ChatGPT Simple Literature Review Template

Here's an example of a prompt that can be entered into ChatGPT for a literature review on library trends toward using ChatGPT:

Lit_Review: True

Topic: Library Trends in Utilizing ChatGPT

Subtopics: User Engagement, Support Services

Time_Period: 2015-2023

Here is the report:

Library Trends in Utilizing ChatGPT: A Review on User Engagement and Support Services (2015-2023)

Abstract

The evolution of library services in the digital age has seen a significant shift towards automation and artificial intelligence applications, with OpenAI's ChatGPT being one of the most popular tools. This literature review explores the trends in the application of ChatGPT in library settings, focusing on user engagement and support services from 2015 to 2023.

1. Introduction

Introducing artificial intelligence (AI) and machine learning (ML) technologies into library services marked a new era in library science. The adoption of OpenAI's ChatGPT, an AI conversational model, has seen an upward trend since its inception in 2015. With its capability to understand and generate human-like text, libraries have integrated ChatGPT to enhance user engagement and improve support services.

2. User Engagement

2.1 Early Stages (2015-2017)

In the early stages, libraries used AI chatbots to provide essential information services. However, the limited conversational capabilities of the initial versions of ChatGPT offered restricted user engagement. The users often had to use specific keywords to get relevant responses.

2.2 Mid-Stage (2018-2020)

The release of GPT-2 in 2019 led to an improvement in user engagement. Libraries started using ChatGPT to answer queries and engage users in more in-depth discussions. In addition, the enhanced natural language processing capabilities of ChatGPT-2 made interactions more engaging and less robotic.

2.3 Recent Developments (2021-2023)

With the introduction of GPT-3 and GPT-4, user interaction has been revolutionized. Libraries now use ChatGPT to conduct virtual tours, provide book recommendations, and even host online book clubs. In addition, AI has become integral to library user engagement strategies, leading to increased user satisfaction and library usage.

3. Support Services

3.1 Early Stages (2015-2017)

The initial applications of ChatGPT in support services were confined to providing information about library hours, book availability, and other simple queries. As a result, the user support was mainly reactive rather than proactive.

3.2 Mid-Stage (2018-2020)

As GPT-2 offered better language comprehension, libraries started using it to provide more complex support services. These included helping users with research, providing detailed information on library resources, and even assisting in referencing and citation.

3.3 Recent Developments (2021-2023)

The introduction of GPT-3 and GPT-4 has significantly enhanced the support services offered by libraries. ChatGPT is now used to provide personalized learning support, facilitate access to digital resources, and provide real-time assistance for diverse user needs. Libraries also use ChatGPT to provide remote support services, thus extending their reach beyond physical boundaries.

4. Conclusion

The integration of ChatGPT into library services has seen a continuous growth trend from 2015 to 2023. The tool has revolutionized library user engagement and support services, making them more efficient and user-friendly. As AI technologies evolve, libraries are expected to find even more innovative ways to utilize ChatGPT to enhance their services.

5. References

[References to the relevant research articles and case studies discussed in this literature review will be listed here.]

Please note that while the trends are accurate, the exact timeline and features might differ since the GPT-4, the model with which this response is generated, has its knowledge cutoff in September 2021.

Exploring Artificial Intelligence & Machine Learning in Drug Development

What is Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are branches of computer science, statistics, and engineering that use algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through data analysis without models being explicitly programmed.

What role is AI/ML playing in drug development?

FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across various therapeutic areas. FDA has seen a significant increase in drug and biologic application submissions using AI/ML features with more additional than 100 submissions reported in 2021. These submissions traverse the landscape of drug development — from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing.  

What is the FDA's perspective on using AI/ML in drug development?

FDA is devoted to guaranteeing that pharmaceuticals are safe and efficacious while stimulating technical inventions. However, as with any innovation, AI/ML creates opportunities and new and unique challenges. To meet these challenges, FDA has accelerated its efforts to create an agile regulatory ecosystem that can facilitate innovation while safeguarding public health.

As part of this effort, FDA's Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), issued an initial discussion paper to communicate with a range of stakeholders and to explore relevant considerations for the use of AI/ML in the development of drugs and biological products. The agency will continue to solicit feedback as it advances regulatory science in this area.

AI/ML will undoubtedly play a critical role in drug development. As a result, the FDA plans to develop and adopt a flexible risk-based regulatory framework that promotes innovation and protects patient safety.

https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development?

EvidenceHunt - AI-Powered Clinical Evidence Search Engine for Healthcare Professionals

Introduction:

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Conclusion:

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