With the rapid development of AI technology, medical AI is gradually becoming a key force driving change in the medical industry. This article provides an in-depth analysis of the future development trend of medical AI and discusses its wide application in the medical and health field for your reference.
Medical AI has attracted much attention in the field of AI, and the discussion continues to rise. Various AI technologies have a place in healthcare: complex diagnoses rely on reasoning capabilities; The management of the whole course of the disease requires long-window memory, and in the future, it can even record the health information of an individual’s life. medical image understanding, auscultation, etc. require multimodal technology; For verification and paper searching, RAG (retrieval augmented generation) should be used.
However, there is still a lack of “leaders” in the field of medical AI for three main reasons:
- Data flywheel construction problem: The medical industry needs a large amount of special data, and the lack of this data in general models has become a major obstacle to the development of AI healthcare. At the same time, doctors are one of the complex human professions, and the “medical content” of the model is still in the exploratory stage.
- Lack of evaluation criteria: The lack of unified criteria to evaluate the capabilities of large medical AI models leads to unclear development and improvement directions, and high trial and error costs.
- Industry Characteristics Constraints: AI healthcare is strictly subject to objective factors such as ethics, and manufacturers face strict authority and qualification reviews.
China’s medical market has huge potential, with 8.4 billion visits a year, and Baidu has more than 50 million health search requests and more than 400 million requests a day. In the long run, chronic disease and health management are expected to become super applications. On the C-side, the super app can become a “doctor friend” for users, familiarize themselves with users, understand users, achieve equal and worry-free communication, and become a personal and exclusive health butler; On the B-side, it will be the doctor’s assistant, assisting in diagnosis and management of the entire course of the disease.
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Specific to medical scenarios, it covers the following links:
- Before treatment: intelligent hospital customer service, department guidance, doctor recommendation, pre-consultation.
- During medical treatment: doctor-patient dialogue and medical records are automatically generated.
- After treatment: follow-up management, patient file transfer to physical examination institutions and other services.
2. Dismantling and analysis of cutting-edge AI medical applications
In this article, I have selected two AI medical applications that I find refreshing, representative and leading, and I will disassemble and analyze them some in the hope of giving you and me some inspiration for doing AI medical health.
1. The world’s first “AI clinic” landed in Saudi Arabia
1) Product positioning:
This is a standardized initial diagnosis product based on AI, allowing AI to take the first step towards “independent medical treatment”. Traditional large model systems (such as GPT) are mostly “questioned” in conversations, while in reality, doctors are the active party asking questions.
To truly replace doctor consultations, AI must have the ability of a “diagnostic dialogue system”. For example, general large models such as ChatGPT are faced with “What should I do if I get acute pharyngitis the day after the first dose of the new crown vaccine?” When this type of question is asked, it is often just a matter of analyzing the condition and recommending medical attention, while human doctors will ask about the symptoms to further rule out less likely diseases.
At this stage, AI clinics are not replacing top doctors, but are aimed at a large number of “common diseases” scenarios in primary care, especially in areas with a shortage of doctors, weak diagnosis and treatment capabilities, and insufficient informatization, aiming to “liberate doctors”.
2) Product architecture:
AI clinics integrate speech recognition, multi-round question-and-answer logic, medical knowledge graph, disease reasoning, and standardized path control to form a three-tier architecture of “AI-led, doctor-reviewed”: AI is responsible for initial diagnosis and judgment, and doctors issue final diagnostic opinions. This closed-loop structure allows AI to operate independently in real-world clinical scenarios and assume initial responsibility. After the patient enters the AI clinic, the diagnosis and treatment is completed through the collaborative mechanism of “AI + human assistant” – AI issues instructions, the human assistant collects data, and the AI receives, analyzes, and updates the diagnosis and judgment in real time.
3) Strategy design:
The system has multiple safety mechanisms, and the AI does not need to have the ability to treat cancer, but it must be able to recognize that “this may be cancer”. When encountering any potentially complex conditions or difficult symptoms, AI automatically triggers a referral or physician review process to avoid the risk of missed or misdiagnosed.
4) Collaboration mode between AI and doctors:
This “AI-centric” diagnosis and treatment structure has achieved a disruptive improvement in efficiency. In traditional outpatient clinics, it takes an average of 10-15 minutes for doctors to see a patient, but in AI clinics, AI can independently complete consultations, information collection, and preliminary judgments, and doctors only need less time for final review and issuance. The “AI-led consultation + human doctor confirmation” model increases efficiency tenfold, providing a new “amplifier” for the resource-constrained primary care system.
2. Good companion AI
Another AI application is a product launched by Hangzhou-based technology company “Zhijiang Technology” – Haoban AI, which has some eye-catching medical AI functions and differentiated technologies. It has an AI expert clone that is online 24 hours a day, 7× and has functions such as chronic disease management, intelligent guidance, and digital health consultant.
1) Product Function:
- Test sheet analysis: Upload the bilirubin/breast nodule report, complete the extraction of key indicators and review recommendations within 2 minutes, and the conclusion is completely consistent with the diagnosis of tertiary medicine
- Long report processing: For 30-page physical examination report PDF, an abnormal item analysis report is generated in 10 minutes to accurately locate risks such as abnormal ST segment of the electrocardiogram
- Expert digital clone: simulates the diagnosis and treatment style and expert level of Wang Liquan and other chief physicians of the Second Hospital of Zhejiang University, and is online 24 hours a day
- General practice consultation: TCM qi and blood diagnosis provides a self-examination method of “tongue and color”, synchronously analyzes the causes of fatigue from the perspective of modern medicine, and solves health anxiety with dual solutions
2) Technical Architecture & Model:
Behind it is WiseDiag-Z1, a general language model for general practice with 73 billion parameters, which has deeper thinking ability, more professional report interpretation, and a more personalized health management experience, so we can ask it to help provide symptom analysis, medication consultation and personalized health guidance like a conversation.
(1) Context and long memory ability:
As an online medical product, it must be able to accompany for a long time, and be able to remember the user’s past consultation records, without having to re-express it every time. With the help of multi-layer memory storage + dynamic knowledge base (12,000 diseases), cross-cycle health tracking is not a problem, WiseDiag can realize accurate information traceability of long-term memory, can store and recall users’ past health information, and each important information will be remembered by the model and form personalized content.
(2) Model capabilities:
- Base model: The AI team has “fed” more than 3 trillion tokens of professional medical data to the model, including authoritative textbooks (such as “Internal Medicine” and “Obstetrics and Gynecology”), the latest clinical guidelines (such as NCCN, Chinese Medical Association series), 500,000 medical papers and real case databases. The medical coding model (Med-Embedding) accurately distinguishes clinical semantic differences (such as “repeated low-grade fever” and “high fever for three days”).
- Fine-tuning: The team also invites clinicians to participate deeply. Through supervised fine-tuning (SFT) and direct preference optimization (DPO), it aligns the logic of human doctors’ diagnosis and treatment.
- Reasoning ability: It adopts a unique multi-level reasoning mechanism, which can conduct in-depth and detailed analysis and thinking and repeated trade-offs like an expert doctor; In key links such as etiological analysis, differential diagnosis, and treatment plan, it can simulate experts and doctors thinking layer by layer until they find the optimal solution.
(3) Knowledge base:
By integrating the clinical experience, medical notes, scientific research papers, lecture videos and other unique personal information of director-level experts of tertiary hospitals, it is created using AI technology to perfectly replicate the real thinking of experts.
3) Future planning:
It is planned to launch a special model for pediatric and chronic disease management and explore the docking with the medical insurance system. In the future, it may integrate smart wearable device data to achieve health early warning and real-time monitoring.
Written at the end
I believe that in the future, medical care will develop in these three directions: doctor-centered to patient-centered, hospital-centered to home-centered, and disease-centered to health-centered. In the end, the large model will “make doctors” and form a new supply.
