumor detection, immunostaining evaluation, pattern recognition, and diagnostic classification have shown promising results. Additionally, AI tools have been employed for tasks requiring precision, such as tumor margin assessment and recommending diagnostic or genetic panels [7-11]. For example, based on morphological assessment, AI systems may recommend appropriate immunohistochemical stains or molecular/genetic panels to support accurate diagnosis and guide personalized treatment planning.

Despite its potential, AI adoption in pathology remains in its infancy, especially in resource-constrained settings like India, where challenges such as limited infrastructure, insufficient training, and lack of awareness persist [7,12]. Recognizing the importance of understanding AI’s role and adoption readiness, this study aims to assess pathologists’ knowledge, perceptions, and interest in AI applications. By analyzing responses from professionals with diverse backgrounds, the study identifies gaps and barriers to AI adoption while offering actionable insights for effective integration into pathology practices.

MATERIALS AND METHODS

Data collection tool

Data were collected from the participants with the help of a structured and anonymous questionnaire to explore the attitudes of pathologists toward AI. The questionnaire was meticulously designed to capture a broad spectrum of information, including demographics, professional background, AI awareness, technological proficiency, and practical experiences with AI in pathology.

The questionnaire was divided into three key thematic sections and comprised 15 questions as shown in Fig. 1. The first section focused on demographics and professional background (Q1–Q5). This section gathered details such as age, sex, professional designation, years of experience, and practice setting (academic, private, or institutional). The second section addressed awareness and knowledge of AI (Q6–Q12), evaluating participants’ familiarity with AI concepts, sources of AI knowledge, self-assessed expertise, prior exposure to AI in pathology, and formal training. Participants rated their AI knowledge using four categories: ‘no knowledge’ (never heard of AI in pathology), ‘basic knowledge’ (general awareness without practical experience), ‘good knowledge’ (familiarity with concepts and some tool exposure), and ‘excellent knowledge’ (confidence in understanding and practical application). These were self-perceived levels, and no external validation was applied, which is an acknowledged limitation.

Fig. 1.

Survey on artificial intelligence (AI) in pathology.

In the third section, technological proficiency and current practices (Q13–Q15) were assessed. This section assessed respondents’ comfort with digital tools, access to WSI, and their reliance on diagnostic modalities like traditional microscopy.

RESULTS

Demographics and professional background

A total of 138 pathologists completed the survey. An overview of the age and sex distribution of the respondents as well as other demographic data are presented in Fig. 2. Detailed individual-level survey responses are provided in Supplementary Table S1.

Fig. 2.

Demographic details of respondents. (A) Age distribution of respondents. (B) Sex distribution of respondents. (C) Professional background of respondents.

The majority of respondents were female (66.7%) and aged 30–40 years (54.3%). Most participants were affiliated with academic institutions (78.3%), and faculty members with more than five years of experience constituted the largest subgroup (36.2%). The majority of participants were working in an academic environment, namely medical colleges (37.7%) and tertiary care centers (34.8%), with 21.7% of respondents working in private practice.

Awareness and knowledge of AI

The survey revealed high levels of awareness regarding AI’s role among pathologists, with 88.4% of respondents acknowledging its growing importance. Sex-based differences were found to be statistically significant, with females exhibiting greater awareness than males (93.5% vs. 78.3%, p = .008) as shown in Table 1.

Table 1.

Sex-based awareness and AI training among pathologists

Age-based differences were found to be statistically significant, with older respondents exhibiting greater awareness than younger respondents (95.8% vs. 84.4%, p = .047) as shown in Table 2.

Table 2.

Age-based awareness and AI training among pathologists

Social media (44.2%) and conferences (41.3%) were the most frequently cited sources of AI knowledge. Formal training in AI was reported by only 6.5% of participants, underscoring a significant gap in education. When asked to self-assess their knowledge of AI in pathology, the majority reported basic knowledge (41.3%) or limited knowledge (39.1%), while only 5.8% rated their knowledge as excellent.

Academic pathologists were significantly more likely to have read medical publications about AI than their non-academic counterparts (57.4% vs. 26.7%, p = .003) as shown in Table 3. A similar trend was observed among females, with a near-significant difference in the number of publications read compared to males (p = .054), as shown in Table 1. Encouragingly, 92.8% of respondents expressed interest in attending AI-related workshops or courses, reflecting a readiness to bridge this knowledge gap.

Table 3.

Academic vs. non-academic pathologist awareness and AI training

Technological proficiency and AI application

Only 16.7% of respondents had used AI as a diagnostic tool in real-world pathology practice. Respondents with good or excellent knowledge were found to be significantly more likely to identify themselves as tech-savvy compared to those with basic or no knowledge (80.0% vs. 43.4%, p = .001), as shown in Table 4.

Table 4.

AI knowledge levels among pathologists based on demographics and technology access

The respondents with good or excellent AI knowledge also reported significantly better access to WSI compared to those with limited knowledge (48.0% vs. 22.1%, p = .008).

DISCUSSION

This study provides valuable insight into the current state of AI awareness, knowledge, and adoption among pathologists in India. The high levels of awareness observed in this study align with global trends indicating growing interest in AI’s transformative potential for pathology [13,14]. However, significant gaps in formal training and practical application highlight barriers that must be addressed to fully realize AI’s benefits [15].

The influence of sex and professional setting on AI awareness is noteworthy. While female respondents demonstrated significantly higher awareness levels, this difference may be partially explained by professional affiliation. Notably, 82.6% (76 of 92) of female respondents worked at academic institutions, compared to 58.7% (27 of 46) of males. Given that academic environments offer greater access to AI-related resources and training, this distribution suggests a potential confounding effect, and the observed sex-based differences should be interpreted with caution. Additionally, respondents working in academic institutions exhibited greater familiarity with AI literature, likely due to better access to academic resources and exposure to research-oriented environments.

Although awareness of AI among pathologists was high, most respondents rated their knowledge as either basic or limited, highlighting a general lack of deeper understanding. The absence of formal training in AI further reinforces the need for well-structured educational programs to equip pathologists with necessary skills. On a positive note, the majority of participants expressed a strong interest in attending AI-related workshops or courses, showing a clear willingness to close this knowledge gap. These findings emphasize the need to incorporate AI training into pathology education, especially in countries like India, where the adoption of AI in practice is still at an early stage.

Additionally, a pattern emerged suggesting that access to advanced technologies like WSI and comfort with digital tools were key contributors to higher levels of AI knowledge. The limited use of AI tools in practice underscores challenges such as inadequate infrastructure, insufficient training, and limited access to enabling technologies like WSI. These findings suggest that digital pathology infrastructure is crucial for fostering familiarity and expertise with AI-driven workflow.

Interestingly, self-identified tech-savvy respondents demonstrated a stronger association with higher levels of AI knowledge, highlighting the importance of digital literacy in adapting to emerging technologies. These findings underscore the need to prioritize technological training and infrastructure development to promote equitable AI adoption across diverse practice settings.

This study highlighted notable differences in AI exposure between academic and private practice settings, with respondents in academic environments demonstrating greater familiarity with AI literature and concepts. This disparity likely stems from variation in resource availability, institutional support, and exposure to research-oriented activities.

The challenges identified in this Indian cohort mirror those observed globally. Worldwide, the integration of AI into pathology has been met with enthusiasm but also with considerable barriers. A bibliometric analysis revealed that the United States leads the field of AI-based tumor pathology research, contributing 41.3% of publications, followed by China and the United Kingdom [16,17]. This underscores the significant investment and focus on AI in pathology within these countries. In Europe, initiatives such as the Ecosystem for Pathology Diagnostics with AI Assistance (EMPAIA) project have been instrumental in accelerating AI adoption. EMPAIA has facilitated open-source platforms and interoperability standards, facilitating the integration of AI into clinical workflows across multiple laboratories [18]. Despite such efforts, Chua et al. [19] described system-wide challenges to AI implementation in oncology, including data bias, dynamic knowledge, and user interface design, and proposed actionable steps for overcoming these barriers through multidisciplinary collaboration. These insights affirm that the limitations seen in our study are not isolated but part of a broader global picture.

Infrastructural and technological barriers remain central to the slow uptake of AI in pathology. Specific challenges include the high cost of digital pathology systems, variable internet connectivity, limited access to computational resources, and a lack of trained personnel to manage and interpret AI outputs. Additionally, digital literacy levels vary significantly, particularly in private practice or rural settings. Data security concerns, particularly in handling sensitive patient images and records, further complicate the deployment of AI systems. Addressing these multi-layered issues will require a coordinated response involving institutional investment, government support, and public–private partnerships to create a conducive ecosystem for AI adoption.

One observation from our study is the overrepresentation of younger pathologists, with 65.2% under the age of 40. Notably, this age distribution is consistent with trends reported in both Indian and global pathology surveys [20,21]. This likely reflects greater digital engagement among younger professionals, which may introduce bias and limit the generalizability of the findings to the broader population of practicing pathologists. Future studies involving a more geographically diverse cohort of pathologists would enhance the statistical robustness of the findings and offer a more comprehensive understanding of AI awareness and adoption across different regions.

In conclusion, while awareness and interest in AI are growing among Indian pathologists, there remains a clear gap between recognition and implementation. To bridge this divide, actionable steps must include the development of standardized AI curricula for pathologists; organizing national and regional workshops; fostering collaborations between academic institutions, private sector developers, and healthcare policymakers; and expanding access to digital infrastructure and secure data-sharing platforms. By investing in education, policy, and technology together, the pathology community in India—and globally—can accelerate meaningful and equitable integration of AI into clinical practice.

Supplementary Information

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