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Schools and schooling in the digital age : a critical analysis. 'Las escuelas y la educación en la era digital : un análisis crítico'

Schools and schooling in the digital age: a critical analysis, by Neil Selwyn, London, Routledge, 2011, 177 pp., £24.99 (paperback), ISBN 978-0-415-58930-7 It might seem curious that this book, whi...

Schools and schooling in the digital age: a critical analysis

In this study, the author collected tweets about ChatGPT, an innovative AI chatbot, in the first month after its launch. A total of 233,914 English tweets were analyzed using the latent Dirichlet allocation (LDA) topic modeling algorithm to answer the question “what can ChatGPT do?”. The results revealed three general topics: news, technology, and reactions. The author also identified five functional domains: creative writing, essay writing, prompt writing, code writing, and answering questions. The analysis also found that ChatGPT has the potential to impact technologies and humans in both positive and negative ways. In conclusion, the author outlines four key issues that need to be addressed as a result of this AI advancement: the evolution of jobs, a new technological landscape, the quest for artificial general intelligence, and the progress-ethics conundrum.

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"What Can ChatGPT Do?" Analyzing Early Reactions to the Innovative AI Chatbot on Twitter

Efforts to apply artificial intelligence (AI) and machine learning in the medical field and specifically in neurosurgery are actively underway.1-7 Currently, imaging-based diagnosis, disease diagnosis, and methods of prediction using clinical data and genomic data are fields that receive the most attention.3-6,8-11 However, AI technologies associated with natural language processing are also being investigated.12 Conversational AI is an application of natural language processing that refers to AI technologies capable of communicating with people.13 These include chatbots or virtual agents using generative pretraining transformer (GPT) language models. These technologies are rapidly advancing and are poised to make a significant contribution to medical research and patient care. Multiple groups have already put various conversational AI platforms to use them foradvance patient care including taking down medical histories, assisting in psychotherapy, and improving care in chronic conditions.7,13-16 However, the conventional conversational AI platforms were limited with mostly structured rules of engagement. ChatGPT has new potential roles in both patient care and research including producing primary documents and assisting in research conceptualization/design. We gave a ChatGPT (OpenAI LLC) with the following prompt: Write me a research editorial for the journal: Neurosurgery about how we can incorporate chatbots into neurosurgical research and neurosurgical patient care and received an insightful and well-written correspondence completely generated by the software. We comment on how as a community we need to carefully embrace these powerful technologies to improve neurosurgical research and patient outcomes. “Integrating Chatbot Technology into Neurosurgical Research and Patient Care: A Research Editorial Over the past few years, chatbot technology has made significant strides in the field of health care, with chatbots being used to assist with tasks such as patient education, appointment scheduling, and symptom management. The use of chatbots in neurosurgery is a relatively new area of exploration, but it has the potential to revolutionize the way we conduct research and deliver care to patients with brain and nervous system conditions. One potential application of chatbot technology in neurosurgery is in the field of research. Chatbots can be used to gather data from large numbers of patients, streamlining the process of data collection and analysis. This can be particularly useful in longitudinal studies, where chatbots can be used to track patient outcomes over time. Chatbots can also be used to administer surveys or questionnaires to patients, allowing researchers to gather data on patient experiences and preferences. Another potential use of chatbot technology in neurosurgery is in the delivery of patient care. Chatbots can be used to provide patients with timely and accurate information about their care and treatment, helping to alleviate anxiety and confusion. Chatbots can also be used to triage patients with urgent issues, reducing the burden on emergency departments and allowing patients to receive care more quickly. In addition, chatbots can be used to facilitate communication between patients and their health care providers, allowing patients to receive care remotely and reducing the need for in-person visits. There are, of course, challenges to the integration of chatbot technology into neurosurgery. One concern is the potential for chatbots to misdiagnose or provide incorrect information, which could have serious consequences for patients. It is important for chatbots to be carefully designed and tested to ensure their accuracy and reliability. In addition, there may be privacy and security concerns surrounding the use of chatbots because sensitive patient information could be transmitted through chatbot conversations. These concerns will need to be carefully addressed as chatbot technology is developed and implemented in the field of neurosurgery. In conclusion, chatbot technology has the potential to revolutionize the way we conduct research and deliver care to patients with brain and nervous system conditions. Although there are challenges to be addressed, the potential benefits of chatbots in neurosurgery are significant, and it is important for researchers and health care providers to explore and invest in this promising area of technology.” (Text generated by OpenAI's Assistant AI:https://openai.com). As with anyone currently exploring ChatGPT technologies, we were impressed with the ease of use and abilities of the chatbot. However, because it becomes easier to write a manuscript than it is to read a manuscript or to have a chatbot respond to patient questions and triage medical necessity, the medical field and neurosurgery as its own community must begin thinking now about how to best integrate these advances into our profession. We must consider the ethics of using content generating AI. We must carefully navigate and control the dialog surrounding the most glaring controversies intrinsic to the use chatbot technology in medical care and scientific research, including Privacy concerns: Chatbot technology contains an intrinsic risk of compromising patient privacy. Chatbots may be used to collect and store sensitive health information, which could be vulnerable to breaches or misuse. Ethical concerns: In an academic setting, it is especially important to follow proper citation guidelines and to disclose the use of chatbot-generated content in works because failing to do so could be considered academic misconduct. Bias: There remains a serious risk of bias in using chatbot technology for recommendations or treatment plans and for compiling manuscripts. As chatbots are trained on available data, if that data are biased, it may perpetuate or amplify existing inequalities in published data. Legal liabilities: There are also concerns about legal liabilities when chatbot technology is used in medical care and research. For example, if a chatbot provides incorrect or harmful advice, who is responsible for the consequences? Validity: The content produced by a conversational AI platform must be validated, and its accuracy must be judged. Effectiveness: Finally, there is some debate about the effectiveness of chatbot technology in medical care and research. Although chatbots may be able to provide some basic information and assistance, they are at this time, likely unable to fully replace human health care providers in all cases. Incorporation of these technologies is poised to make significant contributions in medical research and patient care. Medicine as a whole must consider the use of chatbot/machine-generated content and ensure that it is appropriate for its intended purpose. It remains our human responsibility to verify machine content and ensure that it meets the moral and ethical standards and requirements of our fields and to recognize bias should it occur. Neurosurgery has always embraced innovation and should lead the way in introducing these AI technologies into the larger health care and academic environment. The future is now. 

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care….

An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT’s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT’s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment.

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What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature

Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility.
Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation.
Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion.
Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored.
Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.

Background In knowledge transfer for educational purposes, most cancer hospital or center websites have existing information on cancer health. However, such information is usually a list of topics that are neither interactive nor customized to offer any personal touches to people facing dire health crisis and to attempt to understand the concerns of the users. Patients with cancer, their families, and the general public accessing the information are often in challenging, stressful situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of older patients with cancer, to go through the treatment process. Therefore, having someone with professional knowledge who can listen to them and provide the medical information with good will and encouragement would help patients and families struggling with critical illness, particularly during the lingering pandemic. Objective This study created a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This chatbot was created using the IBM Watson Assistant with artificial intelligence and machine learning features. The chatbot or bot was incorporated into a resource that can be easily accessed by the general public. Methods The radiation treatment process in a cancer hospital or center was described by the radiotherapy process: patient diagnosis, consultation, and prescription; patient positioning, immobilization, and simulation; 3D-imaging for treatment planning; target and organ contouring; radiation treatment planning; patient setup and plan verification; and treatment delivery. The bot was created using IBM Watson (IBM Corp) assistant. The natural language processing feature in the Watson platform allowed the bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as intents during development. Therefore, the bot can be trained using the responses received, by recognizing similar responses from the user and analyzing using Watson natural language processing. Results The bot is hosted on a website by the Watson application programming interface. It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the bot. The bot was tested by potential users, and the overall averages of the identified metrics are excellent. The bot can also acquire users’ feedback for further improvements in the routine update. Conclusions An artificial intelligence–assisted chatbot was created for knowledge transfer regarding radiation treatment process to the patients with cancer, their families, and the general public. The bot that is supported by machine learning was tested, and it was found that the bot can provide information about radiotherapy effectively.

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Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study

BACKGROUND
In knowledge transfer for educational purposes, most cancer hospital/centre websites have existing information on cancer health. However, such information is usually a list of topics which are neither interactive nor offering any personal touches to people facing dire health crisis, not to say attempting to understand the concerns of the users. Cancer patients, their families and the general public accessing the information are often in challenging, stressful, situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of cancer patients in senior ages, to go through the treatment process. Therefore, having "someone" with professional knowledge who can "listen" to them, provide the medical information with good will and encouragement would help patients and families struggling with critical illness, in particular during the lingering pandemic.


OBJECTIVE
The goal of this work is to create a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This virtual assistant was created using the IBM WATSON assistant with artificial intelligence (AI) and machine learning (ML) features. The chatbot or Bot was incorporated into a resource that can be easily accessed by the general public.


METHODS
The radiation treatment process in a cancer hospital/centre was described by the radiotherapy process: (1) patient diagnosis, consultation and prescription, (2) patient positioning, immobilization and simulation, (3) 3D imaging for treatment planning, (4) target and organ contouring, (5) radiation treatment planning, (6) patient setup and plan verification, and (7) treatment delivery. The Bot was created using IBM WATSON assistant. The natural language processing (NLP) feature in the WATSON platform allowed the Bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as "intents" during development. The Bot therefore can be trained from the responses received, by recognizing similar responses from the user and analysed by the WATSON NLP.


RESULTS
The Bot is hosted on a website by the WATSON application programming interface (API). It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the Bot. The Bot was tested by potential users and the overall averages of the identified metrics are excellent. The Bot can also acquire users' feedbacks for further improvements in the routine update.


CONCLUSIONS
An AI-assisted chatbot was created for the knowledge transfer of radiation treatment process to the cancer patients, their families and general public. The Bot with character supported by ML was tested, and it was found that the bot can provide information in radiotherapy effectively.


CLINICALTRIAL

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Learning the Treatment Process in Radiotherapy: An Al-assisted Chatbot.

Context: In cancer centres and hospitals particularly during the pandemic, there was a great demand for information, which could hardly be handled by the limited manpower available. This necessitated the development of an educational chatbot to disseminate topics in radiotherapy customized for various user groups, such as patients and their families, the general public and radiation staff. Objective: In response to the clinical demands, the objective of this work is to explore how to design a chatbot for educational purposes in radiotherapy using artificial intelligence. Methods: The chatbot is designed using the dialogue tree and layered structure, incorporated with artificial intelligence features such as natural language processing (NLP). This chatbot can be created in most platforms such as the IBM Watson Assistant and deposited in a website or various social media. Results: Based on the question-and-answer approach, the chatbot can provide humanlike communication to users requesting information on radiotherapy. At times, the user, often worried, may not be able to pinpoint the question exactly. Thus, the chatbot will be user friendly and reassuring, offering a list of questions for the user to select. The NLP system helps the chatbot to predict the intent of the user so as to provide the most accurate and precise response to him or her. It is found that the preferred educational features in a chatbot are functional features such as mathematical operations, which should be updated and modified routinely to provide new contents and features. Conclusions: It is concluded that an educational chatbot can be created using artificial intelligence to provide information transfer to users with different backgrounds in radiotherapy. In addition, testing and evaluating the performance of the chatbot is important, in response to user’s feedback to further upgrade and fine-tune the chatbot.

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Design of an Educational Chatbot Using Artificial Intelligence in Radiotherapy

COPYRIGHT © 2023 Chow, Sanders and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Impact of ChatGPT on medical chatbots as a disruptive technology

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Kay Li

Papers

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review.

Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study

Learning the Treatment Process in Radiotherapy: An Al-assisted Chatbot.

Design of an Educational Chatbot Using Artificial Intelligence in Radiotherapy

Impact of ChatGPT on medical chatbots as a disruptive technology