A doctor reviewing a complex case may need to search through medical literature, check the latest clinical guidelines, assess drug interactions, interpret test results and bring together years of patient information, often under significant time pressure.

This is where Zhiyi (知医) comes in. Developed by JINGDONG Health (also known as JD Health), Zhiyi is an evidence-based AI assistant built specifically for healthcare professionals. Its Chinese name fittingly brings together the ideas of “professional knowledge” (知) and “doctor” (医).

Launched in January 2026, Zhiyi brings AI into the everyday workflow of doctors in China, supporting clinical decision-making, medical research, patient management and other professional tasks. It has integrated more than 50 million medical papers, clinical guidelines and authoritative journal resources, together with information from more than 160,000 pharmaceutical product instructions. The platform has already served over one million doctors and supported more than 20 million clinical decision-making interactions.

From Finding Information to Understanding the Evidence

Searching for medical information is one thing. Turning it into useful evidence for a particular patient or clinical situation is much harder.

Zhiyi is designed to help doctors move beyond simple search. Its Deep Thinking mode can break down complex questions, assess different levels of evidence and provide structured references for doctors to consider, including in cases involving multiple conditions or medications.

A good example is oncology. Zhiyi has integrated the China Anti-Cancer Association (CACA) Guidelines for Holistic Integrative Management of Cancer, which cover 29 cancer types and 72 diagnostic and treatment technologies. By making this body of specialist knowledge searchable and actionable through AI, Zhiyi can help doctors access relevant evidence when they need it, rather than requiring them to navigate lengthy reference materials separately.

Zhiyi also offers more than 200 medical calculators, covering areas such as disease assessment, risk stratification, medication dosage and cardiovascular risk, with AI providing evidence-based interpretation of the results. Its research assistant extends these capabilities into academic work, supporting literature reviews, patient education materials and presentations.

The aim is simple: reduce the time doctors spend navigating information so they can focus more of their expertise on understanding the patient and making clinical decisions.

Making AI Fit the Way Doctors Actually Work

Clinical information rarely arrives as a perfectly structured prompt.

It may be a laboratory report, prescription, medical image, PDF document or information collected across previous consultations. Zhiyi’s multimodal capabilities allow doctors to upload and analyze different types of clinical materials, reducing the need for manual entry and page-by-page review.

More importantly, Zhiyi is designed to show doctors where its conclusions come from. Its dynamic evidence-location capability can identify the original text supporting a clinical point, allowing doctors to trace AI-generated information back to the underlying evidence.

That distinction matters in healthcare. A useful medical AI assistant should not simply produce an answer; it should help a professional understand the evidence behind it.

When “Sounds Right” Isn’t Good Enough

Generative AI can produce convincing answers. In medicine, however, convincing is very different from reliable.

JD Health has therefore built dedicated evaluation systems around Zhiyi. Its MedScope framework evaluates AI outputs across evidence quality, content accuracy, appropriate expression and practical usefulness, supported by evaluation sets spanning 46 major clinical specialties.

A separate MedSafety framework evaluates 26 categories of high-risk medical scenarios, including medication contraindications and complication warnings. High-risk outputs that cross defined safety boundaries do not pass the evaluation.

This reflects an important principle behind JD Health’s approach: AI is there to support medical professionals, not to replace their experience, judgment or responsibility.

One AI Foundation, Different Healthcare Supports

Zhiyi is part of a broader AI healthcare architecture JD Health has been developing.

Underneath it is Jingyi Qianxun (京医千询), JD Health’s proprietary medical large language model. Rather than positioning the model itself as the final product, JD Health is applying its capabilities to specific healthcare workflows, including evidence retrieval, clinical support, patient follow-up and medical research. Zhiyi is one example of how that underlying AI capability is translated into a practical professional tool.

On the patient-facing side, Dawei (大为), JD Health’s AI doctor, applies AI to another part of the healthcare journey. JD Health has also launched more than 1,500 AI agents based on specialist doctors, alongside solutions developed for hospitals and specific disease areas.

Together, they illustrate a broader strategy: rather than building one AI assistant for every healthcare problem, JD Health is developing specialized AI tools around the needs of different users and healthcare scenarios.

Taking Medical AI Beyond Major Hospitals

Perhaps one of the most meaningful tests of medical AI is whether it can make high-quality tools more widely accessible.

In August, JD Health formed a strategic partnership with ‌PICA Health (Yunqueyi), one of China’s largest digital platforms serving primary-care doctors. Zhiyi has been integrated into the PICA Health app, making its upgraded capabilities, including Deep Thinking, medical calculators and research assistance, available free of charge to the platform’s registered primary-care doctors serving. PICA Health connects around three million registered primary-care doctors serving China’s grassroots.

The significance is not simply putting AI into more hands. A doctor working in a smaller community can use the same AI tool to access medical literature, clinical guidelines and evidence-based decision support that would otherwise be more difficult or time-consuming to obtain.

And this is where JD Health brings another capability into the picture: supply chain infrastructure.

JD Health’s pharmaceutical supply chain reaches more than one million villages in China, while same- or next-day delivery coverage has reached 87.1% across county-level areas covered by its network.

This creates an interesting connection between medical intelligence and healthcare access: AI can help doctors find and interpret the right evidence, while supply chain capabilities can help turn a clinical decision into access to the medicines and healthcare products patients need.

Building AI Around Healthcare, Not Healthcare Around AI

There is no shortage of discussion about how powerful the next generation of medical AI models will become. JD Health’s experience with Zhiyi suggests that model capability is only part of the equation.

Healthcare AI also needs trusted medical evidence, safety mechanisms, integration into real clinical workflows and a way to reach the doctors and patients who can benefit from it.

That is why Zhiyi is particularly illustrative of JD Health’s broader approach. The technology begins with a medical large language model, but extends into authoritative resources such as the CACA Guidelines, professional tools for doctors, partnerships such as PICA Health, patient-facing services such as Dawei, and JD Health’s wider healthcare and supply chain infrastructure.

Doctors’ experience, judgment and responsibility remain at the center. The role of AI is to help those capabilities go further by making trusted knowledge easier to reach, complex information easier to navigate and everyday clinical work more efficient.

The more meaningful question for healthcare may therefore be not whether AI can become a doctor, but how AI can help every doctor do more for every patient.

(vivian.yang@jd.com)