Exploring Chatgpt: An Extensive Examination of its Background, Applications, Key Challenges, Bias, Ethics, Limitations, and Future Prospects

Pathak, A.J.

doi:10.2139/ssrn.4499278

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…With their ability to process vast amounts of textual data and engage users in interactive conversations, GPTbased chatbots hold the promise of offering recommendations that are not only accurate but also more contextually relevant and engaging than ever before [13,14]. By leveraging natural language processing (NLP) and DL techniques, these chatbots can better understand user intentions and preferences and deliver personalized suggestions that align with individual tastes and evolving interests [15]. They can also provide new ways of delivering filtered information to the users, thus improving the quality of the experience for them.…”

Section: Introductionmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

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Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.

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Section: Introductionmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

View full text Add to dashboard Cite

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Conversational and generative artificial intelligence and human–chatbot interaction in education and research

Akpan,

Kobara,

Owolabi

et al. 2024

Int Trans Operational Res

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Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

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Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review

Ghanta,

Al’Aref,

Lala-Trinidade

et al. 2024

Diagnostics

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Heart failure (HF) is a leading cause of mortality, morbidity, and financial burden worldwide. The emergence of advanced artificial intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) systems, presents new opportunities to enhance HF management. In this review, we identified and examined existing studies on the use of ChatGPT in HF care by searching multiple medical databases (PubMed, Google Scholar, Medline, and Scopus). We assessed the role of ChatGPT in HF prevention, diagnosis, and management, focusing on its influence on clinical decision-making and patient education. However, ChatGPT faces limited training data, inherent biases, and ethical issues that hinder its widespread clinical adoption. We review these limitations and highlight the need for improved training approaches, greater model transparency, and robust regulatory compliance. Additionally, we explore the effectiveness of ChatGPT in managing HF, particularly in reducing hospital readmissions and improving patient outcomes with customized treatment plans while addressing social determinants of health (SDoH). In this review, we aim to provide healthcare professionals and policymakers with an in-depth understanding of ChatGPT’s potential and constraints within the realm of HF care.

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Exploring Chatgpt: An Extensive Examination of its Background, Applications, Key Challenges, Bias, Ethics, Limitations, and Future Prospects

Cited by 3 publications

References 14 publications

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review

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