Multi-culture medical AI supervision and audit platform

Abstract

According to the multi-culture medical AI supervision and auditing platform provided by the invention, layered review is carried out on medical AI decisions by introducing a semantic sovereignty framework, so that the accuracy, fairness and compliance of the AI in a cross-culture situation are ensured. A multi-culture knowledge rule base is arranged in the platform, and the whole process of AI from data extraction, knowledge application to decision output is monitored. For each layer of semantics, the auditing unit automatically detects whether the AI has the problems of cultural semantic misunderstanding (such as misunderstanding of traditional terms such as qi deficiency), knowledge bias (such as neglecting local medicine by only adopting western medicine knowledge) or decision-making bias (systematic unfairness exists in suggestions for people with different cultural backgrounds) and the like. Once found, the platform generates an audit report alert and gives adjustment suggestions. Meanwhile, the platform checks ethical suitability of AI suggestions, and the situation that believes of patients and cultural custom taboo are violated is avoided; and through intention layer auditing, the AI decision is ensured to conform to value orientation and medical strategies of the same region. According to the method, the medical AI decision is more transparent and controllable, and the semantic rights and interests of each culture group in the AI era are maintained. As an innovative AI treatment tool, the platform can be used for a supervision institution and a medical institution to continuously audit various intelligent diagnosis and treatment systems so as to realize'controlled autonomous' AI: each cultural context is fully and autonomously learned, human expectation and ethical constraints are followed in output, and a solid guarantee is provided for safe and fair AI medical fusion in a global range.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence governance and medical ethics supervision, specifically a multicultural medical AI regulatory audit platform. This platform evaluates the recommendations and decisions of medical AI systems in cross-cultural contexts in real time, monitoring for semantic misunderstandings, biases, or ethical conflicts, thereby preventing the impact of "semantic colonization" and cultural biases on medical intelligence. Background Technology

[0002] As artificial intelligence (AI) is increasingly applied in medical diagnosis and health management, the transparency and fairness of its decision-making process have become a major concern. Especially in multicultural contexts, AI may implicitly favor a particular medical context or data source, leading to misunderstandings or even neglect of knowledge systems from other cultures. For example, diagnostic AI trained on Western medical data may fail to correctly interpret cases with descriptions of symptoms from traditional medicine, or it may blindly follow Western standards while rejecting local therapies when suggesting treatment plans. This tendency is termed "semantic colonization" by some scholars, where AI systems overlay the expressions of less mainstream cultures with the semantics and values ​​of the mainstream culture. In fact, the 2024 Stanford AI Index report pointed out that a significant portion of Chinese corpora comes from translations or overseas platforms, and this data composition makes Chinese AI semantically biased towards the West. In the medical field, this bias can have adverse effects on people from different cultural backgrounds, such as ignoring their traditional preferences or misinterpreting their symptoms, leading to unfair treatment recommendations. Furthermore, different cultures have different ethics and preferences regarding disease and treatment; for example, some patients may refuse certain treatments due to their beliefs and cultural customs. If AI is unaware of this, it may provide conflicting recommendations, damaging patient trust. Existing AI governance frameworks primarily focus on the overall fairness and performance of algorithms, but lack specific auditing tools for cultural dimensions. To ensure the safe and effective use of medical AI globally, it is necessary to establish a real-time oversight platform at the multicultural semantic level to review the cultural and semantic suitability of every AI decision. Summary of the Invention

[0003] This invention provides a multicultural medical AI oversight and auditing platform for real-time monitoring and auditing of the output and internal semantic processes of medical AI systems, with a focus on examining cultural and semantic risks. Based on a "semantic sovereignty" framework, the platform decomposes the AI's decision-making process into semantic levels such as data, information, knowledge, wisdom, and intent, introducing audit nodes at each level. These audit nodes contain pre-defined cultural context rules and value constraints, enabling the detection of whether the AI ​​deviates from the requirements of the target culture during semantic processing. For example, at the information level, the audit node checks whether the AI's understanding of the input symptom description accurately covers the patient's original intent (e.g., whether it correctly understands "qi deficiency" rather than simply discarding it); at the knowledge level, it checks whether the knowledge cited by the AI ​​takes into account the local medical knowledge base (rather than relying solely on Western medical knowledge for reasoning); at the wisdom level, it assesses whether the AI's proposed solutions violate the patient's cultural taboos (e.g., whether it recommends ingredients or solutions that conflict with the explicit taboos of patients with specific ingredient avoidance requirements or specific dietary / lifestyle restrictions); and at the intent level, it reviews whether the AI's decisions conform to the local medical ethics and strategic goals. The platform generates a semantic audit report for each decision, identifying potential semantic misunderstandings, biases, or ethical risks, and providing adjustment suggestions. For example, if the AI's suggestion does not include herbal remedies favored by the patient, the audit report will warn that "the solution does not cover the patient's culturally preferred treatments, indicating semantic bias." If the AI ​​uses inappropriate stereotypes to interpret the symptoms of patients from different cultural backgrounds, the system will flag the risk of bias and require the model to correct it. Through this platform, regulatory agencies and medical institutions can transparently understand whether the behavior of medical AI is compliant in various cultural environments, and developers can also improve their models accordingly, truly achieving human-centered alignment in AI decision-making. This invention has strategic significance globally: it safeguards the semantic autonomy of people from different cultural backgrounds in the era of medical AI, preventing AI technology from becoming a new tool of cultural hegemony. As a pioneering cultural semantic audit mechanism, this invention is expected to become part of international AI governance standards, possessing extremely high intellectual property and social value.

[0004] The technical solution of the present invention includes:

[0005] A multicultural knowledge rule base: This base includes key semantic and ethical rules from various major medical cultures. For example, Traditional Chinese Medicine (TCM) semantic rules include the correct interpretation of concepts such as "Yin and Yang" and "Qi and Blood," as well as local medical taboos (e.g., certain medications should not be used by pregnant women); rules related to specific beliefs and cultural customs include compliance requirements for drug ingredients and dietary or rest restrictions during specific time periods; Western medical rules include evidence-based requirements and bioethical guidelines. This base is represented in machine-readable logical rules and ontology format, and can be called and compared by the audit module.

[0006] The AI ​​decision tracking module, embedded in the medical AI system, intercepts intermediate representations and rationales at each level of the AI's process from receiving input to outputting suggestions. Combining the DIKWP model, it labels and organizes the AI's internal state according to data, information, knowledge, wisdom, and intent layers. For example, for an input case, it records the symptom information extracted by the AI ​​(I layer), the knowledge items used (K layer), the trade-offs (W layer), and the goals (P layer). This ensures that the auditing platform obtains sufficient process data, not just the final output.

[0007] Semantic audit analysis module: Contains several sub-modules, performing audits at different levels:

[0008] Input semantic integrity audit: Compare the patient information extracted by AI with the original statements, and use a cultural knowledge base to determine whether there are any misunderstandings or omissions. For example, if a patient says "heart fire is strong," but the AI ​​extracts it as "heart burning sensation," the audit will determine the deviation and suggest that the AI ​​may have missed the association with emotional symptoms.

[0009] Knowledge Source Diversity Audit: Analyzing whether the knowledge entries cited by AI inference come from a single source. For example, if the AI ​​only uses a Western medicine knowledge base without consulting a traditional Chinese medicine knowledge base, it may be judged as having semantic bias when dealing with East Asian patients.

[0010] Decision-making bias audit: By simulating input of the same medical condition from different cultural identities, the system detects differences in AI output and identifies potential biases. For example, if the AI ​​gives different levels of attention or different treatments to patients of different ethnicities for the same symptoms, it may imply unfairness, and the platform will record and warn of this phenomenon.

[0011] Ethical compliance audit: This examines whether the AI-driven approach violates ethical taboos or values ​​within the patient's culture. For example, if a patient with specific contraindications or treatment preferences is recommended a drug containing a contraindicated ingredient, or if the communication style fails to respect their long-established treatment choices and values, the risk is flagged and alternative recommendations are suggested in the report.

[0012] Semantic sovereignty consistency audit: Utilizing a semantic sovereignty framework, this audit verifies whether the AI's internal intent-level decisions align with the health strategies and values ​​of the host country / region. If the AI's decision-making logic deviates from local policies (e.g., neglecting primary care prevention while favoring expensive medications), the platform will flag it as inconsistent with national interests.

[0013] The reporting and feedback module synthesizes the results from each submodule to generate an audit report that is both machine-readable and human-readable. The report lists: the items checked and whether they passed, a summary of the issues (e.g., "Knowledge bias: Failure to reference local medical knowledge"), the risk level, and recommended measures (e.g., "Introduce a Traditional Chinese Medicine knowledge graph"). The report can be sent to AI operators and regulators. The platform also provides an interface to push feedback to the AI ​​model for online adjustments or to mark data requiring improvement. Attached Figure Description

[0014] Figure 1 This is the overall architecture diagram of the multicultural healthcare AI regulatory audit platform.

[0015] Figure 2 A schematic diagram of semantic hierarchical tracking for AI decision-making.

[0016] Figure 3 This is a sample audit report.

[0017] Figure 4 This is a diagram illustrating the deployment of the platform and AI system. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be emphasized that these embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention.

[0019] Example 1: Translation and Annotation of a Classical Chinese Medicine Text. Take, for instance, a classic line from the *Huangdi Neijing Suwen*: “If one is harmed by wind in spring, diarrhea may occur in summer.” The original meaning is that if one is affected by wind pathogens in spring, diarrhea is likely to occur in summer. The engine first identifies the seasons “spring” and “summer” in classical Chinese, and the causes “wind” and symptoms “diarrhea.” The NMT model translates this into modern English: “If one is harmed by wind in spring, diarrhea may occur in summer.” Then, the semantic conversion module intervenes, recognizing that “wind” here is not ordinary wind but a concept related to the cause of disease in traditional Chinese medicine, adding an annotation explaining it as “wind pathogens (a type of exogenous pathogenic factor).” Simultaneously, “diarrhea” is labeled as “a type of diarrhea, possibly caused by disharmony of the spleen and stomach.” The final annotated translation is: “If one is harmed by wind pathogens in spring, one may develop diarrhea (digestive upset) in summer.”* A node is added to the knowledge graph: (Spring, Wind Pathogens, Summer Diarrhea). This result is provided to readers with a Western medical background, who can understand that the original text discusses the link between seasonal allergies / infections and summer diarrhea, which is similar to the pathogenic mechanism of climate factors in modern medicine.

[0020] Example 2: Translation of an Ayurvedic Case. A case recorded in Sanskrit describes a patient presenting with "indigestion and irritability due to excessive bile." The engine translation renders the Sanskrit as "Excess bile leads to indigestion and irritability." The semantic module further interprets "bile" as an increase in Pitta in Ayurveda, associated with high gastric acid or bile reflux in modern medicine. Therefore, the annotation translates as: "Excess Pitta (bile, analogous to high gastric acid) leads to indigestion and irritability." The knowledge graph extracts: (excess bile, leading to, indigestion) and associates it with the modern concept of "excessive gastric acid secretion." Researchers can see that ancient cases describing similar conditions of peptic ulcers or gastroesophageal reflux can serve as historical comparative studies of the evolution of these diseases.

[0021] Example 3: Multilingual Teaching Assistance. A TCM college's bilingual teaching required students to understand meridian theory. The teacher input the classic passage, "The collateral vessels of the Heart Channel… its disease manifests as chest fullness in excess, and speechlessness in deficiency." The engine translated it into modern Chinese: "Diseases of the collateral vessels of the Heart Channel, in excess, manifest as chest fullness; in deficiency, one cannot speak." Simultaneously, it translated it into English: "For the Heartchannel's collateral, in excess there is chest fullness, in deficiency one cannot speak." Semantic annotations added: "(Heart Channel: anatomically corresponds to the cardiac neural network; 'speechlessness' may refer to aphasia due to stroke)." In this way, students quickly understood the possible modern correspondence of meridian pathogenesis (cardiac nerve disorders affecting the language center) by comparing the Chinese and English versions with annotations. This example demonstrates that engines can assist in the compilation and teaching of bilingual textbooks, accelerating the cultivation of internationally-minded talents who understand both traditional medical theory and modern medicine.

Claims

1. A multicultural medical AI regulatory audit platform, comprising a multicultural knowledge rule base, a decision tracking module, a semantic audit analysis module, and a report feedback module, characterized in that: The multicultural knowledge rule base stores a set of rules for semantic understanding and ethical norms under different medical cultural backgrounds, defining the semantic scope and value constraints that the AI ​​system should follow in the form of ontology and logical rules. The decision tracking module is integrated with the audited medical AI system and can extract and record data from each cognitive layer when the AI ​​processes medical cases, including input symptom information, knowledge sources used, key judgments in the reasoning process, and final suggested solutions. The semantic audit analysis module includes: an input semantic integrity audit unit, used to compare the information extracted by the AI ​​with the patient's original statement and determine whether there are any semantic omissions or misinterpretations based on the rule base; and a knowledge source diversity audit unit, used to detect whether the AI ​​takes into account the patient's background during reasoning. The system includes: a cultural and medical knowledge audit unit (if not semantically biased); a decision bias audit unit (identifying potential cultural or group biases in AI decision-making through cross-population consistency testing); an ethical compliance audit unit (verifying whether AI recommendations violate patient cultural taboos or ethical principles); a semantic sovereignty consistency audit unit (checking whether the AI ​​decision intent layer aligns with local human values ​​and strategic goals based on the DIKWP hierarchy); and a report feedback module that generates an audit report including the inspection results and risk level indicators of each of the above units, as well as adjustment suggestions for the identified issues. This report is provided to AI operators and regulatory agencies, and supports the application of audit feedback to parameter updates or rule adjustments in the AI ​​system, thereby achieving continuous monitoring and improvement.

2. The platform as described in claim 1, characterized in that: The input semantic integrity auditing unit uses natural language processing and semantic similarity calculation to determine whether the AI's understanding of the patient's free text description (including dialects or traditional medical terms) is biased. If it detects that preset keywords (such as traditional medical terms) are omitted or mismapped, it will trigger a warning.

3. The platform as described in claim 1, characterized in that: The decision bias auditing unit uses an A / B control experiment method, submitting the same medical case multiple times to the AI ​​for comparison by only changing the patient's cultural background label. If there are systematic differences in the output, statistical methods are used to determine the degree of bias, and possible sources of bias (uneven training data, improper model assumptions, etc.) are marked in the report.

4. The platform as described in claim 1, characterized in that: The ethical compliance auditing unit matches the AI-recommended drugs, diets, and therapies with a database of patients' cultural taboos. At the same time, it performs emotional and phrasing analysis on the dialogue to identify whether there are any disrespectful or discriminatory terms used against specific groups. Once a violation is found, it is marked as a violation.

5. The platform as described in claim 1, characterized in that: The semantic sovereignty consistency audit unit introduces a sovereign intent parameter into the "Purpose" layer of the DIKWP model, corresponding to the healthcare service value goals set by the country or institution. An alarm is triggered when the AI's decisions deviate from these goals. This unit ensures that when imported foreign AI systems run locally, their outputs must conform to local semantic specifications and value criteria.

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