How AI & Nanotechnology is Shaping the Future of Personalized Healthcare

Session Focus

Bahai framed the connection the panel was built on. AI’s impact spans digital health, advanced imaging, instrumentation and continuous monitoring, and all of it runs on semiconductors. Every AI algorithm and every sensing or imaging system sits on a piece of silicon, which makes nanotechnology foundational to the rest.

The panel spanned industry, universities and government, covering continuous monitoring, diagnostics, therapeutics and AI.

Three Pillars of Silicon Nanotechnology

Paru Deshpande PhD segmented the question rather than naming a single example, identifying three distinct contributions.

Compute capability. When people discuss AI they often mean simply the ability to compute faster over more data, and that roadmap continues.

Sensor technologies. His example began with familiar genomics and DNA sequencing, which he expects to grow inevitably into the other omics: proteomics, metabolomics and gut microbiome. He named the consequence directly, and it recurred through the session: there will be a gap between the existence of data and the existence of insight drawn from it.

Supporting infrastructure, which he noted people rarely think about. Getting sensors into a watch or handheld device requires solving battery life, wireless communication and compute at the edge. Those unglamorous problems are what bring healthcare solutions into new environments and create the opportunity to intervene earlier.

Why Continuous Monitoring Changes the Data

Paul Gerrish described the value in terms of placement and quality: putting a sensor where you want it, in the body or on it, and obtaining real-time continuous clinical-quality data.

His framing of microelectronics generally was smaller, smarter and simpler as an evergreen opportunity.

The contrast he drew was between a finger stick glucose measurement and a continuous monitor, where the insights available for behavior, wellness and disease management differ enormously.

His forward example was blood pressure, and he made the case from his own experience. A cuff reading at the doctor’s office is a point in time, and usually unrepresentative because the patient is stressed while it is taken. Measuring continuously across day and night, then combining that with other datasets, is where continuous monitoring leads.

Bahai added the clinical perspective he has gathered from working with clinicians and biologists: the information that matters is in the trends, not in a once-a-year blood test.

Finding What Is Worth Monitoring

Alex Shalek PhD reframed the relationship between nanotechnology and his own field. Semiconductors and nanotechnology are integral to every advance made recently, underlying both the measurements taken in hospitals and the analyses run on them.

For genomics and precision medicine specifically, those advances made it possible to look comprehensively at human health, measure genomic features at scale, and discover which things are worth monitoring as the basis for deciding when and how to intervene.

That sequencing matters. Continuous sensors are the next logical step, but what has been powerful is the comprehensive view, from pattern flow cells and imaging systems, that establishes what to monitor in the first place. Only then can that information be leveraged longitudinally toward better interventions for wellness and disease interception.

Proteomics on a Chip

Bahai raised protein sequencing on a chip as a case where work previously requiring a large laboratory machine moves onto a single chip, potentially in a pocket or on the body.

He directed the question to Abhishek Singharoy PhD, whose DARPA mandate he described as developing technologies offering surprise at scale and defense in depth using biology, and whose own research covers proteins and protein-based biotechnologies alongside cancer immunology and cancer evolution at Arizona State University and Mayo Clinic.

The framing of the problem: protein complexity consumes enormous cloud computing bandwidth for modeling. Bringing that work to the experimental level using semiconductor capabilities changes what computational analysis has to carry.

Key Takeaways

1. Every AI application in healthcare runs on semiconductors, which makes nanotechnology foundational rather than adjacent.

2. Silicon contributes through compute, sensing and the unglamorous infrastructure of battery life, wireless and edge computing.

3. Sequencing will extend from genomics into proteomics, metabolomics and microbiome, widening the gap between data and insight.

4. Continuous monitoring produces a different kind of information than point measurements, as glucose demonstrated and blood pressure may follow.

5. Comprehensive measurement has to precede continuous monitoring, since it establishes what is worth monitoring.

6. Laboratory-scale analysis is moving onto single chips, changing where computational load sits.