top of page
  • Writer's pictureMya Digital Blogger


Updated: Jun 21

Since its inception in November 2022, ChatGPT has remained a trending topic globally, with numerous perspectives analyzing its capabilities. The cutting-edge AI technology could tackle various use cases, such as translation, functioning as a virtual assistant, writing content, and debugging code.

As its name suggests, the tool is purpose-built for conversational chat-based applications. It automatically generates text based on written prompts in a way that is far more sophisticated and coherent than its predecessors. The technology has been developed by OpenAI, A research company backed by Microsoft. Unlike most AI chatbots, ChatGPT can respond to follow-up queries, acknowledge mistakes, refute false assumptions, and reject improper requests.

In this article, we explore what ChatGPT has to offer in producing quality, trustworthy healthcare content. But before we delve deep into its pros and cons, let us look at how ChatGPT works.

ChatGPT - How does it work?

To understand how ChatGPT works, we must know what language models are. Language models are integral to tech-based language solutions such as conversational AI (E.g., Alexa, Siri, Cortana) and chatbots (E.g., Jasper, Bard). In short, they are statistical tools trained to predict the probability of the next word or a sequence of words in a text.

Large language models (LLM), on the other hand, are more complex deep learning algorithms that can recognize, condense, translate, forecast, and produce text and other content using information gleaned from enormous datasets. The brain of ChatGPT originates from the GPT-3.5 series, an LLM comprising a much more expansive dataset. LLMs can be fine-tuned for various purposes, ChatGPT being a derivative tool optimized for human-like dialogue. Based on user input, it can generate text in a variety of styles and for a multitude of purposes, much like many generative AI platforms that use language models. Additionally, ChatGPT incorporates machine learning, advanced dialog management, and Natural Language Processing (NLP) techniques such as sentiment analysis, keyword extraction, topic modeling, and named entity recognition to create more accurate, detailed, and coherent responses.

GPT stands for Generative Pre-training Transformer, which is a generative language model built on the "transformer" architecture. These models are effective in learning to execute tasks involving natural language processing while processing massive amounts of texts it has been trained on. A key differentiator of ChatGPT is that it learns from human feedback. ChatGPT learns how to process instructions and provide responses aligned with human intent utilizing Reinforcement Learning with Human Feedback (RLHF), a machine learning technique.

While both supervised learning and reinforcement learning techniques have been used to develop ChatGPT, the reinforcement learning element in particular sets ChatGPT apart. In order to reduce damaging, concocted, and/or biased outputs, the designers incorporated human feedback in the training phase.

Large Language Model - Capability Vs. Alignment

Understanding the concepts of capability and alignment in machine learning and how these aspects have been addressed in the development of ChatGPT can help to get a sense of its capabilities. The ability of a model to carry out a certain task or collection of tasks is referred to as the model's capability in the context of machine learning. On the other hand, alignment is concerned with what we want the model to perform as opposed to what it has been trained on. It describes how closely a model's objectives and actions conform to human values and expectations. In developing ChatGPT, human feedback through the RLHF technique aims to mitigate the alignment issue.

How can ChatGPT help develop healthcare content?

As a neural network trained on vast amounts of text data, ChatGPT can aid in developing healthcare content. Also, it continuously learns from human feedback, and hence the answers it provides are, by and large, well aligned with human intent. ChatGPT can augment healthcare content in the following ways:

  • Create short-format copy for various uses: ChatGPT can help create short-form content, such as meta tags, social media posts, health tips, product descriptions, summaries, and other less complex forms of information. This can help scale up output and potentially enhance content marketing efforts. It can also be utilized to create outlines or drafts of long-format content, which could then be reviewed and refined to ensure quality, accuracy, originality, and audience resonance. With longer format content, coherence can become an issue since the model builds sentences based on the relationships of previous words.

  • Helpful for SEO, topic, and keyword research: Dipping into its expansive content repository and through NLP, ChatGPT can provide suggestions for keywords and topics as a starting point for creating search-optimized content. These suggestions can then be verified and utilized for developing content that is helpful for users, an essential SEO criterion for Google’s algorithm.

  • Save time and human effort: Content writing can frequently end up being a time-consuming, monotonous task. Add to it the writer’s block that may further extend the time frame to come up with a blog or write-up. ChatGPT can automate the writing task to a great extent, analyzing, summarizing, and producing human-like texts, thereby speeding up content creation while freeing up time for other tasks.

Support medical research and academic-oriented papers: The massive volumes of available data, coupled with advanced AI techniques, enable swift summarization of vast amounts of information. Besides saving considerable time and effort involved in the research process, this can help generate new insights for further investigation.

The limitations of ChatGPT

ChatGPT has its limitations as well. Listed below are the disadvantages of using ChatGPT for drafting healthcare content:

  • Distinguishing pre-training on language rules and statistical probability Vs. Cognition and the ability to think: Understanding the distinction between the ability to comprehend human language and the capacity for cognition is vital in assessing ChatGPT’s capabilities and limitations. Through training on massive datasets and advanced AI techniques, language models have developed by leaps and bounds in their ability to predict and generate sentences based on linguistic rules, semantics, and syntax. However, the logical and reasoning capabilities of language models are still at a budding stage. This is because human cognition and thought involve many other sophisticated facets, such as situational awareness, social perception, and world knowledge, to name a few. ChatGPT and other generative AI models are known to generate answers that sound convincing but are factually incorrect or inane. This phenomenon, termed a "hallucination", is particularly risky when dealing with medical content. Therefore, in creating valuable, original, and thought-provoking content helpful for humans utilizing ChatGPT, it is advisable to keep these caveats in mind.

  • Chances of generating biased content: Human involvement in training the model using the RLHF technique can be viewed as a double-edged sword. The data used to fine-tune language models is influenced by a complex range of subjective elements in matching language models with human intents. The bias of the labelers, researchers, or developers involved in the training loop can affect the final output produced by ChatGPT. In addition, the reference data itself may include biased perspectives. Therefore, due human review becomes necessary to prevent the perpetuation of any biases.

  • Accuracy and Safety: The retrospective data (as of this writing, OpenAI states that it has limited knowledge of the world and events after 2021) that it references would not contain recent research, which would reflect in the results that ChatGPT shows. A medical professional, on the other hand, can ensure that the healthcare content is accurate and current. They have the training and experience to cross-check facts. This aspect is crucial since inaccurate information can be harmful or misleading and lead to incorrect self-diagnosis and treatment. ChatGPT cannot check for incorrect information.

  • Ethical considerations: Chat GPT, unlike medical professionals, does not have the responsibility to uphold professional ethics and ensure that healthcare content is based on sound medical principles. It can also not be held accountable in case of any inaccuracy or violations involving confidentiality or plagiarism. This attribution of accountability recently came into focus due to the use of AI-generated content in scientific research papers. Major academic publishers have updated their editorial policies to clarify their stance on using AI-generated content, banning or restricting their use due to concerns over flawed research.

  • Lack of creativity and the possibility of repetitive texts: Since it relies on a finite dataset, the content produced by ChatGPT can lack creativity and might also include repetitive texts. Furthermore, while it can extrapolate ideas from the available data, it would still be derivative information that may not always be accurate or original. In addition, the dataset it relies on can be dated and not contain recent research. Recency and relevance are important factors in SEO that can impact ranking in SERPs. Furthermore, search engines continuously update their algorithms to prioritize original and useful content written for people (and not search engines). Thus, repetitive and generic content can also have an adverse SEO impact.

  • Human reviews are necessary: Relying completely on the text generated by the software is not advisable, particularly for health content. Google’s YMYL (Your Money or Your Life) and EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) criteria exact very high page quality rating standards for health content to counter misinformation that could potentially negatively impact a person’s health. As a result, errors or inaccuracies in the generated responses that go undiscovered without adequate human oversight can prove costly. It is advisable to identify credible sources for any and all AI-generated texts since the model can provide plausible-sounding answers without listing references. Besides accuracy, it is also essential to be mindful of excerpts from confidential content that are part of its repository, which can have ethical and/or legal ramifications.

  • Can ChatGPT content be plagiarized?: Plagiarism can manifest since it compiles answers based on the source information available and due to its propensity to repeat itself. Additionally, copyrighted material from the dataset can be part of the generated text, leading to legal issues. This further underlines the importance of human review.

What does ChatGPT say about itself?

Out of curiosity, we asked ChatGPT what it has to say about its healthcare content creation chops. ChatGPT says it can provide accurate data, however, the same should always be reviewed by a human. Basically, ChatGPT concurs that due diligence and human touch are required for medical content.

Let us take a look at the below screenshot from the OpenAI website, where we asked ChatGPT if we can trust it to write healthcare content.


In Conclusion

ChatGPT is a powerful AI language tool that can certainly support healthcare content writing. The emerging technology is unique since it uses reinforcement learning from human feedback. Hence, it can ‘mimic human preferences’. However, medical professionals have the required knowledge and experience to validate the facts and ensure accuracy. This is immensely important since misinformation can have potentially dire health consequences. Overall, ChatGPT can aid your research, but it is advisable to rely on qualified medical experts for reliable, easy-to-understand, safe, and bias-free content aligned with the latest medical guidelines. In addition, blindly relying on AI-generated content is ill-advised since search engines have safeguards in place to prioritize high-quality content, as opposed to generic content that does not add value and is not helpful for people.


bottom of page