#healthcare-ai

#healthcare-ai

Whenever medical AI handles anything with medium to high risk, you want regulation: internal self-regulation or external governmental regulation. It's mostly been internal thus far, and there are differences in how each hospital system validates, reviews, and monitors healthcare AI. When done on a hospital-by-hospital basis like this, costs to do this kind of evaluation and monitoring can be significant, which means some hospitals can do this, and some can't.

Healthcare is under unprecedented strain, Demand is rising, clinicians are overwhelmed by administrative work, and critical medical knowledge is fragmented across countless sources. At the same time, AI adoption in healthcare is gaining momentum, driven by its potential to help address these challenges. Advances in models have significantly improved AI's ability to support real-world clinical and administrative work, like helping clinicians personalize care using the latest evidence.

In this context, trust is not just an emotional response. It is about system reliability, the confidence that an AI assistant will behave predictably, communicate clearly, and acknowledge uncertainty responsibly. In healthcare, that reliability is not optional. Even when AI performs well, people still hesitate. They ask: Can I rely on this? Does it really understand me? What happens if it's wrong?

AI is entering one of the most human domains: healthcare. It helps people track sleep, manage chronic conditions, monitor mental health, and navigate loneliness. It listens, advises, and sometimes comforts. Yet despite these advances, hesitation remains, not because the algorithms are weak, but because the experience does not always feel reliable. In this context, trust is not just an emotional response. It is about system reliability, the confidence that an AI assistant will behave predictably, communicate clearly, and acknowledge uncertainty responsibly.

Post-acute care providers are spending 25 cents of every dollar on administrative tasks rather than patient care, resulting in structural inefficiency that has intensified as private equity consolidation demands operational excellence while caregiver shortages and regulatory complexity reach critical levels. Traditional electronic medical record systems were built for clinical documentation, not workforce coordination, leaving front-office teams drowning in scheduling conflicts, compliance tracking, and hiring pipelines that consume resources without adding clinical value.

the company said in a press release, adding that "Pfizer will also participate in a direct purchasing platform, TrumpRx.gov, that will allow American patients to purchase medicines from Pfizer at a significant discount. The large majority of the company's primary care treatments and some select specialty brands will be offered at savings that will range as high as 85% and on average 50%."

OpenEvidence, a tool that doctors and nurses have likened to ChatGPT for medicine, plans to announce a $200 million raise at a $6 billion valuation, The New York Times reports. The fresh funds come three months after the startup raised a $210 million round at a $3.5 billion valuation, a testament to the intense investor interest in industry-specific AI applications.

Ten years ago, IBM announced with great fanfare that Watson for Oncology was as accurate as human physicians in reading X-rays, CT scans and other reports. In some regions lacking oncologists, IBM even promoted Watson as a potential substitute for doctors. But the reality soon surfaced. According to ASH Clinical News, internal documents revealed that Watson made unorthodox and unsafe recommendations when provided with synthetic (rather than real) patient data.

At present, most AI agents in healthcare are used as a chatbot which speaks directly to patients. However, the modern capabilities of AI allows them to become more than that. AI agents like Orbit AI, for example, have found great success on the back-end by having AI automate and expedite administrative tasks. Insurance information, benefit verification, and automated prior authorization requests are just 3 ways that AI is making a big difference for healthcare businesses.

Midi, which provides virtual care for perimenopause, menopause, and other midlife women's health conditions, has raised $50 million in a Series C round led by Advance Venture Partners. The raise brings its total funding to about $150 million.Cofounder and CEO Joanna Strober confirmed the raise in an interview with BI. Midi has a $150 million revenue run rate, Strober said, up from about $60 million at the end of 2024.

A new face is lighting up hospitals and nursing homes across the country. It's an animated, cartoonish persona displayed on a digital screen roughly the size of an iPad, mounted on top of a robotic torso shaped like an elongated traffic cone. It slowly rolls around from place to place, cracking jokes with patients, making silly faces, and playing small games.

Archie Mayani is the chief product officer at GHX, a global supply chain company that uses data and cloud-based technologies to connect healthcare providers like hospital systems and their suppliers. For more than 20 years, Mayani has worked on clinical and supply-chain health technologies at companies like Change Healthcare and United Health Group. At GHX, Mayani works to ensure that the company develops technology that can help hospitals procure patient supplies - like implants and IV fluids - as seamlessly as possible.

By then the preparations for a future televised Jeopardy! contest with IBM's creation were well underway, but this was the first time Trebek would encounter the technology in person, and his approval was crucial. Ferrucci was eager to show off one element in particular: the display, which had been rigged to show Watson's top three guesses whenever it answered, along with the numerical confidence rate it had in each one.

Baseten just pulled in a massive $150 million Series D, vaulting the AI infrastructure startup to a $2.15 billion valuation and cementing its place as one of the most important players in the race to scale inference - the behind-the-scenes compute that makes AI apps actually run. If the last generation of great tech companies was built on the cloud, the next wave is being built on inference. Every time you ask a chatbot a question, generate an image, or tap into an AI-powered workflow, inference is happening under the hood.

During six months of chemotherapy in Singapore in 2015, I worked on my scholarship application for an MBA. I interviewed between my 9th and 10th chemo sessions.