Abstract
Background:
Artificial intelligence (AI) is transforming pharmacology by enhancing drug discovery, clinical trials, pharmacovigilance, and medical education. However, concerns about data security, job displacement, and ethical implications hinder its widespread adoption. This study assesses the perception of AI’s scope, threats, challenges, and acceptance among pharmacologists in India.
Methodology:
A cross-sectional, survey-based study was conducted among pharmacologists working in academia and the pharmaceutical industry in India between February 2024 and January 2025. A validated self-administered questionnaire was distributed through online platforms, collecting responses on AI awareness, perceived threats, benefits, challenges, and use. Data were analyzed using descriptive statistics, and categorical variables were compared using the Chi-square test.
Results:
A total of 104 pharmacologists participated, with 64 from academia and 40 from the industry. While 68.26% were familiar with AI tools, industry professionals (82.5%) exhibited higher awareness than academicians (59.37%, P = 0.017). Most respondents recognized AI’s significant role in drug discovery (77%), pharmacovigilance (73.07%), and clinical trials (69.23%). Major concerns included job displacement (62.5%), skill loss (63.46%), and algorithmic biases (64.42%). 33.65% pharmacologists never used AI-based tools in their professional careers. This number is significantly higher among academicians as compared to pharma people (P = 0.03). Limited access to AI tools, expertise, and training (79.8%) and lack of standardized data format/interoperability issues (66.34%) were key barriers to adoption.
Conclusion:
AI is perceived as a valuable tool in pharmacology, but challenges such as skill gaps, ethical concerns, and infrastructural limitations hinder its adoption. Addressing these barriers through targeted training, regulatory frameworks, and interdisciplinary collaborations will be crucial for AI’s seamless integration into the Indian pharmacology sector.
Keywords: Artificial intelligence, challenges, clinical trials, drug discovery, perception, pharmacology, pharmacovigilance
Résumé
Contexte:
L’intelligence artificielle (IA) transforme la pharmacologie en améliorant la découverte de médicaments, les essais cliniques, la pharmacovigilance et l’enseignement médical. Cette étude évalue la perception de la portée, des menaces, des défis et de l’acceptation de l’IA parmi les pharmacologues en Inde.
Méthodologie:
Une étude transversale par sondage a été menée auprès de pharmacologues travaillant dans le milieu universitaire et l’industrie pharmaceutique en Inde entre février 2024 et janvier 2025. Un questionnaire validé a été distribué via des plateformes en ligne, recueillant des réponses sur la sensibilisation à l’IA, les menaces perçues, les avantages, les défis et l’utilisation. Les données ont été analysées à l’aide de statistiques descriptives et les variables catégorielles ont été comparées à l’aide du test du Chi carré.
Résultats:
Au total, 104 pharmacologues ont participé, dont 64 du milieu universitaire et 40 de l’industrie. Alors que 68,26 % connaissaient les outils d’IA, les professionnels de l’industrie (82,5 %) affichaient une connaissance plus élevée que les universitaires (59,37 %, P = 0,017). La plupart des répondants ont reconnu le rôle important de l’IA dans la découverte de médicaments (77 %), la pharmacovigilance (73,07 %) et les essais cliniques (69,23 %). Les principales préoccupations comprenaient le déplacement d’emplois (62,5 %), la perte de compétences (63,46 %) et les biais algorithmiques (64,42 %). 33,65 % des pharmacologues n’ont jamais utilisé d’outils basés sur l’IA au cours de leur carrière professionnelle. Ce chiffre est significativement plus élevé chez les universitaires que chez les professionnels de l’industrie pharmaceutique (p = 0,03). L’accès limité aux outils, à l’expertise et à la formation en IA (79,8 %) et l’absence de format de données standardisé/les problèmes d’interopérabilité (66,34 %) ont été les principaux obstacles à l’adoption.
Conclusion:
L’IA est perçue comme un outil précieux en pharmacologie, mais des défis tels que les lacunes en matière de compétences, les préoccupations éthiques et les limitations infrastructurelles entravent son adoption. Il sera crucial de surmonter ces obstacles par le biais de formations ciblées, de cadres réglementaires et de collaborations interdisciplinaires pour une intégration harmonieuse de l’IA dans le secteur pharmacologique indien.
Mots-clés: Intelligence artificielle, défis, essais cliniques, découverte de médicaments, perception, pharmacologie, pharmacovigilance
INTRODUCTION
Artificial intelligence (AI) has emerged as a breakthrough option in the field of health care. It is the biggest revolution in approaching patient care, disease management, and medical research. AI’s transformative potential in healthcare is rooted in its ability to harness the power of data, advanced algorithms, and machine learning (ML) techniques to revolutionize various aspects of healthcare delivery, from diagnosis and treatment to patient management and administrative tasks.[1,2] In recent years, AI has become an indispensable tool in the healthcare sector, offering innovative solutions to complex challenges and opening up unprecedented opportunities for healthcare providers, researchers, and patients.[3,4]
AI is reshaping the landscape of pharmacology, offering innovative solutions to longstanding challenges in drug discovery, clinical development, pharmacovigilance, and other domains of pharmacology. The application of AI in pharmacology draws upon the power of ML, data analytics, and computational modeling to streamline and enhance various aspects of the drug development process.[5,6] ML models can identify novel molecules with therapeutic potential, saving time and resources in the drug discovery process.[7] AI can also repurpose existing drugs for new therapeutic uses by analyzing their interactions with various biological pathways.[8] In clinical development, AI can help design more efficient and cost-effective clinical trials by identifying suitable patient populations and predicting trial outcomes. AI can also predict potential side effects and toxicity of drug candidates, allowing researchers to optimize drug safety profiles earlier in drug development.[9]
Considering the potential for integration of AI in every domain, we designed this study to identify the perception regarding the application of AI tools in pharmacology including the perceived scope, threats, and challenges related to AI usage among pharmacologists working in academia or pharmaceutical industry. Understanding these perceptions will help identify key areas for improvement, inform policy recommendations, and facilitate the adoption of AI-based tools in pharmacology education and practice.
Objectives
To evaluate pharmacologist’s level of awareness and perception of AI’s role and scope in pharmacology
To identify perceived threats and challenges associated with AI integration
To evaluate acceptance of AI tools among pharmacology professionals
To compare the awareness, threats, challenges, and acceptance among participants affiliated with academics versus industry.
METHODOLOGY
A cross-sectional study was carried out among pharmacologists working in the academia or pharmaceutical industry for a duration of 1 year from February 2024 to January 2025. The study was approved by the Institutional Research Cell and Institutional Ethics Committee (IEC/Pharmac/2023/692 Date November 24, 2023). The study participants were pharmacologists working in an academic or pharmaceutical industry who were approached through E-mail or WhatsApp and provided a Google link to fill out the study survey questionnaire. Electronic consent was obtained from each participant after explaining the nature and purpose of the study. The study participants were requested to proceed on the Google link only if they wished to participate in the study. All pharmacologists working in the academia and pharma industry who were willing to participate were approached for inclusion in the study. Those who were not willing to provide informed consent were excluded from the study.
Study procedure
Designing and validation of study tool
Framing the questionnaire
Data on scope, threats, and challenges related to the usage of AI tools in pharmacology as perceived by participants were reviewed by the study team with the help of published literature. After deliberation from the study team, the self-administered questionnaire is designed. A structured questionnaire with both closed and open ended questions is designed
The questionnaire contains demographic details, questions regarding the perceptions of pharmacologist regarding the application of AI tools in pharmacology and their previous experience with AI tools, the perception of the scope of AI tools in pharmacology, threats, and challenges related to it
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Content of questionnaire
Section I – Demographic details
Section II – Perception and Practices.
Questions 1 and 2 were based on knowledge of AI-based tools
Questions 3–11 were based on the scope of AI-based tools
Questions 12–14 evaluated the perception of threats of using AI-based tools
Questions 15–17 evaluated the challenges of using AI-based tools
Questions 18–20 evaluated the practice related to the use of AI-based tools.
Responses related to perception were recorded using a Likert scale that will ease the process of analysis
We have made efforts to reduce the biased response by avoiding leading questions and questions that might attract similar responses were also removed
Issues such as simplification, error-free construction, and grammatical errors also have been taken care in consideration at the review stage.
Validation of the questionnaire
The face validity of the questionnaire was evaluated in terms of readability, feasibility, layout, style, and clarity of wording. Content validation was done by 5 experts who have expertise in the construction of the questionnaire. The content validation experts were briefed to check the questionnaire items for their adequateness in measuring the constructs and to know whether the chosen items were sufficient to achieve the objective. The experts were asked to comment on the clarity of questions, comprehensibility, and identification of any missed important items in the questionnaire and identify and remove any questions that were not necessary for achieving the objective.
Content validation ratio (CVR) is a linear transformation of a level of agreement on how many “experts” within a panel rate an item “essential.” The CVR was calculated for individual items using Lawshe’s method.[10] The content validity index (CVI) is the mean of the CVR values for all items. CVI as calculated by Lawshe’s method was 0.9.
Measuring reliability
Internal consistency is a measure of the intercorrelation of the items of the questionnaire which was measured in terms of Cronbach alpha coefficient. Cronbach’s alpha value was 0.76 which indicates adequate internal consistency of a given questionnaire.
Test–retest reliability involves administering the questionnaire to the same group of respondents at different points in time. The reliability was assessed by administering the questionnaire to five sample respondents twice in a gap of 2 weeks. The test–retest reliability was evaluated using a correlation coefficient called Pearson’s product-moment correlation coefficient (Pearson’s r). The test–retest reliability was found to be 0.8.
Enrollment of participants and administration of the questionnaire
Subjects fulfilling the inclusion criteria were included in this study. The mailing list includes all pharmacologists working in the academics and pharmaceutical industry fulfilling the inclusion criteria. The participants were requested to give electronic consent. They were provided the Google link to the study survey questionnaire and asked to click on the Google form link if they consented to participate in the study. The questionnaires were sent to participants individually with one reminder after 1 week of the initial contact.
Statistical analysis
Data were analyzed using descriptive statistics and were expressed as percentages and numbers wherever applicable. The categorical data were summarized in percentages and frequencies wherever applicable. Continuous data were expressed as mean (standard deviation). Fisher’s exact/Chi-square test was used for comparing categorical variables. P < 0.05 was considered statistically significant. Statistical analysis were performed using GraphPad Prism version 9.0.0 for Windows, GraphPad Software, San Diego, California USA.
RESULTS
For a collection of data, we approached 152 pharmacologists working in either academia or the pharmaceutical industry. Out of them, 104 participants responded and were willing to take part in the study. We received responses from these 104 participants, of which 64 participants were from academia and 40 were from the pharmaceutical industry. Fifty-four were male and 50 were female pharmacologists. The majority of the participants had professional experience of <10 years (77%).
Among 104 participants, 71 (68.26%) were familiar with the concept of AI/ML/Deep learning (DL) in pharmacology, whereas 30 (28.84%) were least familiar and 3 (2.88%) were not at all familiar. Comparatively, more participants from industry (82.5%) are familiar with the concept as compared to academia (59.37%) (P = 0.017). Table 1 explains the perception of participants on a Likert scale about the application of AI tools. The majority of participants agreed with the statements supporting applications of AI tools in various pharmacology domains. When asked specifically, most of the participants believe that AI tools have a better scope in drug discovery and development (80 [77%]), pharmacovigilance (76 [73.07%]) followed by clinical trials (72 [69.23%]).
Table 1.
Perception of application of artificial intelligence tools in pharmacology (n=104)
| Statement | Strongly agree | Agree | Neutral | Disagree | Strongly disagree |
|---|---|---|---|---|---|
| 1. AI has the significant potential to impact the field of pharmacology | |||||
| Academics (n=64) | 21 | 34 | 9 | 0 | 0 |
| Industry (n=40) | 11 | 25 | 4 | 0 | 0 |
| 2. Undergraduate/postgraduate medical education should include AI-based modules in the curriculum | |||||
| Academics (n=64) | 17 | 31 | 14 | 1 | 1 |
| Industry (n=40) | 16 | 17 | 5 | 2 | 0 |
| 3. Integration with AI-based applications will improve pharmacovigilance activity | |||||
| Academics (n=64) | 19 | 40 | 5 | 0 | 0 |
| Industry (n=40) | 18 | 19 | 3 | 0 | 0 |
| 4. AI technology has many novel potential applications in clinical pharmacology | |||||
| Academics (n=64) | 19 | 40 | 5 | 0 | 0 |
| Industry (n=40) | 12 | 26 | 2 | 0 | 0 |
AI=Artificial Intelligence
Details of pharmacologists’ opinions about key areas in various pharmacology domains having greater scope for AI-based tools are explained in Table 2. Biases in algorithms used for AI-driven decision-making (64.42%), loss of skills (63.46%), and reduced job opportunities (62.5%) were major threats to adopting AI as mentioned in Table 3. We found that participants with industry affiliations felt a significantly higher chance of reduced job opportunities with AI in the future as compared to participants with academic affiliations (P = 0.01). Table 4 enlisted challenges of integrating AI-based tools in various key areas of different domains of pharmacology including limited access to AI tools, expertise, and training (79.8%), lack of standardized data format/interoperability issues (66.34%), formulating ethical guidelines and regulatory frameworks to safeguard potential participants (64.42%), and development/calibration of AI models (63.46%).
Table 2.
Perceived opinion of pharmacologist about the application of artificial intelligence tools in various key domains of pharmacology (n=104)
| Perceived scope for AI tools in different key domains of pharmacology | Academics (n=64), n (%) | Industry (n=40), n (%) | Total, n (%) |
|---|---|---|---|
| 1. Clinical trials | |||
| Data handling/analysis | 55 (86) | 29 (72.5) | 84 (80.76) |
| Randomization, allocation, concealment and blinding | 44 (69) | 30 (75) | 74 (71.15) |
| To optimize clinical trial design, cohort selection and recruitment | 36 (56.25) | 20 (50) | 56 (53.84) |
| Predict the probabilities of side effects | 21 (33) | 17 (42.5) | 38 (36.53) |
| Remote monitoring | 13 (20.31) | 8 (20) | 21 (20.02) |
| 2. Drug discovery | |||
| Structure/ligand based virtual screening for target identification | 31 (48.43) | 17 (42.5) | 48 (46.15) |
| PK/PD modelling | 21 (32.81) | 9 (22.5) | 30 (28.84) |
| Drug repurposing | 5 (7) | 7 (17.5) | 12 (11.53) |
| Predict drug toxicity | 5 (7.81) | 0 | 5 (4.8) |
| 3. Pharmacovigilance | |||
| Adverse event coding | 46 (44.23) | 33 (82.5) | 79 (76) |
| Causality assessment | 46 (44.23) | 18 (45) | 64 (61.53) |
| Signal detection | 31 (48.43) | 27 (67.55) | 58 (55.76) |
| Labelling assessment | 24 (37.5) | 20 (50) | 44 (42.3) |
| 4. Medical education | |||
| Assessment and analysis of student’s performance | 41 (64.06) | 29 (72.5) | 70 (67.03) |
| Personalized learning | 36 (56.25) | 27 (67.5) | 63 (60.57) |
| Learning management systems | 30 (46.87) | 18 (45) | 48 (46.15) |
| Transcription of faculty lectures | 10 (15.62) | 12 (30) | 22 (21.15) |
| Curriculum development and implementation | 31 (48.43) | 16 (40) | 47 (45.19) |
| 5. Medical affairs | |||
| Literature review and medical information sharing | 43 (67.18) | 30 (75) | 73 (70.19) |
| Education and training | 37 (57.81) | 24 (60) | 61 (58.65) |
| Identification of new potential for market therapies | 30 (46.87) | 21 (52.5) | 51 (49.03) |
| Collecting RWE insight | 26 (40.62) | 14 (35) | 40 (38.46) |
RWE=Real-world evidence, AI=Artificial intelligence, PK=Pharmacokinetics, PD=Pharmacodynamics
Table 3.
Threats of using artificial intelligence tools in pharmacology (n=104)
| Perceived threats of using AI tools in pharmacology | Academics (n=64), n (%) | Industry (n=40), n (%) | Total, n (%) |
|---|---|---|---|
| 1. Reduced job opportunities for pharmacologists in the future | 34 (53.12) | 31 (77.5)* | 65 (62.5) |
| 2. Loss of skills if AI is implemented in workflow | 44 (69) | 22 (55) | 66 (63.46) |
| 3. May hamper subject’s autonomy | 29 (45.31) | 18 (45) | 47 (45.19) |
| 4. May hamper data privacy and confidentiality | 38 (59.37) | 22 (55) | 60 (57.69) |
| 5. Biases in algorithms used for AI-driven decision-making | 40 (62.5) | 27 (67.5) | 67 (64.42) |
*P<0.05 when compared by Chi-square test. AI=Artificial intelligence
Table 4.
Challenges in integrating artificial intelligence-based tools in pharmacology (n=104)
| Challenges in the integration of AI-based tools | Academics (n=64), n (%) | Industry (n=40), n (%) | Total, n (%) |
|---|---|---|---|
| 1. Challenges in implementing AI in the conduct of clinical trials | |||
| Formulating ethical guidelines and regulatory frameworks to safeguard potential participants | 43 (67.18) | 24 (60) | 67 (64.42) |
| Development/validation/interpretation of AI-based models like virtual control arm | 39 (60.93) | 23 (57.5) | 62 (59.61) |
| Lack of robust, standardized, and complete data set | 37 (57.81) | 22 (55) | 59 (56.73) |
| 2. Challenges in implementing AI for data processing and analysis | |||
| Lack of standardized data format | 43 (67.18) | 26 (65) | 69 (66.34) |
| Ensuring data privacy and confidentiality | 39 (60.93) | 23 (57.5) | 62 (59.61) |
| Data quality and preprocessing | 35 (54.68) | 2 3 (57.5) | 58 (55.76) |
| 3. Challenges in employing AI in preclinical research or new drug discovery process? | |||
| Development/calibration of AI models | 44 (69) | 22 (55) | 66 (63.46) |
| Development/validation/interpretation of AI-based models like virtual control arm | 39 (60.93) | 23 (57.5) | 62 (59.61) |
| Lack/limited access to high-quality data | 35 (54.68) | 21 (52.5) | 56 (53.84) |
| 4. Challenges hindering the adoption and integration of AI-based tools in academic settings? | |||
| Limited access to AI tools, expertise and training | 54 (84.37) | 29 (72.5) | 83 (79.8) |
| Difficulty in integrating AI into existing curriculum | 39 (60.93) | 26 (65) | 75 (72.11) |
| Resistance to change among faculty and staff | 35 (54.68) | 24 (60) | 49 (47.11) |
AI=Artificial intelligence
Participants admitted that there are many benefits of using AI-based tools including time-saving (83 [79.8%]), reduced workload and manpower (25 [23.32%]), improved quality of work (13 [12.53%]), reduced chances of error (20 [19.23%]), and enhanced productivity (23 [22.11%]). The most commonly used AI tools by participants were ChatGPT (76%), Open AI (30%), Grammerly (22%), and Quill boat (16%). Some also mentioned about use of Semantic Scholar, Copilot, Research Rabbit, LitPro, and GenAI.
However, 35 (33.65%) pharmacologists never used AI technology/AI-based tools in their professional careers. This number is significantly higher among academicians (27 [42.18%]) as compared to pharma people (8[20%])(P = 0.03). The primary obstacles to not using AI tools include lack of access to AI resources (67 [64.4%]), lack of knowledge and training (89 [85.5%]), and budget constraints (45 [43.26%]).
DISCUSSION
In the present study, we tried to explore the perceived scope, threat, and challenges of AI integration in pharmacology. Understanding these perceptions helped in identifying key areas for improvement to facilitate the adoption of AI-based tools in pharmacology education and practice.
Most of the participants were familiar with AI-based tools and had positive attitudes regarding their use. The study findings highlight the increasing familiarity of pharmacologists with AI applications in drug discovery, clinical trials, and pharmacovigilance. A significant proportion of respondents acknowledged AI’s transformative potential in pharmacology, with 77% recognizing its role in drug discovery and 73% in pharmacovigilance. These results align with previous literature demonstrating AI’s capacity to optimize clinical trial designs and enhance adverse event reporting efficiency.[1,3]
AI has a significant scope in clinical trials by optimizing various processes, including patient recruitment, trial design, data monitoring, and adverse event prediction. ML algorithms can enhance patient selection by identifying suitable candidates based on genetic, demographic, and clinical data, thus reducing trial costs and duration. AI-driven predictive analytics help in early detection of treatment responses and potential side effects. In addition, AI facilitates decentralized and virtual clinical trials through remote monitoring and real-time data collection, increasing accessibility and diversity in study populations. A study by Karekar and Vazifdar[11] reported an increasing trend in the studies being conducted using AI, with the majority being conducted in the area of oncology, with medical devices being the most common intervention being tested.
In the present study, most of the participants admit the role of AI in new drug discovery. AI-based approaches can enable the rapid and efficient design of novel compounds with desirable properties and activities. For example, Gupta et al. recently reported on the successful use of AI to identify novel compounds for the treatment of cancer.[12] These authors trained a DL algorithm on a large dataset of known cancer-related compounds and their corresponding biological activity. As an output, novel compounds with high potential for future cancer treatment were obtained, demonstrating the ability of this method to discover new therapeutic candidates. However, the successful application of AI in drug discovery is dependent on the availability of high-quality data, and the recognition of the limitations of AI-based approaches. Recent developments in AI, including the use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges and limitations of AI in the context of drug discovery.[13]
AI has the potential to revolutionize pharmacovigilance practices. ML and natural language processing (NLP) have been leveraged in pharmacovigilance to automate the identification of adverse events and drug–drug interactions. AI-powered cognitive services, which integrate ML and NLP techniques, have been designed to handle specific tasks within the processing of Individual Case Safety Reports, traditionally requiring human intelligence. These AI-driven approaches help alleviate the cognitive workload of pharmacovigilance professionals while enhancing the efficiency and accuracy of various pharmacovigilance operations.[14]
The study also highlighted AI’s potential in medical education, with 67.03% supporting AI-driven assessment and analysis tools. Prior research corroborates the benefits of AI in personalized learning and performance evaluation in pharmacology education. Some challenges of incorporating AI tools in medical education as pointed out by participants include limited access to AI tools, expertise, and training, difficulty in integrating AI into existing curriculum, and resistance to change among faculty and staff. Conversely, as reported by Zarei et al., the main challenges of AI integration in education are ethical and legal issues, scalability limitations, evaluating the effectiveness of these educational methods, and technical difficulties.[15] Interestingly, only a few academicians highlighted the potential of AI in lecture transcription, possibly due to limited awareness of its applications in this area.
In the present study, pharmacologists opined that AI has significant potential in medical affairs by streamlining literature reviews, facilitating medical information sharing, and enhancing education and training programs. It aids in identifying new market therapies and analyzing real-world evidence to support clinical decision-making. By leveraging AI-driven insights, pharmaceutical companies and healthcare professionals can improve drug development strategies and optimize patient care.[16]
Despite the promising scope, notable concerns regarding job displacement (62.5%) and loss of skills (63.46%) were identified. A significant number of industry people had this concern. It could be due to lesser job security in industry and lesser manpower deployment with advancing technology. Similar apprehensions have been reported in earlier studies, where automation in healthcare raised concerns about professional redundancy.[2] In addition, the challenges of data privacy (57.69%) and biases in AI algorithms (64.42%) which could result in unequal access to medical treatment and the unfair treatment of certain groups of people reflect the broader discourse on ethical AI implementation.[13] Addressing these issues through regulatory frameworks and AI literacy programs is imperative.
Overall, data security and privacy concerns, lack of sufficient data, interoperability issues, regulatory compliance, ethical and bias concerns, and resistance to adoption stand as the key challenges. Ensuring robust encryption, standardized data formats, and responsible data sharing can enhance security and interoperability. Addressing data scarcity requires investment in high-quality data collection through wearables and remote monitoring devices. Ethical AI deployment demands algorithm audits, transparency, and accountability to prevent bias. High initial investment costs are a primary hurdle, encompassing the procurement of AI systems, data management tools, computing infrastructure, and staff training. High implementation costs necessitate strategic investments, public–private partnerships, and government support. Overcoming these barriers will facilitate seamless AI adoption in healthcare, improving efficiency, and patient outcomes.
A crucial finding was the discrepancy in AI adoption between academia and industry. Industry professionals exhibited a greater familiarity (67.5%) with AI compared to academicians (59.37%), suggesting the need for integrating AI modules into academic curricula. Resistance to change (47.11%) and lack of access to AI tools (79.8%) were key barriers to adoption in academia. Many academicians may not be fully aware of AI-based tools due to various factors such as limited exposure to technology, traditional teaching methods, a focus on original research without reliance on tools, and concerns about the ethical implications of using AI in academia. There is also apprehension that AI might stifle creativity and originality in academic writing, as these tools often depend on existing knowledge and could lead to bias emphasizing the necessity for targeted training and infrastructural support.
The use of validated survey tool and inclusion of both academicians and industry professionals allows for comparative insights into AI adoption across different sectors. However, perceptions may not always align with actual AI usage or expertise, introducing potential inaccuracies. Differences in access to AI tools and infrastructure across institutions may have influenced responses.
CONCLUSION
While the perception of AI in pharmacology is largely positive, addressing concerns related to job security, ethical AI use, and accessibility will be critical for its seamless integration. Future efforts should focus on interdisciplinary collaborations, robust regulatory measures, and comprehensive AI education programs to maximize AI’s benefits in pharmacology.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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