What are the boundaries of the "division of labor" in diagnostic and therapeutic efficacy?

The application of AI in healthcare does not equate to "replacing doctors." Currently, AI excels at "standardizing high-intensity tasks," such as medical image recognition, drug screening, and automating the consultation process. For example, in breast cancer screening, the AI model developed by Google's health team outperforms experienced radiologists. This system has been trained on millions of breast X-ray images, learning to identify subtle signs of lesions, achieving an accuracy rate of over 94%, with a significantly lower false positive rate compared to manual diagnoses.

However, what AI excels at is not "clinical intuition" or "multiple complex judgments." For instance, for a patient presenting with headache, fever, and fatigue, a human doctor would propose a diagnostic direction based on a comprehensive assessment of the patient's medical history, physical examination, and clinical experience; whereas AI systems often rely more on "questionnaire inputs," providing suggestions without actual contact or contextual judgment, making their accuracy susceptible to the structure of the information. Therefore, a more reasonable understanding at this stage is that "AI can perform part of what doctors excel at," rather than "AI can do everything doctors can do."

The essence of AI "consultation" is data modeling

Many people's first impression of AI doctors comes from various "intelligent consultation" platforms or apps. After users input their symptoms, the system provides preliminary diagnostic suggestions and recommends departments for registration. The underlying technical principle is mainly a combination of natural language processing (NLP) and decision tree modeling. The AI system constructs a network of corresponding relationships between symptoms and causes by continuously "reading" case data and medical guidelines.

Taking the "Future Hospital" launched by Alibaba Health as an example, its AI consultation system is based on tens of millions of electronic medical records and health insurance records, achieving a high matching rate for common diseases. When users input "sore throat, runny nose, cough," the system can determine within seconds that the probability of upper respiratory infection is higher and recommend seeing an ENT specialist.

However, the problems with such consultation systems are also evident. First, they rely on the accuracy of patient inputs and cannot replace physical examinations or biochemical indicators; second, their judgment capabilities for overlapping symptoms or rare diseases are limited. Most critically, their suggestions are often based on "likelihood reasoning" rather than "causal logic," lacking a true clinical interpretation and reasoning process. Therefore, in practical applications, it resembles more of a "medical knowledge retrieval tool" rather than a true clinical doctor.

Medical imaging is the main battlefield for AI to showcase its strength

If AI still appears "immature" in consultation scenarios, then its performance in medical image analysis can be described as "noteworthy." The leap in AI image recognition technology is attributed to the widespread application of deep learning, especially convolutional neural networks (CNNs). The regularity of medical imaging and the high-standard labeling system provide an ideal scenario for AI training.

In fields such as lung nodule screening, diabetic retinopathy recognition, and brain CT hemorrhage recognition, several AI systems have been applied in clinical pilot programs. For example, Tencent's MiYing identifies suspicious nodules in CT images during lung cancer early screening, achieving a sensitivity of over 95%, with a 30% increase in early lung cancer detection rates. Similarly, the retinal image recognition system developed by DeepMind has undergone clinical validation in the UK's National Health Service, capable of determining within 10 seconds whether a retinal image is abnormal, assisting doctors in deciding whether a referral is necessary.

The common advantages of these technologies are: speed, efficiency, and accuracy. AI does not get tired, is not influenced by subjective preferences, and can assist doctors in processing large volumes of data, improving screening efficiency. However, it also has shortcomings—its recognition of new diseases and abnormal presentations still relies on training samples. If the imaging features have not appeared in the data, AI struggles to reason flexibly or "generalize," which is also a key reason why it must always be paired with human review.

Has AI-assisted diagnosis and treatment entered real hospitals?

In China, several hospitals have introduced AI-assisted systems for initial diagnosis triage, prescription review, pathological analysis, and other processes. For example, the "Alpha Doctor" system used by the Second Affiliated Hospital of Zhejiang University School of Medicine can guide patients in the outpatient waiting area to fill out consultation information, preliminarily determine the cause of illness, and provide treatment suggestions; the system can even automatically generate a draft electronic medical record after the doctor's diagnosis, significantly reducing the documentation burden on doctors.

At Zhongshan Hospital in Shanghai, the AI system can analyze CT images of patients suspected of having coronary heart disease, providing doctors with a quantitative reference for the degree of narrowing, saving a significant amount of manual measurement time. Additionally, AI is also used in medical record archiving, health insurance settlement, patient follow-up management, and other non-direct diagnostic processes, improving the efficiency of hospital information operations.

However, most of these "clinical" AIs still play an "auxiliary role," not a "decision-making role." Whether in diagnosis, treatment, or medication, AI suggestions must always be reviewed and signed by practicing physicians. The reason is not due to insufficient AI performance, but rather involves extremely stringent medical ethics, legal liability, patient safety, and other systemic issues. In the existing medical system, AI can only become a "tool," not a "responsible entity."

Medical ethics and responsibility recognition remain the biggest challenges

The biggest challenge of AI in healthcare lies not in technology, but in ethics and law. A very practical question is: when an AI misdiagnosis leads to delayed treatment or adverse outcomes, who is responsible? Currently, mainstream medical systems worldwide have not established a mature mechanism for determining AI responsibility. In China, all diagnostic and therapeutic actions must be performed by licensed physicians; if an AI system provides suggestions and the doctor fails to review them reasonably, the consequences still fall on the doctor.

Moreover, the "black box" problem of AI systems is also concerning. Many deep learning models have decision-making processes that are not transparent, making it difficult to provide clear "diagnostic reasons," which contradicts the principle of "traceability" in medicine. For example, if an AI recommends a certain treatment plan, but is questioned "on what basis," the system may not be able to provide an explainable path. At this point, patient trust is hard to establish, and doctors may hesitate to adopt the recommendations.

Furthermore, AI training heavily relies on large-scale medical data, which often involves privacy, ethics, and security issues. If the data sources are not transparent or the labeling is not standardized, it may lead to systematic biases in the model. For instance, a certain AI system once had significant errors in breast cancer recognition due to a low proportion of female samples in the training set, revealing "gender bias." Therefore, the emergence of AI doctors is not only a technical issue but also a comprehensive challenge to moral and legal systems.

The future path is integration rather than replacement

The development path of AI doctors should not be dictated by the "replacement theory," but should return to the essence of healthcare—serving human health. The most promising direction at this stage is "human-machine collaboration," where AI takes on structured, repetitive tasks, freeing up doctors' energy to focus on personalized judgments, humanistic communication, and complex decision-making.

For example, in chronic disease management, AI can analyze long-term data on patients' blood sugar, blood pressure, weight, etc., providing trend alerts and warning information to help doctors intervene early; in telemedicine, AI can make preliminary judgments on images and reports, allowing doctors to serve remote areas more efficiently; in clinical teaching, AI can assist doctors in training novice physicians by providing simulated reasoning cases, enhancing teaching quality.

A deeper trend is the establishment and normative governance of an AI medical ethics framework. In the future, AI's "medical access" may have a unified review system, clearly defining the boundaries of "what can be suggested" and "what cannot be decided." At the same time, AI systems also need to enhance "explainability" design to improve public trust.

From "tool" to "partner," AI doctors still have a long way to go. They may never replace the warm "clinical gaze," but they can become the most powerful intelligent assistants beside doctors. And this is the most anticipated endpoint of AI in healthcare.