Should I Use ChatGPT or Google Gemini For Medical SEO Content?

Short answer is “With Caution” But…. a human person must verify the results of the contents, reword them for accuracy and then use the SEO content with caution. Majority of the content produced by ChatGPT or Gemini will be considered “plagiarized”, hence it should not be used to improve the SEO of your website. Actually, it can reduce the quality score of your website. If you have plagiarized content on your website, your quality score is reduced, hence your SEO rankings are reduced also.

Key reasons to avoid using ChatGPT or Google Gemini for medical SEO

1. You should not primarily rely on ChatGPT to generate medical SEO content, as it is highly likely to produce inaccurate or outdated information due to its limitations in understanding complex medical topics and its knowledge cut-off date, which is usually around 1- to 2 years ago; always thoroughly fact-check and verify any AI-generated medical content with reliable sources before publishing it.
2. Accuracy concerns: ChatGPT may generate factually incorrect medical information as it lacks the depth of knowledge needed for complex healthcare topics.
3. Lack of nuance: ChatGPT may not capture the necessary nuances and complexities of medical conditions, potentially causing misinterpretations.
4. Ethical considerations: Providing inaccurate medical information can have serious consequences for patients, making it crucial to prioritize reliable sources.

How to use AI responsibly for medical content:

1.Employ ChatGPT to generate outlines or initial drafts, but always thoroughly review and fact-check the content with medical professionals.
2. Consider using AI for broader health topics where accuracy is less critical, but avoid sensitive medical diagnoses or treatment advice.
3. Prioritize human expertise. Ensure that all medical content is reviewed and approved by qualified healthcare practitioners.2.

This is how the AI ChatGPT & Google Gemini works:

Bing ChatGPT is based on search engines data. This data already exists. For example Google search and Bing search has catalogued billions of pages. The Bing ChatGPT works as follows. This is high level summary for doctors and medical & dental practice managers, not a detailed algorithmic based definition.

1. Bing ChatGPT reads data from millions of websites, these web pages already exist
2. Then it assigns a “quality score” to each website based on the accuracy and usefulness of the website
3. Using this data, it answers questions typed or spoken by a person using LLM or SLM models
4. So in a very simplistic way, it is a more user-friendly search engine.
5. The answers it provides are based on the quality of information on websites it reads.
6. So if the quality of website (like this website that you are reading – www.patientgain.com) determines that quality of the answer, and ability of the algorithms of Bing ChatGPT to piece together proper grammar based answers.
7. According to Bing ChatGPT results based on the web content summarized, so please use your best judgment.

Majority of the content produced by Bing ChatGPT will be considered “plagiarized”, hence it should not be used to improve the SEO of your website.

LLM vs SLM models

A “Large Language Model” (LLM) like OpenAI’s GPT-3 or Google’s Gemini is a powerful, general-purpose model with a vast number of parameters, while a “Small Language Model” (SLM) like DeepSeek is a smaller, more specialized model with fewer parameters, better suited for specific tasks and requiring less computational power; essentially, LLMs are great for complex, open-ended tasks while SLMs excel in niche, domain-specific applications.

Example LLMs (Large Language Models):
OpenAI GPT-3, GPT-4: Known for generating creative text formats, translation, and answering questions with high fluency.
Google PaLM, Gemini: Capable of complex reasoning, code generation, and multi-modal tasks
Meta Llama: Strong in dialogue and summarization capabilities

Example SLMs (Small Language Models):
DeepSeek is considered a Small Language Model (SLM), not a Large Language Model (LLM). This means it requires less computing power and data compared to larger models like ChatGPT, making it more efficient for certain applications.

Key Differences:
Parameter size:
LLMs have significantly more parameters than SLMs, allowing for greater versatility but requiring more computational power.
Generalization:
LLMs can generalize well across diverse tasks and domains, while SLMs are often better at handling specific, niche tasks.
Resource usage:
SLMs are more lightweight and can run on less powerful hardware compared to LLMs.

LQM vs LLM models

While “LLM” stands for “Large Language Model” and focuses on processing and generating human language, “LQM” stands for “Large Quantitative Model,” which is specifically designed to handle complex numerical data, statistical analysis, and quantitative calculations, making it more suited for tasks involving financial modeling, scientific simulations, and optimization problems, unlike the text-based capabilities of an LLM.

If you need help with Medical SEO services, please contact PatientGain.