Google DeepMind’s gemini AI versus ChatGPT: a comparative analysis in ophthalmology

Masalkhi, Mouayad; Ong, Joshua; Waisberg, Ethan; Lee, Andrew G.

doi:10.1038/s41433-024-02958-w

Cited by 38 publications

(4 citation statements)

References 13 publications

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“…However, according to our knowledge, this is the first study in the literature to investigate both the diagnostic accuracy and appropriateness of triage recommendations of LLMs in response to maxillofacial trauma cases. Moreover, given the recent release of GEMINI, few studies that compare these advanced LLMs have been conducted [17,18]. Our findings confirmed that LLMs hold promise in trauma triage, but improvements are necessary for reliable implementation in clinical practice.…”

Section: Discussionsupporting

confidence: 72%

The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study

Frosolini,

Catarzi,

Benedetti

et al. 2024

Diagnostics

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Background: In the evolving field of maxillofacial surgery, integrating advanced technologies like Large Language Models (LLMs) into medical practices, especially for trauma triage, presents a promising yet largely unexplored potential. This study aimed to evaluate the feasibility of using LLMs for triaging complex maxillofacial trauma cases by comparing their performance against the expertise of a tertiary referral center. Methods: Utilizing a comprehensive review of patient records in a tertiary referral center over a year-long period, standardized prompts detailing patient demographics, injury characteristics, and medical histories were created. These prompts were used to assess the triage suggestions of ChatGPT 4.0 and Google GEMINI against the center’s recommendations, supplemented by evaluating the AI’s performance using the QAMAI and AIPI questionnaires. Results: The results in 10 cases of major maxillofacial trauma indicated moderate agreement rates between LLM recommendations and the referral center, with some variances in the suggestion of appropriate examinations (70% ChatGPT and 50% GEMINI) and treatment plans (60% ChatGPT and 45% GEMINI). Notably, the study found no statistically significant differences in several areas of the questionnaires, except in the diagnosis accuracy (GEMINI: 3.30, ChatGPT: 2.30; p = 0.032) and relevance of the recommendations (GEMINI: 2.90, ChatGPT: 3.50; p = 0.021). A Spearman correlation analysis highlighted significant correlations within the two questionnaires, specifically between the QAMAI total score and AIPI treatment scores (rho = 0.767, p = 0.010). Conclusions: This exploratory investigation underscores the potential of LLMs in enhancing clinical decision making for maxillofacial trauma cases, indicating a need for further research to refine their application in healthcare settings.

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Section: Discussionsupporting

confidence: 72%

The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study

Frosolini,

Catarzi,

Benedetti

et al. 2024

Diagnostics

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“…Apart from GPT-4, Gemini (Google's AI) and LlaMa (Meta's AI) are promising AI chatbots with image analysis capabilities. To date, this ability seems to be far from application in clinical practice [3], because the evaluation of medical images is complicated by many factors. Nevertheless, the integration of chatbots with existing AI systems, such Face2Gene, might improve the ability to recognize rare disorders [99].…”

Section: Discussionmentioning

confidence: 99%

“…In the realm of healthcare, GAI has the potential to enhance various activities; for example, DALL-E can analyze patient medical images to aid in diagnosis [2]. Similarly, Gemini AI can process images, even with low performance at the moment [3]. Some advanced GAI models are large language models (LLMs) that have been developed to understand and generate human-like languages.…”

Section: Introductionmentioning

confidence: 99%

Innovations in Medicine: Exploring ChatGPT’s Impact on Rare Disorder Management

Zampatti,

Peconi,

Megalizzi

et al. 2024

Genes

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Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field’s potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

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“…OpenAI's ChatGPT applications span across domains including language translation, text summarization, and creative writing assistance, making it an adaptable and scalable tool for researchers exploring complex topics and generating insights from textual data. Meanwhile, Google's Gemini, a newer entrant, offers broader capabilities as a multimodal AI, integrating language and image processing [3]. Continuously evolving algorithms ensure that both models improve based on user interactions, providing up-to-date and accurate information.…”

Section: Introductionmentioning

confidence: 99%

AI for Biophysical Phenomena: A Comparative Study of ChatGPT and Gemini in Explaining Liquid–Liquid Phase Separation

Rana,

Katoch

2024

Applied Sciences

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Recent advancements in artificial intelligence (AI), notably through generative pretrained transformers, such as ChatGPT and Google’s Gemini, have broadened the scope of research across various domains. Particularly, the role of AI in understanding complex biophysical phenomena like liquid–liquid phase separation (LLPS) is promising yet underexplored. In this study, we focus on assessing the application of these AI chatbots in understating LLPS by conducting various interactive sessions. We evaluated their performance based on the accuracy, response time, response length, and cosine similarity index (CSI) of their responses. Our findings show that Gemini consistently delivered more accurate responses to LLPS-related questions than ChatGPT. However, neither model delivered correct answers to all questions posed. Detailed analysis showed that Gemini required longer response times, averaging 272 words per response compared to ChatGPT’s 351. Additionally, the average CSI between the models was 0.62, highlighting moderate similarity. Despite both models showing potential to enhance scientific education in complex domains, our findings highlight a critical need for further refinement of these AI tools to improve their accuracy and reliability in specialized academic settings.

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Google DeepMind’s gemini AI versus ChatGPT: a comparative analysis in ophthalmology

Cited by 38 publications

References 13 publications

The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study

The Role of Large Language Models (LLMs) in Providing Triage for Maxillofacial Trauma Cases: A Preliminary Study

Innovations in Medicine: Exploring ChatGPT’s Impact on Rare Disorder Management

AI for Biophysical Phenomena: A Comparative Study of ChatGPT and Gemini in Explaining Liquid–Liquid Phase Separation

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