Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Lee, Peter; Bubeck, Sébastien; Petro, Joseph

doi:10.1056/nejmsr2214184

Cited by 886 publications

(483 citation statements)

References 5 publications

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“…GPT-4 has already been integrated into ChatGPT and this model seems to be more reliable, creative, and able to handle much more nuanced instructions, even if only a few technical details on the model were provided by OpenAI, for competitive and safety concerns. ( 18 ) GPT-4 impact on health care delivery and medical research is expected to be huge, but its limitations need to be taken into account ( 19 ).…”

Section: The Evolution Of Pre-trained Large Language Modelsmentioning

confidence: 99%

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Angelis

Baglivo

Arzilli

et al. 2023

Front. Public Health

289

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Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field.The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain.The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health. Infodemic is a trending topic in public health and the ability of LLMs to rapidly produce vast amounts of text could leverage misinformation spread at an unprecedented scale, this could create an “AI-driven infodemic,” a novel public health threat. Policies to contrast this phenomenon need to be rapidly elaborated, the inability to accurately detect artificial-intelligence-produced text is an unresolved issue.

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Section: The Evolution Of Pre-trained Large Language Modelsmentioning

confidence: 99%

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Angelis

Baglivo

Arzilli

et al. 2023

Front. Public Health

289

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show abstract

“…Following previous research demonstrating exchanges with LLMs (Lee et al, 2023), the ability of ChatGPT to conduct specific types of psychotherapy skills or processes was examined (excerpted and complete responses are provided in Table 1 and supplemental Table S1 respectively). In some use cases, the LLM's ability to conduct the skill is promising, e.g., conducting assessment or producing psychoeducation (Table 1, first and second rows respectively).…”

Section: Current Llms Employ Some Therapeutic Techniques Well But Oth...mentioning

confidence: 99%

Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation

Stade¹,

Stirman²,

Ungar³

et al. 2023

Preprint

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Large language models (LLMs) built on artificial intelligence (AI) – such as ChatGPT and GPT-4 – hold immense potential to support, augment, or even replace psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. Here, we provide a roadmap for ambitious yet responsible applications of clinical LLMs. First, we discuss potential applications of clinical LLMs to clinical care, training, and research, emphasizing imminent applications while highlighting areas that present risk given the high-stakes, complex nature of psychotherapy. Second, we outline a continuum of assistive to fully autonomous clinical LLM applications that could be integrated into digital treatment modalities, analogous to the development of autonomous vehicle technology. Third, we outline recommendations for the responsible development of clinical LLMs, which should center clinical science and improvement, involve robust interdisciplinary collaboration, and attend to issues like assessment, risk detection, transparency, and bias. Fourth, we offer recommendations for the critical evaluation of clinical LLMs, arguing that psychologists are uniquely positioned to scope and guide the development and evaluation of clinical LLMs. Last, we outline a vision for how LLMs might allow for a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

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“…ChatGPT is a general-purpose AI tool, designed merely to help with common tasks and not scientific writing or medical tasks. 87 We foresee that within a few years, it is possible newer large language models, with Internet access and trained for specialist medical and scientific writing, would be useful.…”

Section: Author Contributionsmentioning

confidence: 99%

Immunogenicity of coronavirus disease 2019 vaccines in children: A review with ChatGPT

Leung

Duque

et al. 2023

Pediatric Discovery

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SARS‐CoV‐2 causes millions of infection cases and coronavirus disease 2019 (COVID‐19)‐related deaths worldwide. In addition to acute illnesses, children and adolescents suffer from post‐infectious complications. Vaccination is a promising preventative treatment that can confer protection from these devastating outcomes. Utilizing ChatGPT, this review discusses the immunogenicity of mRNA and inactivated COVID‐19 vaccines in children and adolescents. Rapid vaccine discovery during the COVID‐19 pandemic led to the approval of the mRNA vaccines that stimulate potent antibody responses in pediatric population, and the younger age groups develop higher neutralizing and non‐neutralizing antibody responses than those who are older. Natural infection induces weaker antibody responses than vaccination. Vaccine‐induced humoral immunity decreases over time, as antibodies decline six months after the second dose. However, antibody avidity increases, which partly maintains neutralization and Fc‐effector functions that provide more durable protection. Inactivated COVID‐19 vaccines generate strong antibody responses in children and adolescents. They induce T cell responses against multiple structural protein antigens, although their neutralizing antibody responses appear weaker and wane more quickly than mRNA vaccines. Full‐dose intradermal administration and heterologous prime‐boost may improve the immunogenicity of inactivated vaccines. In children and adolescents, immune protection from the pre‐Omicron variants of concern (VOCs) is maintained. Vaccination induces less antibody neutralization against the Omicron variant, but non‐neutralizing antibodies and T cell responses persist. Hybrid immunity provides stronger immunogenicity against SARS‐CoV‐2 in the pediatric population. Future research must focus on long‐term immunity, interaction with breakthrough reinfections, cross‐reactivity against new VOCs, T cell immunogenicity and immunogenicity in young children.

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Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine

Cited by 886 publications

References 5 publications

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation

Immunogenicity of coronavirus disease 2019 vaccines in children: A review with ChatGPT

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