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