For many heart patients, the risks don’t end with surgery. After the patient is discharged from the hospital, follow-up may be delayed and warning signs may be missed, so we will follow the patient home.
The gap is global. For example, Pakistan has less than one doctor per 1,000 people, while sub-Saharan Africa will have an average of about 0.2 doctors in 2022, China Economic Net (CEN) reported, citing World Bank data.
Hospitals, universities, and technology companies in China are testing whether AI ECG models, disease-specific agents, and smartphone follow-up tools can extend cardiac care beyond the hospital ward.
At Fuwei Hospital, China’s national cardiovascular disease center and one of the largest cardiovascular centers in the world, a team working with cardiologist Wu Yongjian described a system designed to bring patients to the attention of clinicians after they are discharged from the hospital.
The model, unveiled at the recent Beijing Association for Artificial Intelligence (BAAI) conference in Beijing, combines inpatient records with follow-up data from surveys and wearable devices, with patients interacting through mobile tools to generate risk scores and escalation alerts between visits.
Fuwai’s broader plan, announced at the conference, is to encode the clinical expertise of experts into disease models that can support long-term patient management at scale.
The research team said at the forum that it was conducting a multicenter study to collect clinical evidence, but that study details had not been independently verified in public trial registries.
At Peking University, Hong Shengda is tackling the same problem from the device side. His group developed ECGFounder, an ECG Foundation model reported in the 2025 NEJM AI. It was built on more than 10.7 million ECGs from approximately 1.8 million subjects across 150 label categories. The model is designed to work with standard clinical ECGs as well as single-lead measurements and is relevant for mobile monitoring.
Hong’s team is also testing a miniature ECG device that would make AI-assisted ECG analysis more available between hospital visits. The user presses two fingers against the small device, waits approximately 30 seconds, and records a single-lead ECG via the mobile interface. The aim is to create a signal where there is currently a lot of silence.
For such a system to work, the alerts must be trusted by physicians. Yidu Tech, a Chinese company that builds clinical AI systems for hospitals, says its evidence-based medical agent connects conclusions to underlying guidelines and literature sources, allowing clinicians to verify the original material.
This approach reflects a broader theme at the forum. This means that medical AI is moving away from autonomous diagnostics and toward traceable support systems.
Patient records, device data, and insurance information often reside in different systems. No AI model can bridge them without access.
The Fuwai team has clearly identified this. That long-term vision requires connecting inpatient data, home monitoring data, and insurance data. Forum presentations set that connectivity as a goal rather than an entrenched reality.
There’s also the issue of liability. Continuous monitoring only works if the AI knows when to stop and hand decisions back to humans, and if it’s clear who is responsible if they aren’t. Regulation is also an open question.
Speakers noted that approval, safety evaluation, and accountability for open-ended medical AI systems remain unresolved.
Follow-up research from Hong’s group is exploring more ambitious uses for AI ECG, including biological aging of the heart and predicting future disease risk. Much of this work remains in the research phase. The gap between compelling demos and validated clinical tools is where many medical AI projects face their toughest challenges.
This approach could be important in countries where there is a shortage of doctors, including specialists, and follow-up maintenance is difficult.
Low physician density in Pakistan and parts of Africa can make regular specialist follow-up difficult. If validated, AI-assisted ECG tools and phone-based follow-up systems could help healthcare professionals identify high-risk patients earlier.
It doesn’t mean replacing cardiologists. That means leveraging our expertise to triage patients, flag warning signs, and support regular follow-up closer to home.
This model still requires local data, clinical validation, affordable devices, and clear rules about who responds when the software raises an alert. Without these, you risk remaining a hospital pilot. If it works, China’s experiment could provide a template for expanding cardiac care in physician-starved health systems.
Credit: Independent News Pakistan (INP) — Pak-China
