Every year, MIT Technology Review puts together a list of some of the brightest and best young minds working across science and technology. Our 35 Innovators Under 35 are the ones to watch—people whose research and technical work stands to shape the future of their fields.

This year, the list includes nine people who are transforming biotech. And this week, I’m going to give you a taste of some of the very cool stuff five of them are working on, which includes lifesaving innovations and groundbreaking “age reversal” tech.  

1. Preventing maternal deaths

Let’s start with Paschal Kija, a 28-year-old who has developed a device to treat postpartum hemorrhage—a dangerous birth complication that contributes to around 29% of maternal deaths in his home country, Tanzania. The Mkanda Salama (“Safe Wrap” in Swahili) is easy to use and costs just $70. A study found that it stopped postpartum bleeding in 73% of women within 20 minutes.

2. Making brain electrodes inspired by Japanese art

For decades, scientists have been developing, testing, and implanting brain electrodes. These devices are literally inserted into people’s brains, so while they can help us understand brain activity and treat various neurological disorders, it’s not totally surprising that they can also cause a bit of damage. Xiao Yang, 34, is working on ultra-small electrodes, which she hopes will have less of an impact on surrounding brain tissue. Her electrodes are flexible, too—in fact, they look a lot like actual neurons.

Yang is also creating sheets of electrodes to study brain cells in the lab. Inspired by kirigami—the traditional Japanese art of cutting paper to form three-dimensional shapes—she’s created a sheet of electrodes with a honeycombed structure shaped like a spiral basket. And she’s already using it to study brain cells.

3. Developing an all-new treatment for baby KJ

In 2024, Kyle “KJ” Muldoon Jr. was born with a rare and potentially fatal genetic disorder. Sarah Grandinette was a member of a team that developed an entirely new, personalized treatment for him—a gene-editing therapy essentially designed to correct a genetic misspelling.

Grandinette, who is now 26, created cells with KJ’s genetic variant and used them to screen gene-editing approaches; then she tested potential medicines in mice and monkeys. KJ ultimately got his first dose of the resulting treatment when he was about seven months old. He responded well and was eventually discharged from hospital. He’s “doing pretty great,” she says.

4. Reversing the aging process to treat eye disease

The buzziest tech in longevity right now centers on reprogramming—attempts to rewind the age of cells by resetting them to a more embryonic-like state. In a study published in 2020, Yuancheng (Ryan) Lu (now 34) and his colleagues showed that a reprogramming therapy reversed vision loss in aged, blind mice. Now an almost identical version of that therapy is being tested in people with eye disease. Life Biosciences, the company developing the drug, dosed its first volunteer in June.

5. Using AI to design new viruses

Last year, Samuel King used a generative AI model to come up with new genetic blueprints for bacteriophages—teeny viruses that can infect bacteria. Once he had those blueprints, he printed them out as strands of DNA. In experiments, he found that those AI-designed viruses could create new copies of themselves, burst out of bacterial cells, and infect other nearby bacteria. Viruses aren’t alive, but King, 27, hopes that AI-designed life forms might one day be used to make drugs or soak up pollution.

You can read more about these innovators, and the others on the biotech list, here.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

The entrance of major AI companies into healthcare is a meaningful and welcome development, accelerating the technical foundation available to the industry.

Their models are increasingly capable of processing long clinical records, interpreting complex terminology, comparing documentation against evidence and generating coherent summaries from large volumes of information. For clinicians, operators, and administrative teams who spend significant time searching through fragmented data, these advances are helping reduce cognitive burden and make high-value information easier to access.

But healthcare leaders should not confuse model capability with operational capability.

Healthcare’s administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information. The industry has spent decades investing in systems that capture activity: electronic health records, billing platforms, payer portals, scheduling systems, call center platforms, and analytics applications. Each system records something important. But few were designed to reason across the full chain of decisions that determines whether patients get timely access, clinicians have the right documentation and providers are reimbursed appropriately.

This is the problem that AI must now confront.

Revenue cycle is becoming one of healthcare AI’s proving grounds

The revenue cycle is the process healthcare providers use to get paid for care — from scheduling and registration through coding, billing, payer follow-up, and payment collection.

It is unusually suited to rigorous AI deployment because it combines high transaction volume, complex reasoning, structured and unstructured data, measurable outcomes, and significant operational variation. It also sits at the intersection of financial performance, patient access, and administrative workload.

A single claim can be influenced by patient insurance information, clinical documentation, coding rules, payer-specific policies, prior authorization requirements, medical necessity criteria, and many other data sources and operational processes. A breakdown in any one of those areas can create downstream consequences weeks or months later.

This is why generic automation has often fallen short.

Traditional robotic process automation works well when workflows are stable and rules are predictable, but healthcare administration is neither. Payer requirements change. Documentation expectations evolve. Exceptions are common and often material.

Large language models improve part of the equation, extracting meaning from narrative text, summarizing records and supporting reasoning over complex documentation. But when used alone, they inherit important limitations. They may produce plausible outputs without sufficient traceability. They may lack awareness of local workflow constraints. They may miss payer-specific history or context that determines whether an action is likely to change an outcome.

Why foundation models will become necessary but insufficient

The major AI firms are solving real technical problems for healthcare.

Better context windows make it easier to process longitudinal records. Stronger reasoning improves the interpretation of complex clinical scenarios. Better multimodal capabilities may eventually help connect text, imaging, structured data, and clinical signals in more useful ways. Safer model behavior and healthcare-specific tuning will continue to improve adoption.

These capabilities will make healthcare work faster, more consistent and easier to navigate. But they will not, on their own, solve deep-rooted administrative complexity.

Much of healthcare’s operational knowledge does not live in general medical literature, coding manuals, or public payer guidance. It lives in the accumulated experience of what actually happens after decisions are made. For example:

• Why does one appeal strategy outperform another?
• Which documentation gaps are most likely to cause reimbursement delay?
• How does a specific payer respond to a particular clinical argument?

These insights are behavioral, operational, and longitudinal. They emerge from years of transactions, outcomes, exceptions, and human judgment.

As foundation models become more capable, access to baseline healthcare knowledge will become less differentiating. Most leading systems will be able to interpret ICD-10 codes, recognize medical terminology, summarize payer policies, and reason over public clinical criteria. The durable advantage will come from how organizations combine that model intelligence with proprietary operational data, structured knowledge, workflow context, and governance.

The technical shift: From automation to orchestration

Agentic orchestration turns foundation model understanding into coordinated action — intelligence that can follow work across systems, apply the right rules, adapt when something changes, and keep learning from what happens next.

A prior authorization workflow, for example, may require retrieving clinical documentation through fast healthcare interoperability resources (FHIR) APIs, mapping patient history to payer criteria, identifying missing evidence, generating a submission packet, routing exceptions to a specialist, monitoring payer response, adjusting patient care pathways, and learning from the outcome.

This type of workflow requires coordination. It also requires guardrails: regulatory requirements, privacy standards, clinical policies, coding rules, payer criteria, and organizational risk thresholds. One promising approach is hybrid architecture that combines LLMs with structured knowledge bases, symbolic logic, reinforcement learning, and deterministic validation layers.

At Ensemble, this is the design principle behind EIQ, our revenue cycle intelligence engine. EIQ brings together operational activity, clinical documentation, payer behavior, and reimbursement outcomes into a continuously learning intelligence layer that’s integrated with the hospital’s electronic health record (EHR). It supplements the system of record with a system of intelligence, designed to connect information and surface actions most likely to improve outcomes.

EIQ uses a neuro-symbolic approach that combines LLMs and custom small language models with rules-based reasoning. That architecture is built on one of the most robust datasets in healthcare, informed by more than a decade of award-winning operational performance, transaction history, payer behavior, and operator decision-making. The language models help interpret information and generate human-readable outputs. The symbolic layer represents policies, rules, payer requirements, and workflow constraints so the system can apply guardrails, make reasoning steps more traceable and recommend actions that fit the specific operational context.

What the next decade will reward

The contribution of major AI firms to healthcare will be significant. Their models will become faster, safer, more capable, and more accessible.

But the next decade of healthcare AI will be defined by integration, not model capability alone.

The organizations that create the most value will be those that connect models to governed data, operational workflows, domain expertise, human oversight, and measurable outcomes. They will understand that healthcare intelligence cannot live in a separate interface. It has to exist inside the decisions that shape access, documentation reimbursement, and patient experience.

This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.