AI Unlocked

Artificial intelligence (AI) is poised to fundamentally transform health care. It’s not just about adding a new tool, it’s about rethinking how we deliver medicine. Data is a valuable asset, but it’s only powerful when we can position it properly to enable innovation. AI helps us deliver on that innovation. It holds the promise to enable predictive, real-time and consistent care, and opens the door to innovations we couldn’t even imagine before. Think about early cancer detection, individualized treatments, and accelerated drug discovery. AI gives us the potential to change everything, but we must do it responsibly and ethically.

It's often said that data is the new oil. Just like oil, it's incredibly valuable, but not in its raw form. To extract the value from data, we must invest in making it ready for innovation, just as the oil industry has invested in extracting value from crude oil mined from the ground.

Priority 1 is data ingestion: figuring out where the high-value data assets live and pulling them from their source systems, like medical records or imaging databases. Data sitting in a medical record helps us deliver clinical care, but it doesn't help us innovate. We have to extract it first.

Priority 2 is annotation and curation: structuring, cleaning, and organizing the data so it can be used. Most of it isn't ready for computing and research in its original form. This process is like refining oil, turning raw material into fuel and other byproducts for innovation.

Priority 3 is building advanced computing and analytic platforms. The oil industry has developed expansive distribution channels and pipelines for refined oil and byproducts to reach the industries that utilize them. For data, we would aim to do the opposite. Rather than copying and distributing massive data sets to researchers, which is risky and inefficient, we will develop computing platforms that can bring researchers and their tools to the data, where it securely resides. This is critical for both privacy and performance. In a way, this is like having the distribution channels and pipelines for refined oil and byproducts to reach the industries they can serve.

Priority 4 is responsible stewardship: We can't allow unbridled access to sensitive data. There is a lot of power here, and we must build in governance and ethical and equitable frameworks to ensure that our innovation doesn't cause harm. At Johns Hopkins, we established an AI and Data Trust Council to help oversee this.

We take security very seriously. Security is most vulnerable when data is decentralized, sitting on individual computers. A bad actor or hacker only needs to breach one machine. That's why we're shifting to centralized, monitored systems where access is logged, and unusual activity can be flagged immediately. Our IT@JH team and the office of the chief information security officer help us develop secure computing environments and establish ways to identify and prevent potential breaches and generate an immediate response if a breach occurs. Our goal is to innovate with the data while ensuring that we maintain patient privacy and data security.

Much of the data we use was collected for clinical care. Now, we’re repurposing it for secondary research. We have legal frameworks for that, but more importantly, we have an ethical obligation to show that what we are doing is truly for society’s benefit. The public must be a partner in this. That means being transparent, thoughtful and inclusive. We make progress by building trust. 

This is where it gets really exciting. With interdisciplinary collaboration spanning medicine, data science and engineering, we're not just making tweaks, we are transforming health care. Here's how:
​
We're shifting from reactive to preventive, predictive care. AI tools can help detect disease earlier, allowing us to act before symptoms become an illness. We can also develop models to forecast an individual's disease course or response to a given therapy, and develop a treatment plan accordingly.
​
We're turning ad hoc care into real-time care. AI agents can use monitoring systems and wearable and smart device data 24/7, flag issues, and alert care teams in real time.

We're reducing unwanted variation. Whether it's differences in access, knowledge gaps, or errors, AI helps ensure that best practices are consistently applied.

We're accelerating innovation. AI can detect patterns in imaging that the human eye cannot. It can screen billions of virtual drug compounds, design new ones from scratch, and drastically reduce development timelines and this is only the beginning.

What we’re building is designed for both scale and personalization. The data sets we use are broad and deep, but the discoveries we make feed directly back into individualized care. That’s the essence of an intelligent, innovative health system — data from many helps the one. It’s a full-circle model in which innovation informs better care.

That's a real concern, especially with early detection. Just because we can find something early doesn't always mean we should treat it. AI can help us pair early detection with risk stratification, figuring out whether what we found is likely to become dangerous. For example, in prostate cancer, we're developing biomarkers to distinguish aggressive from indolent cases, so patients get the right level of care. We will look to develop similar strategies for every cancer type.

We're developing tools like Patient Insight & a dashboard that helps doctors see a patient's journey in context of thousands of others. Another project, the Health General Reasoner, is a backend tool to embed AI into clinical workflows. We are piloting a collaboration with Microsoft of a tool developed in-house at Johns Hopkins that helps identify patients at risk for blood clots & a common risk of hospitalization and suggests preventive treatment in real time.

No single institution has all the data or expertise. That's why we're part of a national initiative called the Cancer AI Alliance (CAIA) with four other top cancer centers. We're working to build a federated learning platform where each center keeps its data secure, but shares its models. AI tools travel to the data, not the other way around. This preserves security and privacy while enabling broad collaboration. If we succeed, it could serve as a model for every cancer hospital in the country to join CAIA in this collaborative effort.

Yes, in many ways we are, but we're not doing this alone. We're working across institutions, with industry, and with our communities. The technology is powerful, but it's the people and partnerships that will make it truly impactful.
The future is incredibly promising. We're exploring how AI can accelerate drug development, personalize treatments, and help us understand cancer in ways we couldn't before. But we're also focused on making sure the technology is inclusive, equitable, and trusted. If we do it right, AI will raise the standard for everyone. Thats our mission.