Chatbots in Healthcare [Part 2]. In April 2017 I wrote this story on the… | by Tatyana Kanzaveli | Jan, 2023

In April 2017 I wrote this story on the potential use of chatbots in healthcare: https://medium.com/p/984fc23e0410 . It got over 3.5K views! 6 years later the topic of chatbots in healthcare is still HOT!

You all probably are aware of the recent public release of ChatGBT. In this article I want to go over its use in healthcare.

ChatGPT is a large language model developed by OpenAI. It is based on the GPT (Generative Pre-training Transformer) architecture and is trained on a massive dataset of internet text to generate human-like text. ChatGPT is pre-trained on a massive corpus of text data and can be fine-tuned on specific tasks such as text generation, language translation, text classification, text summarization, and building dialogue systems. It can also be used for other natural language processing (NLP) tasks, such as named entity recognition, part-of-speech tagging, and sentiment analysis. ChatGPT is considered state-of-the-art in terms of its ability to generate human-like text, and it’s being widely used in the industry.

1. Text generation and completion: ChatGPT can be used to generate text in a variety of formats, such as articles, stories, and social media posts. It can also be used to complete partially written text, such as a sentence or a paragraph.
2. Dialogue systems: ChatGPT can be used to build chatbots and virtual assistants that can understand and respond to natural language inputs from users.
3. Language translation: ChatGPT can be used to translate text from one language to another, as well as to summarize text in multiple languages.
4. Text classification: ChatGPT can be used to classify and categorize text, such as identifying the sentiment of a social media post or the topic of an article.
5. Text summarization: ChatGPT can be used to extract the key information from a longer piece of text and present it in a condensed format.
6. Language model fine-tuning: ChatGPT can be fine-tuned on specific task or domain to improve performance on that task.

There are several potential use cases for ChatGPT in healthcare, including:

1. Medical chatbots: ChatGPT can be used to build chatbots that can understand and respond to natural language inputs from patients and caregivers, providing symptom checking, triage, scheduling and other basic healthcare information.
2. Medical language understanding: ChatGPT can be fine-tuned on medical texts to improve its understanding of medical language and concepts, which can be used to extract structured information from unstructured texts, such as electronic health records (EHRs) and clinical notes.
3. Medical summarization: ChatGPT can be used to summarize medical reports and other texts, such as clinical trials, to make them more accessible to healthcare professionals and patients.
4. Medical language translation: ChatGPT can be used to translate medical texts from one language to another, making it easier for healthcare professionals and patients to communicate and understand important information.
5. Medical report generation: ChatGPT can be used to generate medical reports automatically, such as radiology reports, pathology reports and discharge summaries, based on input data.
6. Medical research: ChatGPT can be used to analyze large volumes of medical research papers, identify key concepts and trends, and assist with the discovery of new treatment options.
7. Medical Adverse event reporting: ChatGPT can be fine-tuned on adverse event reporting data to identify patterns and trends in adverse event reporting, which can be used to improve patient safety.

There are a few examples of chatbots that have been created in the healthcare industry using the ChatGPT model:

1. SymptomChecker: A chatbot that helps users identify potential medical conditions based on their symptoms. It uses natural language processing (NLP) to understand user input and provides information on possible causes and treatment options.
2. MedWhat: a chatbot that uses NLP to understand medical questions and provides accurate and reliable answers.
3. MedChat: A chatbot that helps patients to schedule their appointments and manage their medical records.
4. MyCancerCompanion: A chatbot that provides personalized support and information to cancer patients throughout their treatment journey.
5. HealthTap: A chatbot that connects patients with real doctors and health experts via a text-based interface.

1. Pubmed-Chatbot: A chatbot that uses ChatGPT to assist researchers in finding relevant scientific articles on PubMed, a database of biomedical literature. It allows researchers to ask natural language questions, and the chatbot will retrieve the most relevant articles.
2. Clinical Trial Chatbot: A chatbot that utilizes ChatGPT to assist patients in finding clinical trials that match their medical condition and location. The chatbot can answer questions about the trial’s eligibility criteria, location, and contact information.
3. Research Assistant Chatbot: A chatbot that uses ChatGPT to assist researchers in finding and retrieving data from various sources such as scientific databases and journals.
4. Medical Literature Chatbot: A chatbot that uses ChatGPT to assist researchers and students in finding relevant and reliable medical literature on a specific topic. The chatbot can answer questions about the literature and provide summaries of the articles.

1. MedSum: A chatbot that uses ChatGPT to summarize medical articles and research papers for healthcare professionals, students, and researchers. It can provide a concise and accurate summary of the main findings and conclusions of the article.
2. Clinical Summary Chatbot: A chatbot that utilizes ChatGPT to generate summaries of patient medical records for healthcare professionals, including information on diagnosis, treatment, and progress.
3. Medical Summary Generator: A chatbot that uses ChatGPT to summarize medical news articles for healthcare professionals, providing a quick and easy way to stay informed about the latest developments in the field.
4. Journal Summary Chatbot: A chatbot that uses ChatGPT to provide a summary of a scientific journal article for researchers and students.

1. Epic-Chatbot: A chatbot that uses ChatGPT to extract and analyze patient data from the Epic electronic health record (EHR) system, and generate detailed medical reports for healthcare professionals. It can be used to generate reports on patient demographics, diagnosis, treatment, and progress.
2. Epic-Report-Generator: A chatbot that uses ChatGPT to extract patient data from Epic EHR system and generate summary reports on patient encounters, lab results, and medication orders for healthcare professionals.
3. Epic-Clinical-Documentation-Chatbot: A chatbot that utilizes ChatGPT to extract and summarize patient data from Epic EHR system and generate clinical documentation, such as discharge summaries, progress notes, and consultation reports.
4. Epic-Patient-Summary-Chatbot: A chatbot that uses ChatGPT to extract patient data from Epic EHR system and generate a summary of the patient’s medical history, current condition, and treatment plan for healthcare professionals.

1. MedTranslate: A chatbot that uses ChatGPT for medical language translation, which can translate medical terms and phrases from one language to another for healthcare professionals, patients, and researchers.
2. Medical Language Translation Chatbot: A chatbot that utilizes ChatGPT to provide accurate translations of medical documents, including patient records, consent forms, and discharge summaries, for healthcare professionals working with a multilingual patient population.
3. Clinical Language Translation Chatbot: A chatbot that uses ChatGPT to translate clinical terms and phrases, including diagnosis, treatment, and medication names, for healthcare professionals, patients, and researchers.
4. Medical Interpreter Chatbot: A chatbot that uses ChatGPT to provide real-time translation during medical consultations, enabling healthcare professionals to communicate effectively with patients who speak different languages.

1. Pubmed-Chatbot: A chatbot that uses ChatGPT to assist researchers in finding relevant scientific articles on PubMed, a database of biomedical literature. It allows researchers to ask natural language questions, and the chatbot will retrieve the most relevant articles.
2. Clinical Trial Chatbot: A chatbot that utilizes ChatGPT to assist patients in finding clinical trials that match their medical condition and location. The chatbot can answer questions about the trial’s eligibility criteria, location, and contact information.
3. Research Assistant Chatbot: A chatbot that uses ChatGPT to assist researchers in finding and retrieving data from various sources such as scientific databases and journals.
4. Medical Literature Chatbot: A chatbot that uses ChatGPT to assist researchers and students in finding relevant and reliable medical literature on a specific topic. The chatbot can answer questions about the literature and provide summaries of the articles.
5. Knowledge-base-Chatbot: A chatbot that uses ChatGPT to assist researchers in searching and retrieving information from a knowledge base of medical research papers, articles, and journals.

1. Adverse Event Chatbot: A chatbot that uses ChatGPT to assist healthcare professionals and patients in reporting adverse events related to medical treatments and products. The chatbot can guide users through the reporting process, answer questions about the event and the reporting requirements, and assist with the completion of necessary forms and documentation.
2. MedSafety Chatbot: A chatbot that utilizes ChatGPT to assist healthcare professionals in reporting adverse events related to medication use, including side effects, interactions, and allergic reactions. The chatbot can provide guidance on the appropriate reporting channels and assist with the completion of necessary forms and documentation.
3. Adverse Event Reporting Assistant: A chatbot that uses ChatGPT to assist patients and healthcare professionals in reporting adverse events related to medical devices, such as implantable devices, diagnostic equipment, and surgical instruments.
4. Clinical Safety Chatbot: A chatbot that uses ChatGPT to assist healthcare professionals in reporting adverse events related to clinical trials, such as adverse reactions, serious adverse events, and unanticipated problems.

1. Define the scope and purpose of the chatbot: Determine the specific medical use case(s) the chatbot will address, such as symptom checking or appointment scheduling.
2. Gather and structure data: Collect a large dataset of medical information and questions that the chatbot will use to respond to user input. Organize the data into categories and create a database or spreadsheet to store it.
3. Train the model: Use the dataset to train ChatGPT or another language model to understand and respond to user input in a natural and accurate way.
4. Test the chatbot: Use a test set of data to evaluate the chatbot’s performance and make any necessary adjustments to the model.
5. Integrate the chatbot into a user interface: Create a user-friendly interface for the chatbot, such as a website or mobile app, that allows users to interact with the chatbot easily.
6. Monitor and update the chatbot: Regularly monitor the chatbot’s performance and update the model and data as needed to improve its accuracy and usefulness.

Creating a medical chatbot using ChatGPT requires a combination of skills, including:

1. Natural Language Processing (NLP): Knowledge of NLP techniques and methods is essential for training the ChatGPT model to understand and respond to user input in a natural and accurate way.
2. Machine Learning: Understanding of machine learning algorithms and techniques, such as supervised and unsupervised learning, is necessary for training and fine-tuning the ChatGPT model.
3. Medical knowledge: A good understanding of medical terminology, anatomy, and disease processes is required to create a chatbot that can accurately respond to medical questions and provide reliable information.
4. Data analysis: Knowledge of data analysis techniques, such as data cleaning, data visualization, and statistical analysis, is necessary for creating a large dataset of medical information and questions that the chatbot will use to respond to user input.
5. Programming: Knowledge of programming languages such as Python is necessary for implementing the chatbot, integrating it with other systems, and fine-tuning its performance.
6. User interface design: Knowledge of user interface design principles is necessary for creating a user-friendly interface for the chatbot, such as a website or mobile app, that allows users to interact with the chatbot easily.
7. Compliance with regulations: Familiarity with legal and ethical guidelines and regulations, such as HIPAA and GDPR, is important for ensuring the chatbot is compliant with regulations and guidelines.

Creating a medical chatbot using ChatGPT requires a team of experts with a variety of skill sets, including:

1. Medical experts: Medical professionals, such as doctors, nurses, or pharmacists, with knowledge of medical terminology, anatomy, and disease processes, are necessary to ensure that the chatbot provides accurate and reliable medical information.
2. Natural Language Processing (NLP) experts: NLP experts with knowledge of NLP techniques and methods are necessary for training the ChatGPT model to understand and respond to user input in a natural and accurate way.
3. Machine Learning experts: Machine learning experts with knowledge of machine learning algorithms and techniques are necessary for training and fine-tuning the ChatGPT model.
4. Data scientists/analysts: Data scientists or analysts with knowledge of data analysis techniques, such as data cleaning, data visualization, and statistical analysis, are necessary for creating a large dataset of medical information and questions that the chatbot will use to respond to user input.
5. Software engineers: Software engineers with knowledge of programming languages, such as Python, are necessary for implementing the chatbot, integrating it with other systems, and fine-tuning its performance.
6. User interface designers: User interface designers with knowledge of user interface design principles are necessary for creating a user-friendly interface for the chatbot, such as a website or mobile app, that allows users to interact with the chatbot easily.
7. Legal and compliance experts: Legal and compliance experts with knowledge of regulations and guidelines, such as HIPAA and GDPR, are necessary for ensuring the chatbot is compliant with regulations and guidelines.

The timeline for designing and developing a medical chatbot using ChatGPT can vary depending on the scope and complexity of the project, as well as the size of the team working on it. However, a typical timeline might include the following steps:

1. Defining the scope and purpose of the chatbot: This step usually takes 1–2 weeks, and involves determining the specific medical use case(s) the chatbot will address, such as symptom checking or appointment scheduling.
2. Gather and structure data: This step can take several weeks to a few months, and involves collecting a large dataset of medical information and questions that the chatbot will use to respond to user input. Organizing the data into categories and creating a database or spreadsheet to store it.
3. Training the model: This step can take several weeks to a few months, and involves using the dataset to train ChatGPT or another language model to understand and respond to user input in a natural and accurate way.
4. Testing the chatbot: This step can take several weeks, and involves using a test set of data to evaluate the chatbot’s performance and make any necessary adjustments to the model.
5. Integrating the chatbot into a user interface: This step can take several weeks to a few months, and involves creating a user-friendly interface for the chatbot, such as a website or mobile app, that allows users to interact with the chatbot easily.
6. Monitoring and updating the chatbot: This step is ongoing, and involves regularly monitoring the chatbot’s performance and updating the model and data as needed to improve its accuracy and usefulness.

Creating and deploying a medical chatbot using ChatGPT can be a complex and challenging process, but it can also provide valuable insights and lessons learned. Some of the key lessons that can be learned from this process include:

1. Data quality is critical: Having high-quality, accurate, and relevant data is essential for training a medical chatbot using ChatGPT. Data must be cleaned and organized in a structured manner to ensure that the chatbot can provide reliable and accurate information.
2. Fine-tuning the model is necessary: Fine-tuning the ChatGPT model is necessary to improve the chatbot’s performance and ensure that it can understand and respond to user input in a natural and accurate way.
3. Compliance with regulations is crucial: Creating a medical chatbot using ChatGPT requires compliance with relevant regulations and guidelines, such as HIPAA and GDPR. It is important to consult with legal and ethical experts to ensure the chatbot is compliant with regulations and guidelines.
4. User-centered design is essential: Creating a user-friendly interface is essential for a successful medical chatbot using ChatGPT. The chatbot’s design should be user-centered, taking into account the needs and preferences of the target users.
5. Ongoing monitoring and maintenance is necessary: Deploying a medical chatbot using ChatGPT requires ongoing monitoring and maintenance to ensure its accuracy and usefulness. The chatbot’s performance should be regularly evaluated and updated as necessary to improve its accuracy and usefulness.
6. Working with a multidisciplinary team is important: Developing a medical chatbot using ChatGPT requires a multidisciplinary team with different skill sets, including medical, NLP, machine learning, data, software engineering, user interface design, and legal/compliance experts.
7. The quality of the translation might not be as good as human translator: If the chatbot is used for medical language translation, it’s important to note that the quality of translations might not be as good as a human translator, and it’s important to have a human translator available when necessary.