Unleashing the Power of AI for Better Science: The Case for an In-House Data Science Group
Artificial Intelligence, or AI, is everywhere. Given the many recent advancements in AI for healthcare, it’s important for academic medical centers to be at the forefront of these trends.

At NYU Langone Health, we acknowledge the need to invest in our own understanding of AI and how it will transform the way we practice medicine. That’s why we’re committed to cultivating data science expertise within our institution, creating a Predictive Analytics Unit (PAU) that’s dedicated to realizing the promise of AI in healthcare.

1. Communication: Even though AI is everywhere, it can feel like a black box. An in-house data science resource, such as our PAU, offers easily accessible advice and guidance, demystifying the technology. At NYU Langone Health, the PAU mediates conversations between users, leadership, and the companies we partner with who develop AI-enabled tools to ensure that everyone understands the benefits and goals of using these tools. We consult on projects and offer perspective on how we can use AI and predictive modeling to better achieve our goals. We bring not only our understanding of AI to the table, but also knowledge of intuitional needs and priorities, helping to make decisions about the best implementation of AI-enabled tools in the right context.

2. Education: Since we’re just starting to understand how AI will effect healthcare, part of my role is to go into our community of providers and both demonstrate the value that AI-enabled solutions can offer us and examine how it could be leveraged in the future. I educate our front-line providers on how to leverage the information produced by predictive models to augment and enhance their decision-making. Working in-house allows me to develop the personal relationships and institutional awareness necessary to ensure that what we teach sticks, is reinforced, and is continuously improved. The in-house team provides a consistent experience, a singular source of advice that isn’t contingent on a relationship with a vendor.

3. Replication:The AI scientific community as a whole has embraced sharing and the open-source movement. Information is easily available. Our PAU routinely evaluates models that are available to the public, assessing them for use at NYU Langone Health in a way that best suits our needs and goals. We can work with a flexibility and speed that isn’t replicable when working with an external team.

4. Validation: When in-house development and replication of models isn’t the appropriate approach for a specific need, we turn to external offerings. There is no shortage of start-ups claiming their AI capabilities are the solution to a health system’s problems. An in-house data science team serves as a vital resource for cutting through the sales pitch and really understanding the capabilities offered by external companies. By having a knowledgeable team that can test the validity of a vendor’s claim, we ensure that only those tools that will actually benefit the institution are adopted. Similar to an architectural review committee, we’ve enacted a formal process to closely examine externally developed models as part of our regular project management program.

5. Representation: Data scientists are a hospital’s best resource when advocating on the hospital’s behalf about AI and predictive modeling. Similar to a lawyer, serving as in-house counsel, in-house data science teams have the best interest of the intuition at heart and are uniquely suited to advocate on the institution’s behalf, ensuring that goals are met and services are rendered as committed.

So should a health system develop in-house data science expertise? The answer is an unequivocal yes. By offering communication, education, replication, validation, and representation capabilities, an in-house data science resource offers immense value to a health system, especially given we’ve just scratched the surface of what AI can do for healthcare.
['Nyu Langone Health Tech Hub']
['Healthcare', 'Data Science', 'Innovation', 'Artificial Intelligence']