# Promise and peril of artificial intelligence: Digital monoculturalism in robotic world

Kavita Batra, Ravi Batra, Ying Guo, Mina Attin, Bryar Shareef

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Kavita Batra, Ravi Batra, Ying Guo, Mina Attin, Bryar Shareef. Promise and peril of artificial intelligence: Digital monoculturalism in robotic world. Advances in Medicine, Psychology, and Public Health, 2025, 2 (4), pp.210-214. 10.5281/zenodo.14052827. hal-04859270

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# Promise and peril of artificial intelligence: Digital monoculturalism in robotic world

Kavita BATRA<fnref n="1" />*, Ravi BATRA<fnref n="2" />, Ying GUO<fnref n="3" />, Mina ATTIN<fnref n="4" />, Bryar SHAREEF<fnref n="5" />

**Cite this paper as:** Batra K, Ravi B, Guo Y, Attin M, Shareei B. Promise and peril of artificial intelligence: Digital monoculturalism in robotic world. Adv Med Psychol Public Health. 2025;2(4):210-214.

Doi: 10.5281/zenodo.14052827

Received: 12 August 2024
Revised: 20 October 2024
Accepted: 25 October 2024
Published Online:
07 November 2024

**Keywords:** Artificial intelligence; bolts; digital monoculturalism; healthcare; research.

Dear Readers,

Artificial Intelligence (AI) has influenced several industries, including healthcare, sports/gaming, agriculture, information technology (IT), and automobile (e.g. self-driving vehicles) etc. [1,2]. With the new advancements in AI technologies, several concerns emerged with one being digital monoculturalism [3]. AI raises a range of ethical considerations and data security concerns, including issues related to consent, fairness, transparency, accountability, data privacy, and security [4]. In the context of how AI will shape our digital culture, this editorial aim to provide a balanced perspective to encourage a dialogue and action towards promoting a pluralistic digital landscape in healthcare, research, and emergency management.

As stewards of medicine, we need to think “if our patients accept the “machine-human” relations over “human-human” interactions? Because robotics doctors and nurses will lack one important human attribute called compassion [5]. It is well-established in the existing literature that there is a strong positive association between compassion and patient reported health outcomes, quality of life and their experiences [5,6]. This is one of those aspects where machine or robots will possibly fail to overtake humans in delivering the true compassion to their patients, albeit, machines may be able to simulate empathy through programmed responses and facial recognition. Our system is already struggling with the appropriate provisioning of this core component of compassion in the complex adaptive system e.g. healthcare that could equip our physicians to

<footnote n="1">¹Department of Medical Education, Kirk Kerkorian School of Medicine at UNLV, University of Las Vegas, Nevada, USA. Office of Research, Kirk Kerkorian School of Medicine at UNLV, University of Las Vegas, Nevada, USA. E-mail: kavita.batra@unlv.edu ORCID: 0000-0002-0722-0191</footnote>

<footnote n="2">²Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, NV 89119, USA. Department of Information Technology and Testing Center of Excellence, Coforge, Atlanta, GA 30338, USA. E-mail: batrar2@unlv.nevada.edu ORCID: 0000-0002-7335-3582</footnote>

<footnote n="3">³Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, NV 89119, USA. E-mail: ying.guo@unlv.edu ORCID: 0000-0002-0602-8843</footnote>

<footnote n="4">⁴School of Nursing, University of Nevada, Las Vegas, USA. E-mail: mina.attin@unlv.edu ORCID: 0000-0002-0641-3274</footnote>

<footnote n="5">⁵Department of Computer Science, University of Nevada, Las Vegas, Nevada, USA. E-mail: bryar.shareef@unlv.edu
ORCID: 0009-0006-2124-1032</footnote>

<footnote>*Correspondence</footnote>
practice compassion in their clinical care [7]. Some studies highlighted the need to reconceptualize compassion in healthcare by adding an important element of “intelligent caring,” comprising of AI functionality to have awareness, understanding, attention, and responding to patients’ sufferings [7,8].

Similar to other industries, AI colonization in medical practice has also been observed given its benefit in improving the clinical efficiency and decision making [9]. However, it is important to note that AI based healthcare solutions are driven by standardized algorithms without accounting for the full complexity of clinical scenario and preferences. Healthcare practitioners' clinical judgment and critical thinking abilities may be compromised by an over-reliance on AI in clinical practice and decision-making [10]. It is critical to permit AI implementation with an understanding of its limitations without sacrificing possibilities for human judgment and empathy in patient care in order to mitigate these threats of digital monoculturalism in the healthcare industry.

In the evidence-based research and practice, AI helped us to stay updated with the current best available evidence, despite the quickly evolving research landscape with an exponential growth in the scientific production each day [11]. It is humanly impossible to stay current with the newly published research, AI-powered text mining tools can assist in summarizing the latest research, however, “AI hallucinations” or bugs may require researchers to perform an additional critical appraisal of the literature [12,13]. AI can help in analyzing large and complex datasets to inform new insights and discoveries. The Natural Language Processing (NLP) techniques can help facilitate a robust evidence synthesis comprising of systematic reviews and meta-analysis. Moreover, AI can help in efficient experimental designing and optimization to allow reproducibility in advancing the scientific knowledge. In addition to the implementation phases of the research, AI modalities also help in writing scholarly articles to maximize productivity, however, these may be without accounting for the ethical, critical, and originality considerations [13]. In the wake of algorithmic curation, where choices and preferences are similar, this carries the risk of monocultural scientific research, which poses a threat to diversity and plurality in science and education [14]. It is important to note that these tools can not replace human critical and rational thinking, which are the core elements of any scholarly product. These tools can not fully comprehend the nuances of complexity of a research topic, and the AI-generated text may be with imaginary subjects and statistics-another concern, which warrants human supervision.

Similar to this, academic institutions are attempting to keep up with the mass manufacturing of AI tools in teaching and education. Funding is being provided by several funding organizations for initiatives that use AI tools in science teaching. The European Union made a funding announcement for research projects that will boost Europe's technical competitiveness back in November 2023 [15]. Several scientific journals have launched special issues which aim to investigate transformative potential of AI in teaching and learning.

The AI industry led to a paradigm shift and this new wave has potential to dehumanize learning. According to a recent review conducted by Heeg and Avraamidou (2023), educators are using AI tools to provide feedback, to resolve students' queries, to design curriculum, and to create learning materials etc. In other words, students are interacting with chatbots instead of teachers. What the AI is essentially doing here? This is doing automated curation of existing learning resources, which have already been taught instead of identifying novel ways of teaching and learning. Also, there is a lack of concrete evidence, which will allow us to have a complete understanding on how AI can promote socio-emotional learning to meet our inclusivity goals in science and education [16]. AI may have met our cognitive goals, however, important question to ask here is if AI has been successful in meeting the goals of nature of scientific knowledge? In this input-output process, let ask ourselves some questions-are we not dehumanizing our scientific learning? If AI fails or make errors, who will be held accountable?

Interestingly, several studies emphasized the need of human validations on the AI-generative outputs to ensure accuracy [12-17]. In a recent study, the performance of ChatGPT and Bard in medical education was investigated, both performed well in medical knowledge and clinical reasoning questions with relatively low performance in the ECG interpretative models
[17]. This points to the need of appropriate non-AI based (human-driven) validation strategies thereby limiting overreliance on the AI.

The benefit of AI in predictive modelling and forecasting is unparalleled, especially in monitoring the patterns in epidemiological, environmental science and economics research. With accurate predictive models, prevention of life-threatening conditions will be possible, which will help relieving the financial strain on our healthcare system. Moreover, these AI-modelled projections can help overcoming future challenges and emergency health management [18]. AI integrated satellite images, Unmanned Aerial Vehicles (UAVs) or drones, Geographic Information Systems (GIS), Artificial Neural Networks (ANN) and several others can predict a range of disastrous outcomes, which can help curbing the post disaster damage [19]. It is worth mentioning a few examples of AI use in emergency preparedness and response planning. Guikema and Colleagues were successful in predicting power cuts during hurricane events in the United States (U.S.) [19]. Recently, Japan used the AI technology to assess psychological health of people living in the earthquake prone areas, which would allow timely interventions to curb the damage [20]. Interestingly, disasters like chemical spills, nuclear wastes can be monitored through the “walking robots” deployed by the Boston Dynamics Spot [21]. As a known fact that “opportunities and risk come in pairs,” it is important to acknowledge the limitations of AI in disaster management, for instance, validation of AI-based data may be challenging and technical flaws can limit its efficiency [22]. If we carefully account for the algorithmic bias of the AI and supplement it with human intelligence, it offers immense potential to enhance preparedness, response, and recovery efforts in the face of natural and man-made disasters.

To summarize, AI exerts a powerful transformative force to improve the way we live. However, the complete picture of what will the future hold for human and AI is unclear at this time. In the midst of this turmoil, some believe that AI is a silver bullet, while others believe this as hijackers of our minds. Are these speculative views? Is this a genuine curiosity or understandable fear? If we view this from a historical lens, like other technologies, AI can be neither good or evil. Editors of this journal think that we need to balance our concerns until we unlock the full potential of AI and alleviate emerging concerns associated with it. The possibility is that these technologies-induced concerns will be solved through technology-based solutions. To realize the full potential of AI, we will have to tailor these AI technologies in resource-poor settings as well to improve global health.

**Author Contributions:** All authors have equally contributed to this editorial.

**Funding:** None.

**Acknowledgments:** The lead author would like to express gratitude to the AI experts and additional peers in this field, who generously provided their insights and feedback, greatly enhancing the quality of this editorial. Their expertise has been invaluable in refining our perspectives.

**Conflicts of Interest:** The authors declare no conflict of interest. Disclaimer/Publisher's.

**Note:** The Publisher remains neutral regarding jurisdictional claims in published maps and institutional affiliations. Additionally, the Publisher is not responsible for the accuracy, completeness, or validity of the content of scientific articles published herein. This statement exempts the Publisher from any responsibility regarding the content of scientific articles, which is solely the responsibility of the authors and peer reviewers.

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**Copyright:** © 2025 by the authors. Submitted for possible open-access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).