A Better Alternative than ChatGPT for Healthcare

ChatGPT has highlighted how artificial intelligence (AI) can make tasks – and ultimately, people’s lives – easier. Based on a large language model (LLM), ChatGPT uses deep learning to process massive volumes of unstructured text. It can understand language and formulate relevant outputs from poetry, music, software code, or social media posts. But does using ChatGPT for healthcare analytics provide value? 

What ChatGPT and Other LLMs Do (and Don’t Do) Well

LLMs like ChatGPT can predict the next word or sentence contextually to create answers to queries, lists, creative works, and more. The experience is like speaking to another human with a vast store of knowledge and can organize thoughts logically in an instant.

ChatGPT stands out among other models, however, because it’s been trained on vast amounts of text, from books and literature to academic journals and blogs. Because of its extensive training, it can create impressive responses in just a few seconds. Additionally, ChatGPT uses reinforcement learning (RL), seeking “rewards” for successful interactions with users. When users respond positively, it stays the course, and when it receives negative feedback, it makes adjustments so future responses will be on point.

However, there is a downside, which may make using ChatGPT for healthcare risky. ChatGPT FAQs point out that the model occasionally produces “harmful instructions” or biased content, so checking responses are recommended. Additionally, developers didn’t build the model specifically for healthcare, so it would take organizations significant time to implement it.

A Better Alternative than ChatGPT for Healthcare

AI delivers the best results when specifically trained for a specific domain. And while an LLM makes it easy for users of all IT and data science skill levels to interact with a platform, healthcare users often need more than a text response. A platform that acts more like an analyst than a conversationalist can provide more value, delivering data visualizations that show relationships rather than just summaries in paragraph form.

Organizations also benefit from predictive capabilities, which they won’t have when using ChatGPT. Knowing the next word in a sentence isn’t enough. Healthcare analytics will help practitioners perform their jobs more effectively and positively impact patient outcomes when the platform can guide users to the actions they need to take next.

With the right LLM for healthcare and life sciences, organizations can staff more efficiently and help reduce employee burnout, streamline commercial operations, and keep policies updated without a long training and implementation schedule for the platform.

Why WhizAI is a Better Choice than ChatGPT for Healthcare and Life Sciences

  • Purpose-Built for Life Sciences:
    WhizAI is trained with life science data and uses a large language model as well as deep learning and other forms of AI. From the ground up, WhizAI was built specifically for life sciences and healthcare applications.
  • Support for Data Governance:
    WhizAI gives administrators granular control over data, providing the ability to deny access to data in a specific column or field or deidentify data. This capability allows teams to make the best use of data without violating data protection and privacy regulations and policies.
  • Scalable:
    Scalability is essential in healthcare and life sciences. Data volumes are increasing, and data sources are constantly updating. WhizAI scales with growing data volumes in life sciences and healthcare and doesn’t limit analysts to the number of data sources they can use with the platform.
  • Enterprise-Ready:
    WhizAI integrates with the business applications that life science organizations use, supports multiple languages, and includes enterprise-grade security and access control to keep data secure.
  • Anytime, Anywhere Access:
    WhizAI can be embedded in applications, such as Veeva, Salesforce, or Microsoft Teams, to give business users easy access to data insights so they can build them into their day-to-day workflows. Users can also use their smartphones, tablets, and laptops to access data insights when working away from the office or to prepare for a meeting with a physician or health system.

Frequently Asked Questions

Is WhizAI a proven solution for life sciences?
WhizAI’s ease of use, accuracy, and reliable performance that builds trust increase user adoption from an average of 33% to as much as 100%. It can also reduce reliance on the IT or data team by as much as 40% and lower data analytics total cost of ownership (TCO) by 50%.
How can you deploy WhizAI?
WhizAI offers you the flexibility to deploy it on-premises or with a firewall in a private or public cloud, such as AWS, Azure, or other popular cloud services.
Does WhizAI provide alerts?
WhizAI includes automated anomaly detection. Users receive proactive alerts to deviations in data that require prompt corrective action. WhizAI uses historical data to generate predictions for metrics that you choose
Can you use WhizAI with dashboard solutions?
Yes. WhizAI can work with dashboard solutions, minimizing the number of dashboards that the IT or data team must maintain and giving business users self-service analytics options
How much data can we use in an analysis with WhizAI?
WhizAI is capable of analyzing billions of data points in a subsecond and scales without negatively impacting performance.


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