当AI进入医院时：医生和病人有什么变化

中文版正在随流水线自动整理，稍后刷新即可；可先阅读下方英文原文。

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

Thought Leaders

When AI Enters the Hospital: What Changes for Doctors and Patients

Add Unite.AI to your preferred sources on Google

In my previous column, I explored medical AI from a business perspective, focusing on the opportunities it creates for technology companies. But beyond the potential for innovation and new markets, there is a more fundamental question: what difference will these technologies make to the doctors who use them and the patients who depend on them? After all, the true value of medical innovation lies in its ability to improve the quality and accessibility of care. And I’m already seeing remarkable changes in how that care can be delivered.

How Staff Shortages Are Accelerating Healthcare Automation

I know an entrepreneur whose company provides temporary medical staff to hospitals, including nurses who fill in when regular employees are unavailable. You might expect a business like this to thrive amid persistent staffing shortages. But he says that even companies that specialize in supplying healthcare workers are struggling to find enough qualified people.

Hospitals have faced shortages of doctors and nurses for decades. Training new healthcare professionals takes considerable time and resources. Medical education, particularly in the United States, can be too expensive, and the years of study and clinical training come with financial and psychological pressure. Even after completing their training, doctors face high levels of stress, enormous responsibility, and constant time constraints.

As a result, physicians sometimes have just a few minutes with each patient to review symptoms, consider possible diagnoses, order tests, and complete the necessary documentation. This workload increases the risk of errors and leaves less time for meaningful patient interaction. And hiring more staff alone is unlikely to solve the problem.

That’s why I believe persistent staff shortages and growing workloads will be among the main forces accelerating AI adoption in hospitals.

What Can Doctors Entrust to AI?

One of the most practical applications is AI-powered systems that record conversations between doctors and patients, summarize consultations, and help prepare clinical documentation. Instead of spending time writing notes manually, physicians receive a draft they can review and edit as needed.

The benefits of these tools are already becoming evident in clinical practice. An April 2026 study published in JAMA examined data from more than 8,500 healthcare professionals across five US medical centers. Among clinicians who adopted AI-powered documentation tools, time spent on clinical notes decreased by an average of 10%, equivalent to approximately 16 minutes per eight-hour period of scheduled patient visits.

Another example is AI-assisted differential diagnosis. Imagine a patient visiting a doctor with abdominal pain and a tingling sensation on the left side. An AI system can analyze symptoms and suggest several possible causes to investigate. It doesn’t make the final diagnosis, but can help the physician develop a list of possibilities, identify follow-up questions, and determine which tests might be necessary. I’ve already encountered tools like these in practice, and I expect them to become a routine part of medical consultations. But they also raise a legitimate concern: could relying on AI-generated suggestions gradually weaken doctors’ ability to reason independently? When a system offers ready-made answers, it’s tempting to trust its conclusions, especially when there’s already so little time for each patient.

There is another side to this, however. No matter how experienced a physician is, their knowledge is inevitably shaped by the patients they’ve treated, the training they’ve received, and the research they’ve had time to study. AI models, meanwhile, can potentially identify patterns across much larger volumes of medical data, including cases an individual physician may never have encountered.

AI as a Participant in Medical Research

Another area worth exploring is complex clinical cases in which even experienced specialists struggle to reach consensus. Today, such cases are often brought before medical case studies, where physicians review test results, discuss possible diagnoses, and agree on the next steps in treatment. I don’t think AI will replace these discussions, but I can imagine it becoming another participant.

There are several ways this could work. Multiple models could independently analyze the same clinical case, allowing physicians to compare their findings and identify areas of agreement or disagreement. Alternatively, a single model could examine the case multiple times using different analytical approaches, with the results then brought together for review.

Of course, agreement among AI models doesn’t necessarily mean they’ve reached the correct conclusion. Models may share limitations, rely on similar training data, and make the same mistakes. Still, these additional perspectives could help uncover inconsistencies, flag rare conditions, or suggest possibilities that the physicians hadn’t initially considered.

For AI to contribute meaningfully to such discussions, however, it needs to consider more than isolated test results. I’ve seen the importance of this broader clinical context firsthand in our work at Keymakr. In one ultrasound imaging project, we worked with scans from the same patients taken at different times, sometimes several times a day and again months later. The team annotated anatomical structures and flagged potential abnormalities for specialist review, while accounting for changes across the patient’s examination history.

Small inconsistencies in individual records could affect the interpretation of related data across the entire span of the dataset. This experience reinforced how important it is to consider a patient’s medical history as a whole – something that would be equally essential for AI systems supporting complex clinical decisions.

MRI Without an MRI Scanner

But making a diagnosis isn’t just about interpreting medical data correctly. It’s also about being able to obtain that data in the first place. One of the most interesting AI projects I’ve come across at a Scryon-recommended event addresses exactly this challenge. A team of developers has found a way to use CT scans and several X-ray images taken from specific angles to reconstruct an MRI-like image using artificial intelligence. The idea emerged from a very practical problem: MRI scanners are expensive, and patients sometimes have to wait months for an examination, while X-ray machines and CT scanners are much more widely available.

For now, the technology focuses on the knee, although the developers are considering extending it to other joints. According to the team, the reconstructed images are intended to provide information comparable to MRI scans, even though the imaging techniques rely on different physical principles.

Rather than developing another expensive piece of medical equipment, the team is using existing imaging technology and expanding what it can do through AI-powered image reconstruction.

An Operating Room Without Borders

I expect AI to play a role in at least half of all medical processes in one form or another over the next decade. I’m talking about a broad range of applications, from medical data analysis and diagnostic support to real-time AI guidance during surgical procedures.

In the longer term, these technologies could also help overcome financial, organizational, and even geographical barriers to healthcare. Imagine a skilled surgeon in the United States assisting with an operation on a child in India, or a specialist in India helping treat a patient in Canada, without being physically present in the operating room. Advances in robotics, remote-control systems, and AI-powered surgical assistance could make scenarios like these possible.

We’re already seeing early steps in this direction. For example, the Proximie platform allows surgeons to connect remotely to operating rooms, observe procedures in real time, and collaborate with on-site medical teams. The system supports live video, voice communication, and visual annotations, and uses AI to analyze surgical workflow data. This doesn’t mean surgeons can already perform any operation remotely, but it does allow specialists to share their expertise and provide guidance without being in the same operating room.

I believe improving access to healthcare should be one of the central goals of medical AI. What matters is whether these technologies can help shorten diagnostic test wait times, ease the workload of medical professionals, and give doctors more time with their patients. Ultimately, I would measure the success of medical AI by how many more people can receive timely, high-quality medical care because of it.

Auto-fetched and translated by AI Atlas. All rights belong to the original author.