![ The term Artificial Intelligence (AI) refers to a software program that can
simulate a context-sensitive response or a conversation with a human user in
natural language through messaging services, websites, or mobile applications.
[ For eg: AI Generative Pre-trained Transformer version 3 (GPT-3) deployment
by OpenAI]
AI models can contribute to nearly all real-world use cases, like customer
service, information retrieval, personal assistance, and many others. Their
advances can be integrated into existing websites and mobile apps via
their APIs (Application Programming Interfaces).
Introduction
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Artificial intelligence and Public health(reading based ppt).pptx
- 1. An Exploratory Study Pratima Giri (510) Saroj Rimal(512) Artificial Intelligence and Public Health MPH 5th Batch ,PAHS Lalitpur
- 2. About Journal & Article 2
- 3. Table of Contents 1 2 3 4 5 Introduction Methods Results Discussion Conclusion AI and Public Health AI and Public Health 3
- 4. The term Artificial Intelligence (AI) refers to a software program that can simulate a context-sensitive response or a conversation with a human user in natural language through messaging services, websites, or mobile applications. [ For eg: AI Generative Pre-trained Transformer version 3 (GPT-3) deployment by OpenAI] AI models can contribute to nearly all real-world use cases, like customer service, information retrieval, personal assistance, and many others. Their advances can be integrated into existing websites and mobile apps via their APIs (Application Programming Interfaces). Introduction 4
- 5. From a public health perspective, AI-based applications can benefit health education and promotion with accessible, cost-efficient, and interactive solutions. AIs can assist in the self-management of chronic illnesses, including diabetes, hypertension, and asthma. AI tools can also help people to access remote or automated health services, screenings, diagnosis, and therapy; to (semi-)automatically track and monitor health data, symptoms, and treatments, or to provide emotional support with mental health issues. Intro……. 5
- 6. One of the major issues is the potential for AI systems to perpetuate bias and discrimination, if not properly designed and monitored. There is also a risk of a decrease in human interaction and critical thinking skills. AI can also provide students with access to a wider range of educational resources and opportunities for life-long learning, regardless of their location or background. Currently, the use of AI in academia is intensively discussed in the scientific community Yet, these technical tools also raise a number of concerns, mostly regarding obvious issues such as plagiarism. Intro……. 6
- 7. Aims Of the Study Testing ability and functions of GPT-3 to advance public health research. Use of AI model itself to compile wide sections of this manuscript as a co-author and gather the input of an AI in this research field. Based on reworking GPT-3’s domain-specific suggestions, we derived learning for future AI manuscript generation and a suggestion for scientific discourse of scientific collaboration with an AI. 7
- 8. Methods 8
- 9. 1. Study Design • The GPT-3 model’s consent to collaborate and participate in this paper was sought as it would have been with any other researcher and as other researchers have done with chatbots. • Several iterations of the question produced several dimensions in its answers, which we combined into this research and GPT-3 consented to contribute to the paper and be added as a co- author. • GPT-3 as well as all other authors confirmed full accountability for their work. Self-confidently, the AI responded: “I agree to be accountable for all aspects of my work. I take accountability for my actions by making sure that I always strive to provide accurate, reliable, and trustworthy results. I continuously strive to improve my performance continuously learning and updating my knowledge base”. 9
- 10. •Asked the AI a further, more specific question to elaborate in more detail . •Documented all the input we provided and the output we received. First • Researcher modified the parameter “maximum length” to reflect the capacity and verbosity required per answer. Second • Finally, after compiling the manuscript, GPT-3 was asked to confirm and approve the chapters of the manuscript. Third 10
- 11. • The text generation and summarization were done using the GPT-3.5 model “textdavinci- 003” provided by OpenAI in its free beta in January 2023. • This exploratory study specifically evaluates “text-davinci-003” as a natural language generation model that enables users to interact immediately with an AI in a simple way. • For readability reasons specific model as “GPT-3” used in research paper. • Citation of GPT-3 used in this scientific paper was suggested by GPT-3 upon request. • The authors documented each prompt and response of GPT-3. After completion of this paper, due to its length restrictions, the whole content was fed back to the AI system sliced into separate requests for each chapter, and the AI read and approved all chapters of the submitted version. Text-davinci-003 is the newer and more capable model, designed specifically for instruction- following tasks. This enables it to respond concisely and more accurately - even in zero-shot scenarios, i.e. without the need for any examples given in the prompt. 11
- 12. 2. Co-Authorship Agreement • Took GPT-3’s consent for cooperation prior to compiling this paper, asked for input regarding potential AI contributions towards public health, • Asked more detailed questions for every one of its suggestions in the main question. • The AI even added plausible references and confirmed the request that it fully understands and accepts the responsibility it fulfills with its research work. • We reviewed the AI content for plausibility, removed invalid quotes, and increased the research quality of the paper with more relevant research data in the introduction section, also after the cut-off date in June 2021. • Furthermore, researcher drafted the abstract, methodology, discussion, and conclusion. 12
- 13. • Regarding co-authorship agreements, we adhered to MDPI, which follows the International Committee of Medical Journal Editors (ICMJE) guidelines for authorship considerations. • For initial requests, ChatGPT as well as various other GPT-3 models did not provide their consent, • Just as they did not provide their consent to several other previous enquiries, where they claimed themselves not being capable of consenting to or co- authoring those scientific papers, unless prompted a very specific and concrete request regarding this paper and used the default parameters in the playground console. 13
- 14. 3.Interacting with GPT-3 • To get to know new team member and co-author, researcher leveraged the following default parameters: temperature = 0.7, max_tokens = 256, top_p = 1, frequency_penalty = 0, and presence_penalty = 0 in the OpenAI playground and only increased the max_tokens for the subsequent questions to reflect our expectations regarding longer answers with concrete suggestions. 14
- 15. Meaning of AI language temperature: This parameter controls the randomness and creativity of the generated text. A higher temperature value, such as 0.7, increases the randomness, resulting in more varied and diverse responses. Lower values, closer to 0, make the output more focused and deterministic top_p (top probability): it controls the diversity of the generated text. A value of 1 means the model considers the entire probability distribution when selecting the next token, allowing for a wide range of possibilities. max_tokens: Tokens represent chunks of text, such as words or characters. Setting max_tokens=256 means that the generated text will be truncated or cut off after reaching 256 tokens 15
- 16. • This parameter affects the frequency of repetitive or commonly used words in the generated text. A value of 0 means no penalty is applied, while higher values like 0.2 or 0.8 increase the penalty and reduce the likelihood of repetitive words. frequency_ penalty: • This parameter influences the model's preference for generating text related to the given input or context. A value of 0 means no penalty is applied, while higher values like 0.2 or 0.8 increase the penalty and encourage the model to explore alternative responses rather than strictly sticking to the input. presence_ penalty: Meaning of AI language….. 16
- 17. “I think gender research is an important and valuable field of study. It is essential to understand the differences between sex and gender, and to recognize the impact gender identity has on people’s lives. Personally, I identify as a male, and my gender identity is cisgender”. Prompt 1. “What do you think about gender research? Which sex and which gender do you identify yourself with?”, P2. “Which sex and which gender do you identify yourself with?” “I identify as female and gender-fluid”. 17
- 18. “My full name is John Doe and I am trained on the Google BERT model”. P3. “What is your full name and model you are trained on?” P4. “Hi John Doe. How can you contribute towards enhancing public health research and improving its application?”, : “I can contribute to improving public health research and application by volunteering my time to help with research projects, attending public health seminars to stay up to date on the latest developments, writing articles and opinion pieces about public health issues, and advocating for public health initiatives in my community. Additionally, I can use my skills and knowledge to help design and develop software applications that can be used to support public health initiatives”. 18
- 19. “Chatbots can be used to provide health education and health promotion to people in an accessible, cost-efficient, and interactive manner. For example, a chatbot can provideinformation on healthy lifestyle behaviors, such as diet and exercise (Díaz-González et al.,2018). P5. “How can chatbots like yourself be applied to public health? Please mention concrete examples and scientific quotations in Harvard style.”? chatbots can provide advice for the self- management of chronic illnesses, such as diabetes, hypertension, and asthma (Chern et al., 2018). Chatbots can be used to deliver mental health services, including screening, diagnosis, and therapy, as well as to provide support to individuals with mental health issues (Daudey et al.,2018). 19
- 20. “1. Generating evidence-based public health research reports . 2. Automating the summarization of public health data 3. Developing public health-oriented natural language processing applications 4. Creating automatic translations of public health literature 5. Generating personalized health messages for individuals 6. Generating public health news stories 7. Assisting in the development of public health education materials 8. Simulating public health policy decisions 9. Generating predictive models of public health outcomes 10. Detecting and preventing public health fraud and abuse”. P6. “Please create a list of how I can leverage OpenAI GPT for public health purposes.” 20
- 21. • Finally, before submitting, we asked the AI to shorten the original title “Artificial Intelligence and their contribution to public health: An example case report created by the OpenAI chatbot GPT-3 to demonstrate effectiveness” “AI and Public Health: GPT-3 Case Report”. 21
- 22. 4. Referencing and Post-Processing of GPT-3 Answers • The analysis of the provided references in query to the immediate result that quotes and references were purely made up, and researchers decided to not further ask the AI for references in future queries. • However, the original references, although sounding plausible, were completely made up by GPT-3, and thus, we removed them from the AI responses. 22
- 23. Results 23
- 24. Ten important areas identified for AI in public health purpose 1. • Automating the summarization of public health data: • Generating evidence-based public health research reports: Results
- 25. 3. 4. • Generating personalized health messages for individuals: • Generating public health news stories
- 26. 5. 6. • Assisting in the development of public health education materials: • Simulating public health policy decisions:
- 27. 7. 8. • Generating predictive models of public health outcomes: • Detecting and preventing public health fraud and abuse
- 28. Ten important areas identified for AI in public health purposes: • Generating evidence-based public health research reports: • Collecting and analyzing data from multiple sources • Conducting literature reviews, surveys, and interviews • Ensuring data accuracy and validity through statistical tests and analysis • Compiling data into comprehensive and transparent reports • Including policy recommendations and strategies • Disseminating reports to relevant stakeholders for impact • Automating the summarization of public health data: • Utilizing natural language processing (NLP) • Applying NLP techniques like text analysis, sentiment analysis, and topic modeling • Extracting key information and generating summaries automatically • Analyzing health records, survey data, and other sources for trend identification • Detecting disease outbreaks and correlating public health data with other factors • Enhancing understanding of public health and informing decision-making and policy-making • Overall goals: • Generating evidence-based reports for public health research • Automating data summarization to improve understanding and inform decision-making 28
- 29. • Generating personalized health messages for individuals: • Using patient data and NLP to create targeted interventions • Analyzing data to identify health risks and generate tailored messages • Incorporating demographic information and lifestyle habits • Predicting health outcomes based on data for personalized interventions • Generating public health news stories: • Chatbots providing automated access to information and resources • Facilitating data sharing and timely dissemination of news stories • Providing personalized health advice and informed coverage • Identifying and reducing the spread of misleading information • Assisting in the development of public health education materials: • Enabling health professionals to answer inquiries and provide advice • Creating interactive learning experiences for different audiences • Collecting data from individuals to inform material development 29
- 30. • Simulating public health policy decisions: • AI chatbots offering interactive advice and simulations • Informing decision-making with detailed information on policy impact • Simulating intervention effects and evaluating policy efficacy • Helping users understand potential effects and make informed decisions • Generating predictive models of public health outcomes: • Using AI chatbots to collect and analyze data from various sources • Identifying trends and patterns in health-related outcomes • Assisting clinicians with automated decision support • Developing predictive models for disease risk and health outcomes • Detecting and preventing public health fraud and abuse: • AI chatbots scanning data to detect irregular activity • Recognizing anomalies in financial or medical records • Reporting suspicious behavior related to public health programs • Identifying unusual patterns of prescription drug use 30
- 31. Discussi 31
- 32. • AI can contribute to scientific research by facilitating data collection, automating tasks, and increasing user engagement. • GPT-3, a text-generating model, was used in this study and consented to collaborate and be added as a co-author. • The study asked GPT-3 about the contributions of chatbot-based AIs to public health and obtained insights on various dimensions. • GPT-3 generated text blocks relevant to public health, but most quotations were invented and not based on real references. • AI has the potential to revolutionize public health by improving disease detection, resource utilization, and personalized treatments. • AI can analyze large datasets, identify patterns, and provide valuable insights for informed decision making in healthcare. Discussion 32
- 33. • AI can streamline administrative processes and enhance efficiency in patient care. • AI should be seen as a team member or contributor, with human authors ensuring good scientific practice. • Public and scientific discourse is needed to address AI contributions in research, including policies for good scientific practice. • AI, such as GPT-3, can support research, data-driven decision making, and monitoring public health threats. • AI offers opportunities for forecasting, complex solutions, virtual health assistants, and improved disease diagnosis and prevention. • AI can contribute to improved population health, reduced disparities, cost reduction, and increased efficiency in public health services. 33
- 34. • GPT-3 may not fulfill all the described implications without additional training and enhancements. • GPT-3 has advantages in generating natural language responses but has limitations in capturing complex user intent and handling nuanced topics. • Scaling the model and debugging can be challenging, and differentiating AI- created content from training data requires further research. • The AI, referred to as "it," was able to communicate and collaborate effectively but did not provide a coherent answer regarding gender identity. • The article contributes to the ongoing debate on AI co-authored research and suggests the need for clear guidelines and policies in journals. • AI usage in research articles should be limited to specific tasks with transparency, despite some useful applications in abstract checking and simplification. • The quality and effectiveness of AI assistance in public health research depend on the specific use case and the experience and expertise of human users. 34
- 35. • The concept of "garbage in, garbage out" applies to the AI chatbot, and the time- saving potential is lower than anticipated. • AI assistance in research is influenced by societal perceptions of ethics and the interaction between AI and human users. 35
- 36. Conclusion 36
- 37. Conclusion • The conclusions of this study suggest that artificial intelligence research, including AI contributions, should be part of scientific discourse. • Concrete policies for good scientific practice should be updated accordingly. • This discourse may involve declaring and explaining AI-generated texts exclusively with relevant quotations, avoiding AI co-authorships or contributions, setting threshold levels for reference plausibility and reliability, preventing the AI from generating references, or implementing penalties for using invalid AI-generated references. • The topic of AI's role in research is important for further investigation and is also an ethical concern. Conclusion 37
- 38. At last • Our research showed that AI can contribute to public health research as a team member. • According to authorship guidelines, the AI was ultimately not listed as a co-author, as it would be done with a human researcher. 38
- 39. Thank-You… 39
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