Google Gemini

Anichur Rahman, Shahariar Hossain Mahir, Md. Tanjum An Tashrif, A. Aishi, Md. Ahsan Karim, Dipanjali Kundu, Tanoy Debnath, Md. Abul Ala Moududi, MD. Zunead Abedin Eidmum

Nowadays, DeepSeek, ChatGPT, and Google Gemini are the most trending and exciting Large Language Model (LLM) technologies for reasoning, multimodal capabilities, and general linguistic performance worldwide. DeepSeek employs a Mixture-of-Experts (MoE) approach, activating only the parameters most relevant to the task at hand, which makes it especially effective for domain-specific work. On the other hand, ChatGPT relies on a dense transformer model enhanced through reinforcement learning from human feedback (RLHF), and then Google Gemini actually uses a multimodal transformer architecture that integrates text, code, and images into a single framework. However, by using those technologies, people can be able to mine their desired text, code, images, etc, in a cost-effective and domain-specific inference. People may choose those techniques based on the best performance. In this regard, we offer a comparative study based on the DeepSeek, ChatGPT, and Gemini techniques in this research. Initially, we focus on their methods and materials, appropriately including the data selection criteria. Then, we present state-of-the-art features of DeepSeek, ChatGPT, and Gemini based on their applications. Most importantly, we show the technological comparison among them and also cover the dataset analysis for various applications. Finally, we address extensive research areas and future potential guidance regarding LLM-based AI research for the community.

Noha Taymour, Shaimaa M. Fouda, Hams H. Abdelrahaman, Mohamed G. Hassan

STATEMENT OF PROBLEM Artificial intelligence (AI) chatbots have been proposed as promising resources for oral health information. However, the quality and readability of existing online health-related information is often inconsistent and challenging. PURPOSE This study aimed to compare the reliability and usefulness of dental implantology-related information provided by the ChatGPT-3.5, ChatGPT-4, and Google Gemini large language models (LLMs). MATERIAL AND METHODS A total of 75 questions were developed covering various dental implant domains. These questions were then presented to 3 different LLMs: ChatGPT-3.5, ChatGPT-4, and Google Gemini. The responses generated were recorded and independently assessed by 2 specialists who were blinded to the source of the responses. The evaluation focused on the accuracy of the generated answers using a modified 5-point Likert scale to measure the reliability and usefulness of the information provided. Additionally, the ability of the AI-chatbots to offer definitive responses to closed questions, provide reference citation, and advise scheduling consultations with a dental specialist was also analyzed. The Friedman, Mann Whitney U and Spearman Correlation tests were used for data analysis (α=.05). RESULTS Google Gemini exhibited higher reliability and usefulness scores compared with ChatGPT-3.5 and ChatGPT-4 (P<.001). Google Gemini also demonstrated superior proficiency in identifying closed questions (25 questions, 41%) and recommended specialist consultations for 74 questions (98.7%), significantly outperforming ChatGPT-4 (30 questions, 40.0%) and ChatGPT-3.5 (28 questions, 37.3%) (P<.001). A positive correlation was found between reliability and usefulness scores, with Google Gemini showing the strongest correlation (ρ=.702). CONCLUSIONS The 3 AI Chatbots showed acceptable levels of reliability and usefulness in addressing dental implant-related queries. Google Gemini distinguished itself by providing responses consistent with specialist consultations.

I. Salman, O. Ameer, Mohammad A. Khanfar, Y. Hsieh

Background Artificial intelligence (AI) is revolutionizing medical education; however, its limitations remain underexplored. This study evaluated the accuracy of three generative AI tools—ChatGPT-4, Copilot, and Google Gemini—in answering multiple-choice questions (MCQ) and short-answer questions (SAQ) related to cardiovascular pharmacology, a key subject in healthcare education. Methods Using free versions of each AI tool, we administered 45 MCQs and 30 SAQs across three difficulty levels: easy, intermediate, and advanced. AI-generated answers were reviewed by three pharmacology experts. The accuracy of MCQ responses was recorded as correct or incorrect, while SAQ responses were rated on a 1–5 scale based on relevance, completeness, and correctness. Results ChatGPT, Copilot, and Gemini demonstrated high accuracy scores in easy and intermediate MCQs (87–100%). While all AI models showed a decline in performance on the advanced MCQ section, only Copilot (53% accuracy) and Gemini (20% accuracy) had significantly lower scores compared to their performance on easy-intermediate levels. SAQ evaluations revealed high accuracy scores for ChatGPT (overall 4.7 ± 0.3) and Copilot (overall 4.5 ± 0.4) across all difficulty levels, with no significant differences between the two tools. In contrast, Gemini’s SAQ performance was markedly lower across all levels (overall 3.3 ± 1.0). Conclusion ChatGPT-4 demonstrates the highest accuracy in addressing both MCQ and SAQ cardiovascular pharmacology questions, regardless of difficulty level. Copilot ranks second after ChatGPT, while Google Gemini shows significant limitations in handling complex MCQs and providing accurate responses to SAQ-type questions in this field. These findings can guide the ongoing refinement of AI tools for specialized medical education.

Ipek Kinikoglu

Artificial intelligence (AI) has emerged as a transformative tool in education, particularly in specialized fields such as dentistry. This study evaluated the performance of four advanced AI models - ChatGPT-4o (San Francisco, CA: OpenAI), ChatGPT-o1, Gemini 1.5 Pro (Mountain View, CA: Google LLC), and Gemini 2.0 Advanced, in the Turkish Dental Specialty Examination (DUS) for 2020 and 2021. A total of 240 questions, comprising 120 questions per year from basic and clinical sciences, were analyzed. AI models were assessed based on their accuracy in providing correct answers compared to the official answer keys. For the 2020 DUS, ChatGPT-o1 and Gemini 2.0 Advanced achieved the highest accuracy rates of 93.70% and 96.80%, respectively, with net scores of 112.50 and 115 out of 120 questions. ChatGPT-4o and Gemini 1.5 Pro followed with accuracy rates of 83.33% and 85.40%. For the 2021 DUS, ChatGPT-o1 again demonstrated the highest accuracy at 97.88% (115.50 net score), closely followed by Gemini 2.0 Advanced at 96.82% (114.25 net score). Overall, ChatGPT-4o and Gemini 1.5 Pro scored lower for 2021, achieving accuracy rates of 88.35% and 93.64%, respectively. Combining results from both years (238 total questions), ChatGPT-o1 and Gemini 2.0 Advanced achieved accuracy rates of 97.46% (230 correct answers, 95% CI: 94.62%, 100.00%) and 97.90% (231 correct answers, 95% CI: 94.62%, 100.00%), respectively, significantly outperforming ChatGPT-4o (88.66%, 211 correct answers, 95% CI: 85.43%, 91.89%) and Gemini 1.5 Pro (91.60%, 218 correct answers, 95% CI: 87.75%, 95.45%). Statistical analysis revealed significant differences among the models (p = 0.0002). Pairwise comparisons demonstrated that ChatGPT-4o underperformed significantly compared to ChatGPT-o1 (p = 0.0016) and Gemini 2.0 Advanced (p = 0.0007) after Bonferroni correction. The consistently high accuracy rates and narrow confidence intervals for the top-performing models underscore their superior reliability and performance in answering the DUS questions. Generative AI modules such as ChatGPT-01 and Gemini 2.0 have the potential to enhance dental board exam preparation through question evaluation. While the AI modules appear to outperform humans on DUS questions, the study raises a concern about the ethical uses of AI and the true justification and value of DUS examinations as dental competency examinations. A higher level of knowledge evaluation should be considered. This research contributes to the growing body of literature on AI applications in specialized knowledge domains and provides a foundation for further exploration of its integration into dental education.

Tavishi Choudhary

Artificial Intelligence large language models have rapidly gained widespread adoption, sparking discussions on their societal and political impact, especially for political bias and its far-reaching consequences on society and citizens. This study explores the political bias in large language models by conducting a comparative analysis across four popular AI models—ChatGPT-4, Perplexity, Google Gemini, and Claude. This research systematically evaluates their responses to politically charged prompts and questions from the Pew Research Center’s Political Typology Quiz, Political Compass Quiz, and ISideWith Quiz. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances based on their training data sets. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate any of these biases. The most significant question surrounding political bias in AI is its consequences, particularly its influence on public discourse, policy-making, and democratic processes. The results of this study advocate for ethical implications for the development of AI models and the need for transparency to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex AI bias issue.