mlsanityData to 7 Oct 2026

A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions

arXiv 2305.00237 ↗First posted 29 Apr 2023cs.CY cs.AIcited by 12

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Abstract (arXiv)

ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google Bard and Meta LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 66 references that are in the map. First posted on arXiv 29 Apr 2023.

Builds on

24 of 66 references.

References · 24

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  5. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  7. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  8. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 487
  9. 2022Multimodal Learning with Transformers: A Surveycited by 399
  10. 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 220
  11. 2017Learning to Represent Programs with Graphscited by 219
  12. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
12 more
  1. 2020Explainable deep learning models in medical image analysiscited by 111
  2. 2020Adversarial Training for Large Neural Language Modelscited by 109
  3. 2019Towards Explainable Artificial Intelligencecited by 106
  4. 2021Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videoscited by 86
  5. 2021Towards Improving Adversarial Training of NLP Modelscited by 79
  6. 2020XGPT: Cross-modal Generative Pre-Training for Image Captioningcited by 51
  7. 2019Improving Neural Language Modeling via Adversarial Trainingcited by 49
  8. 2022Rethinking Attention-Model Explainability through Faithfulness Violation Testcited by 43
  9. 2018Visual Analytics for Explainable Deep Learningcited by 39
  10. 2022Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysiscited by 22
  11. 2022Local Interpretable Model Agnostic Shap Explanations for machine learning modelscited by 3
  12. 2022Generative Adversarial Training Can Improve Neural Language Modelscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 7; 2025: 0; 2026: 1.

12 papers.

  1. 2024A Complete Survey on LLM-based AI Chatbotscited by 71
  2. 2024Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracycited by 66
  3. 2024All in How You Ask for It: Simple Black-Box Method for Jailbreak Attackscited by 30
  4. 2024Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practicecited by 25
  5. 2023The Moral Machine Experiment on Large Language Modelscited by 24
  6. 2024AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracycited by 21
  7. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  8. 2023Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the modelcited by 15
  9. 2024FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examinationcited by 10
  10. 2024Large-scale moral machine experiment on large language modelscited by 9
  11. 2026The Fragility Of Moral Judgment In Large Language Modelscited by 8
  12. 2023InteraSSort: Interactive Assortment Planning Using Large Language Modelscited by 2