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A Guide to Choosing the Best Generative AI Tools for Life Sciences

A Guide to Choosing the Best Generative AI Tools for Life Sciences

A Checklist of Features of the Best Generative AI Tools

When evaluating generative AI solutions, life sciences organizations should use these criteria to find a tool that has the most significant impact on business outcomes.

  • Natural Language Processing
    Generative AI for life sciences must use sophisticated linguistic techniques to enable all business users to interact easily with the platform. Various forms of NLP, such as natural language query (NLQ) that eliminates the need to learn keywords or phrases, and natural language generation (NLG) that allows the platform to respond contextually, enhance usability for any life sciences team member.
  • Zero Code Environment
    Analytics that requires heavy intervention from the IT team will limit productivity. Organizations should look for a tool that allows business users to configure their own dashboards without coding. This feature saves time and makes it possible for users to build data-driven decision-making into their routine workflows without the delay of waiting for assistance from IT.
  • AI Visualizations
    The best generative AI tools for life sciences will automatically choose the optimal visualization for presenting the information the user requests based on the data.
  • Domain Expertise
    Generative AI performs best when it’s trained for a specific domain. AI tools for life sciences should be trained with life sciences data, allowing them to provide accurate, contextual outputs from day one. Domain-specific training also enables faster implementation and quick time to value
  • Embedded AI
    Life sciences organizations will benefit most from AI that can be embedded into the business applications that team members commonly use. This feature allows users to factor data analytics insights into their workflows, whether preparing for a call with a customer, collaborating with colleagues, or troubleshooting a problem. The generative AI tool should also be accessible from any device, including PCs, laptops, tablets, and smartphones.
  • Content Personalization
    The generative AI tool should meet each user’s specific needs, providing insights based on role, market, geography, brand focus, or other factors related to their jobs. The platform should also learn the user’s behaviors and preferences over time, enabling it to provide the precise insights the user needs.
  • Predictive AI and Root Cause Analysis
    The generative AI tool should do more than operate as an advanced search engine. It will provide life sciences organizations with maximum value if it can deliver cognitive insights that help forecast brand performance and make sales projections. The tool should also be able to identify patterns and anomalies and provide alerts so users can take immediate action. Furthermore, when a change occurs, the tool should also help users discover why and reveal the best next steps.
  • Enterprise-Readiness
    The tool should be purpose-built for reliability, uptime, and scalability. It should also meet the needs of enterprises with adapters for integrating with data sources and include enterprise-grade security features. Global organizations will also benefit from a tool that supports multiple languages, enabling teams in different regions to interact in their preferred languages.

WhizAI Offers the Best Generative AI Tools for Life Sciences

WhizAI is a leading generative AI tool specifically trained for the life sciences domain. WhizAI’s platform includes a powerful hybrid natural language processing engine that understands users’ intent and will even ask for additional information to produce the desired information. It also automatically chooses the best visualization so users can quickly understand status, trends, and relationships. It also surfaces anomalies and helps users determine the why behind outliers and changing trends.WhizAI’s zero-code environment allows users to configure their dashboards so they have the information at their fingertips to optimize their job performance. Users can also access WhizAI from within their commonly used applications, such as Veeva, Microsoft Teams, and Salesforce, and from any device.WhizAI’s enterprise-ready tool also deploys quickly, typically in a few weeks, compared to the months it takes to implement a solution that was not pre-trained for the life sciences domain.

Frequently Asked Questions

Does WhizAI have a limit to the amount of data it can analyze?

Internal testing shows that WhizAI can analyze several petabytes of data and still generate a response in less than a second. WhizAI is designed to scale with increasing data volumes.

Can different life sciences teams use WhizAI?

WhizAI can provide insights to field sales, market access, patient services, R&D, and brand teams. It’s also a proven solution for different life sciences businesses, from pharmaceutical companies and healthcare to medical device manufacturing.

If a life sciences company needs analytics for its commercial team, R&D, and leadership, do they need separate tools?

WhizAI enables a life sciences company to establish a data repository and use it for all analyses. The insights that different teams use for decision-making align, improving collaboration. However, users can configure their dashboards to easily access the information that’s most relevant to their jobs.

How long does it take WhizAI to update when new data is available?

WhizAI automatically updates along with data sources, so analyses are always based on the most current information.

Do users ever see disruptions to WhizAI availability or performance?

WhizAI leverages microservice architecture managed via Kubernetes, resulting in high reliability and no impact on performance during updates or when new features are added.

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