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Google’s AI chatbot “Bard”: a side-by-side comparison with ChatGPT and its utilization in ophthalmology

Eye volume 38, pages 642–645 (2024)Cite this article

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Bard AI, developed by Google, represents a significant advancement in the realm of artificial intelligence (AI) powered chatbots [1]. This innovative AI is designed to simulate human-like interactions, answering a wide array of user inquiries and prompts [1]. Having been trained on a vast dataset of text data, Bard utilizes sophisticated language models to generate comprehensive and informative responses to user inputs [1]. The mechanism behind Bard involves its large language model, specifically the Pathways Language Model 2 (PaLM 2), which has been engineered to excel in comprehending facts, logical reasoning, and mathematics [1]. The capacity of the model to effectively analyze and comprehend contextual information enables it to deliver comprehensive and precise responses to a wide array of inquiries [1, 2]. While Bard shares some similarities with ChatGPT, Bard AI distinguishes itself through its emphasis on factual accuracy. Bard demonstrates exceptional proficiency in understanding and answering questions that require precise information [1]. In contrast, ChatGPT, a generative language model, is proficient in generating creative and diverse types of text, such as articles, code, scripts, and more which may or may not be completely accurate [3].

One of the fundamental differences between Bard AI and ChatGPT lies in their data sources and connections to the internet. Bard AI is linked to Google’s expansive search network, granting it access to real-time information from the internet [1]. This allows Bard AI to provide up-to-date information about current events and topics [1]. On the other hand, ChatGPT is not directly connected to the internet, and leverages data up to 2021 to generate its responses [3, 4]. ChatGPT has already shown considerable potential in the field of ophthalmology, from helping triage ophthalmic symptoms [5], medical education [6], answering ophthalmic board certification practice questions [7], writing cataract surgery operative notes [8], developing ophthalmology AI algorithms to classify OCT images [9].

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Fig. 1: LLM-generated outputs.
Fig. 2: LLM-generated outputs.
Fig. 3: LLM-generated outputs.
Fig. 4: Output generated from Bard.

References

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Author information

Authors and Affiliations

  1. Department of Ophthalmology, University of Cambridge, Cambridge, UK

    Ethan Waisberg

  2. Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, USA

    Joshua Ong

  3. University College Dublin School of Medicine, Belfield, Dublin, Ireland

    Mouayad Masalkhi

  4. Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA

    Nasif Zaman, Prithul Sarker & Alireza Tavakkoli

  5. Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA

    Andrew G. Lee

  6. Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew G. Lee

  7. The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA

    Andrew G. Lee

  8. Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA

    Andrew G. Lee

  9. Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA

    Andrew G. Lee

  10. University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Andrew G. Lee

  11. Texas A&M College of Medicine, Bryan, TX, USA

    Andrew G. Lee

  12. Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA

    Andrew G. Lee

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EW and JO: writing, figure development. MM: writing. NZ, PS, AGL and AT: review, intellectual support.

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Correspondence to Ethan Waisberg.

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Waisberg, E., Ong, J., Masalkhi, M. et al. Google’s AI chatbot “Bard”: a side-by-side comparison with ChatGPT and its utilization in ophthalmology. Eye 38, 642–645 (2024). https://doi.org/10.1038/s41433-023-02760-0

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