医疗AI智能体：从数据到关怀人文设计：告别冰冷精准，构建有温度的诊疗交互.131

# 医疗AI人文话术生成器：基于大模型逻辑的落地实现
def medical_ai_humanistic_response(wbc_count: float, user_emotion: str = "anxious") -> str:
    """
    医疗AI人文交互生成函数
    :param wbc_count: 白细胞计数（单位：10⁹/L）
    :param user_emotion: 用户情绪：anxious(焦虑)/calm(平静)/panic(恐慌)
    :return: 符合三要素的人文话术
    """
    # 1. 医学判断核心（技术基础）
    normal_min, normal_max = 4.0, 10.0
    infection_result = ""
    risk_level = "low"

    if wbc_count > normal_max:
        infection_result = "白细胞数值略高于正常范围，提示存在轻微细菌感染"
        # 风险分级
        if wbc_count <= 12.0:
            risk_level = "low"
        elif 12.0 < wbc_count <= 15.0:
            risk_level = "medium"
        else:
            risk_level = "high"
    else:
        infection_result = "白细胞数值在正常范围内，无感染指征"
        risk_level = "low"

    # 2. 共情表达库（人文兜底）
    empathy_dict = {
        "anxious": "我理解你看到报告的担心，",
        "panic": "我特别理解你现在的紧张心情，先不要害怕，",
        "calm": "你好，帮你解读一下报告，"
    }

    # 3. 风险分级话术库
    risk_dict = {
        "low": "这个数值略高/正常，风险很低，无需恐慌，",
        "medium": "数值有一定升高，需要多加留意，",
        "high": "数值偏高明显，存在一定风险，请务必立即就医，"
    }

    # 4. 行动指引库
    guide_dict = {
        "low": "配合医生指导用药/居家观察即可，很快会恢复；",
        "medium": "建议及时咨询医生，调整护理/用药方案；",
        "high": "不要自行用药，尽快前往医院就诊！"
    }

    # 5. 拼接三要素话术
    empathy = empathy_dict[user_emotion]
    risk = risk_dict[risk_level]
    guide = guide_dict[risk_level]

    final_response = f"{empathy}{infection_result}，{risk}{guide}"

    return final_response

# ========== 测试调用 ==========
if __name__ == "__main__":
    # 开篇案例：白细胞12，用户焦虑
    result1 = medical_ai_humanistic_response(wbc_count=12.0, user_emotion="anxious")
    print("=== 优化后人文话术 ===")
    print(result1)

    print("\n=== 对比：冰冷技术型回复 ===")
    print("白细胞计数12×10⁹/L，提示细菌感染，建议服用阿莫西林")

    # 测试恐慌情绪场景
    result2 = medical_ai_humanistic_response(wbc_count=12.0, user_emotion="panic")
    print("\n=== 恐慌情绪场景话术 ===")
    print(result2)

import re

# ===================== 配置常量 =====================
# 白细胞正常范围 10^9/L
WBC_NORMAL_MIN = 4.0
WBC_NORMAL_MAX = 10.0

# 情绪词典 & 共情模板
EMPATHY_MAP = {
    "anxious": [
        "我理解你看到异常检查报告时的担忧心情，",
        "看得出来你现在比较焦虑，请先放宽心态，"
    ],
    "panic": [
        "非常理解你此刻紧张不安的感受，先不要过度恐慌，",
        "明白你心里很害怕，我们先客观看数据，不必慌张，"
    ],
    "calm": [
        "你好，为您专业解读检查结果：",
        "您好，这份血常规指标分析如下："
    ],
    "confused": [
        "我知道你对专业指标存在疑惑，我用通俗语言为你说明，",
        "很多用户都会看不懂报告单，我帮你梳理清楚："
    ]
}

# 风险分级文案
RISK_DESC = {
    "low": "整体风险偏低，属于轻度波动或正常区间，无需过度紧张",
    "medium": "指标存在明确异常波动，需要持续观察并咨询临床医师评估",
    "high": "数值显著偏离安全范围，存在较高临床风险，严禁自行用药"
}

# 标准化行动指引
ACTION_GUIDE = {
    "low": "遵循常规医嘱居家观察、按时基础护理即可，定期复查就行。",
    "medium": "建议线下就诊结合体征综合判断，由医生调整诊疗与用药方案。",
    "high": "立刻前往正规医院急诊科或专科就诊，切勿拖延、禁止自行处置！"
}

# ===================== 风险分级函数 =====================
def get_risk_level(wbc_val: float) -> str:
    if WBC_NORMAL_MIN <= wbc_val <= WBC_NORMAL_MAX:
        return "low"
    elif WBC_NORMAL_MAX < wbc_val <= 13.0:
        return "low"
    elif 13.0 < wbc_val <= 18.0:
        return "medium"
    else:
        return "high"

# ===================== 简易文本情绪规则识别(工程兜底) =====================
def simple_emotion_detect(user_text: str) -> str:
    text = user_text.lower()
    panic_words = ["害怕","严重","绝症","怎么办","完了","危急"]
    anxious_words = ["担心","紧张","偏高","不正常","有事吗"]
    confused_words = ["看不懂","什么意思","解释一下","没明白"]
    
    if any(w in text for w in panic_words):
        return "panic"
    elif any(w in text for w in anxious_words):
        return "anxious"
    elif any(w in text for w in confused_words):
        return "confused"
    else:
        return "calm"

# ===================== 标准化三要素话术组装 =====================
def generate_humanistic_reply(wbc_val: float, user_input: str) -> dict:
    # 1 情绪识别
    emo_type = simple_emotion_detect(user_input)
    # 2 医学风险判定
    risk = get_risk_level(wbc_val)
    
    # 基础医学结论
    if wbc_val > WBC_NORMAL_MAX:
        medical_core = f"检测白细胞计数 {wbc_val}×10⁹/L，高于正常参考区间({WBC_NORMAL_MIN}~{WBC_NORMAL_MAX}×10⁹/L)，提示存在感染倾向性"
    else:
        medical_core = f"检测白细胞计数 {wbc_val}×10⁹/L，处于正常安全参考区间内，无明显感染提示"

    # 3 抽取共情随机一句
    import random
    empathy_txt = random.choice(EMPATHY_MAP[emo_type])
    risk_txt = RISK_DESC[risk]
    action_txt = ACTION_GUIDE[risk]

    final_sentence = f"{empathy_txt}{medical_core}。{risk_txt}。{action_txt}"

    # 原始冰冷版本对照
    cold_reply = f"白细胞计数{wbc_val}×10⁹/L，{'提示细菌感染' if wbc_val>WBC_NORMAL_MAX else '指标正常'}，建议遵医嘱处理。"

    return {
        "情绪类型": emo_type,
        "风险等级": risk,
        "医学核心判断": medical_core,
        "人文优化回复": final_sentence,
        "原始冰冷回复": cold_reply
    }

# ===================== 本地测试运行 =====================
if __name__ == "__main__":
    test_input = "医生，我这个数值偏高好害怕，是不是很严重？"
    res = generate_humanistic_reply(12.0, test_input)
    
    print("===== 识别结果 =====")
    for k,v in res.items():
        print(f"{k}：{v}")

MEDICAL_ETHIC_PROMPT_TPL = """
你现在是合规医疗AI咨询助手，严格遵守三条铁律：
1. 禁止独立确诊、禁止私自开具处方药品名称；
2. 输出结构固定：第一步共情安抚情绪、第二步客观风险分级说明、第三步标准化行动就医指引；
3. 高危指标、急症、生命相关症状，强制引导线下医院就诊，严禁安抚淡化风险。

用户检查数据：{report_data}
用户原始提问&情绪上下文：{user_query}
基础医学参考结论：{base_medical_conclusion}

请用温和专业、通俗易懂语言生成合规人文回复，严禁冰冷罗列数据，禁止绝对化诊疗判定。
"""

# 调用填充示例
if __name__ == "__main__":
    prompt_fill = MEDICAL_ETHIC_PROMPT_TPL.format(
        report_data="白细胞计数：12×10⁹/L",
        user_query="看到化验单数值高特别担心身体情况",
        base_medical_conclusion="轻度高于正常上限，存在轻微感染趋势，非危重指标"
    )
    print("===== 送入大模型最终Prompt =====")
    print(prompt_fill)

def ethical_security_check(reply_content:str, risk_level:str)->tuple[bool,str]:
    """
    返回：是否通过校验、拦截原因
    """
    # 高危禁止自行用药建议
    forbidden_words = ["直接吃","立刻服用","自行买药","阿莫西林","头孢","抗生素随便吃"]
    for w in forbidden_words:
        if w in reply_content:
            return False, "伦理拦截：严禁AI直接指导具体处方药服用"
    
    # 高风险未强制就医拦截
    if risk_level == "high" and "医院" not in reply_content and "就诊" not in reply_content:
        return False, "伦理拦截：高风险指标必须强制引导线下就医"
    
    # 禁止确诊定性
    if "确诊" in reply_content or "肯定得病" in reply_content:
        return False, "伦理拦截：AI不可做出疾病确诊结论"

    return True, "伦理校验通过"

# 测试接入
if __name__ == "__main__":
    test_reply = "你可以自行吃阿莫西林消炎"
    ok, msg = ethical_security_check(test_reply, "high")
    print(ok, msg)

def batch_test_demo():
    cases = [
        {"wbc":12.0,"text":"我好害怕是不是严重生病"},
        {"wbc":8.5,"text":"帮我看看报告单正常吗"},
        {"wbc":16.2,"text":"数值太高我特别焦虑睡不着"},
        {"wbc":22.0,"text":"这个结果到底什么意思啊看不懂"}
    ]
    for idx,case in enumerate(cases,1):
        res = generate_humanistic_reply(case["wbc"],case["text"])
        pass_ok,reason = ethical_security_check(res["人文优化回复"],res["风险等级"])
        print(f"\n【测试案例{idx}】")
        print(f"原始输入：{case['text']}")
        print(f"伦理校验：{pass_ok} | {reason}")
        print(f"最终输出：{res['人文优化回复']}")

if __name__ == "__main__":
    batch_test_demo()