医疗保健从业者对人工智能的真实需求
内容总结：
【业界观察】医疗机构AI需求转向务实：不炫技，要实效
在人工智能技术快速发展的当下，医疗行业对AI产品的需求正从"概念演示"转向"实战检验"。据梅奥医疗平台最新研究显示，2025年医疗机构将更青睐能直接解决临床痛点的AI解决方案，而非华而不实的演示模型。
当前医院管理层最关注的四大核心诉求包括：缓解医护人员短缺、降低职业倦怠、控制运营成本以及优化患者流程。能够自动生成电子病历的自然语言处理工具、可预测患者流量的分析系统等能直接减轻工作负担的AI应用更受青睐。
值得关注的是，医疗机构对AI产品提出五大硬性指标：
- 真实场景验证：要求提供第三方评估、试点项目数据及同行评审证明
- 系统无缝集成：必须兼容现有电子病历系统，避免增加IT负担
- 算法透明可解释：拒绝"黑箱"算法，需提供清晰的决策逻辑
- 明确投资回报：要求准确测算成本回收周期和人力节省效果
- 符合监管要求：必须满足医疗数据隐私和AI治理规范
梅奥医疗平台强调，成功的AI供应商应深度理解医疗场景的特殊性，将技术融入以人为中心的医疗环境。真正有价值的AI解决方案必须通过临床实效检验，成为医疗工作者值得信赖的合作伙伴，而非短期销售产品。
（本文基于梅奥医疗平台发布的研究报告编译整理）
中文翻译：
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医疗从业者真正需要怎样的AI技术
成功的AI供应商深知，即便最卓越的技术也必须融入高度以人为中心且充满不确定性的医疗环境。
由梅奥诊所平台呈现
当医疗AI市场被过度承诺充斥，决策者们已不再为炫酷演示或抽象潜力所惑。他们如今需要的是经得起压力测试的实用产品——能为医护人员、患者创造价值且提升运营效益的解决方案。为抢占2025年及未来的先机，医疗机构正在积极寻找能落地应用的人工智能方案。
直击痛点的解决方案
医院和医疗系统正聚焦于能解决核心难题的AI方案：人员短缺、医护倦怠、成本攀升及患者流程梗阻。这些现实困境时刻困扰着管理层，而AI必须直面这些挑战。
例如医疗机构迫切需求能减轻医护文书工作的AI工具。相比空谈"提升效率"，能自动生成临床笔记或优化编码的自然语言处理（NLP）方案更受青睐——它们能真正解放时间用于患者照护。同样，能优化人力配置或患者流向的预测分析工具，可直接改善运营流程提升服务效能。
若AI方案无法针对这些关键问题提供切实效益，便难以引发采购方的真正兴趣。
实证有效的成果
AI解决方案需要在模拟真实医疗场景的环境中验证。首要条件是使用高质量、精心整理的现实世界数据来驱动可靠洞察，避免在模型构建过程中产生误导性结果。
医疗机构需要确凿证据证明方案的有效性，包括第三方验证、试点项目、同行评审论文或详实的案例研究。梅奥诊所平台通过临床专家、数据科学家和法规专员组成的独立评估流程，对解决方案的预期用途、价值主张及临床算法性能进行 rigorous 验证，为创新者建立赢得医疗领导者信任所需的公信力。
无缝集成现有系统
面对多重需求，医疗IT领导者对增加复杂度的独立AI工具缺乏耐心。他们需要能与现有系统和 workflow 无缝衔接的解决方案。与主流电子健康记录（EHR）平台的兼容性、稳健的API接口和平滑的数据导入流程已成为基础要求。
需要大量IT资源的定制化集成——尤其是可能造成重复劳动的方案——对资源紧张的医疗机构而言往往是交易终止符。AI方案的介入越无感，其推广阻力就越小。这正是开发者纷纷投向梅奥诊所平台解决方案工作室的原因：该平台提供无缝集成、统一实施、降低风险的专家指导，以及加速医疗端应用的简化流程。
可解释性与透明度
在医疗领域，建立信任至关重要，而透明度和可解释性是构建AI信任的基石。随着AI模型日益复杂，医疗机构意识到仅了解算法预测结果远远不够，更需要理解其决策逻辑。
医疗组织对"黑盒"AI系统愈发警惕，转而要求解决方案能提供清晰易懂的解释，使临床工作者能向同行、患者及监管机构自信阐述决策依据。麦肯锡研究显示：将可解释性嵌入AI战略的机构不仅能降低风险，还能获得更高采纳率、更优绩效和更强财务回报。能解密模型、提供透明性能指标并构建多层信任的解决方案开发者，将在当今医疗市场获得显著优势。
明确投资回报与低实施负担
医疗机构需要确切知道：AI方案多久能实现成本回收？能节省多少工时？可抵消哪些开支？答案越具体、越有实证支持，采纳率就越高。
提供全面培训和响应式支持的开发商更易赢得订单并维持长期客户满意度。
符合法规合规要求
随着AI应用普及，监管审查也日益严格。医疗机构愈发关注新方案是否符合HIPAA法案、数据隐私法以及关于AI治理与偏见管控的新兴指南。
能主动证明合规性的解决方案开发商将带来显著安心优势。透明的数据处理规范、严密的安全措施以及符合伦理的AI原则，正成为不可或缺的卖点。
懂医疗的解决方案开发者
最终，技术并非全部。医疗机构需要真正理解临床护理和医院运营复杂性的合作伙伴。他们寻求能使用医疗专业语言、把握变革管理细节，并理解在紧张预算和高风险环境下提供患者照护的现实困境的伙伴。
成功的AI供应商认识到：即使最顶尖的技术也必须适应高度人性化且常具不确定性的环境。他们的目标是建立长期伙伴关系，而非短期销售。
用AI交付真实价值
要赢得医疗机构的信任与投资，AI开发者必须聚焦于：解决真实问题、验证实际效果、无缝集成系统、保持透明合规。满足这些期望的企业，将有机会共同塑造医疗行业的未来。
本内容由梅奥诊所平台制作，并非《麻省理工科技评论》编辑部撰写。
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What health care providers actually want from AI
Successful AI vendors recognize that even the best technology must fit into a highly human-centered and often unpredictable environment.
Provided byMayo Clinic Platform
In a market flooded with AI promises, health care decision-makers are no longer dazzled by flashy demos or abstract potential. Today, they want pragmatic and pressure-tested products. They want solutions that work for their clinicians, staff, patients, and their bottom line.
To gain traction in 2025 and beyond, health care providers are looking for real-world solutions in AI right now.
Solutions that fix real problems
Hospitals and health systems are looking at AI-enabled solutions that target their most urgent pain points: staffing shortages, clinician burnout, rising costs, and patient bottlenecks. These operational realities keep leadership up at night, and AI solutions must directly address them.
For instance, hospitals and health systems are eager for AI tools that can reduce documentation burden for physicians and nurses. Natural language processing (NLP) solutions that auto-generate clinical notes or streamline coding to free up time for direct patient care are far more compelling pitches than generic efficiency gains. Similarly, predictive analytics that help optimize staffing levels or manage patient flows can directly address operational workflow and improve throughput.
Ultimately, if an AI solution doesn’t target these critical issues and deliver tangible benefits, it’s unlikely to capture serious buyer interest.
Demonstrate real-world results
AI solutions need validation in environments that mirror actual care settings. The first step toward that is to leverage high-quality, well-curated real-world data to drive reliable insights and avoid misleading results when building and refining AI models.
Then, hospitals and health systems need evidence that the solution does what it claims to do, for instance through independent-third party validation, pilot projects, peer-reviewed publications, or documented case studies.
Mayo Clinic Platform offers a rigorous independent process where clinical, data science, and regulatory experts evaluate a solution for intended use, proposed value, and clinical and algorithmic performance, which gives innovators the credibility their solutions need to win the confidence of health-care leaders.
Integration with existing systems
With so many demands, health-care IT leaders have little patience for standalone AI tools that create additional complexity. They want solutions that integrate seamlessly into existing systems and workflows. Compatibility with major electronic health record (EHR) platforms, robust APIs, and smooth data ingestion processes are now baseline requirements.
Custom integrations that require significant IT resources—or worse, create duplicative work—are deal breakers for many organizations already stretched thin. The less disruption an AI solution introduces, the more likely it is to gain traction. This is the reason solution developers are turning to platforms like Mayo Clinic Platform Solutions Studio, a program that provides seamless integration, single implementation, expert guidance to reduce risk, and a simplified process to accelerate solution adoption among healthcare providers.
Explainability and transparency
The importance of trust cannot be overstated when it comes to health care, and transparency and explainability are critical to establishing trust in AI. As AI models grow more complex, health-care providers recognize that simply knowing what an algorithm predicts isn’t enough. They also need to understand how it arrived at that insight.
Health-care organizations are increasingly wary of black-box AI systems whose logic remains opaque. Instead, they’re demanding solutions that offer clear, understandable explanations clinicians can relay confidently to peers, patients, and regulators.
As McKinsey research shows, organizations that embed explainability into their AI strategy not only reduce risk but also see higher adoption, better performance outcomes, and stronger financial returns. Solution developers that can demystify their models, provide transparent performance metrics, and build trust at every level will have a significant edge in today’s health-care market.
Clear ROI and low implementation burden
Hospitals and health systems want to know precisely how quickly an AI solution will pay for itself, how much staff time it will save, and what costs it will help offset. The more specific and evidence-backed the answers, the better rate of adoption.
Solution developers that offer comprehensive training and responsive support are far more likely to win deals and keep customers satisfied over the long term.
Alignment with regulatory and compliance needs
As AI adoption grows, so does regulatory scrutiny. Health-care providers are increasingly focused on ensuring that any new solution complies with HIPAA, data privacy laws, and emerging guidelines around AI governance and bias mitigation.
Solution developers that can proactively demonstrate compliance provide significant peace of mind. Transparent data handling practices, rigorous security measures, and alignment with ethical AI principles are all becoming essential selling points as well.
A solution developer that understands health care
Finally, it’s not just about the technology. Health-care providers want partners that genuinely understand the complexities of clinical care and hospital operations. They’re looking for partners that speak the language of health care, grasp the nuances of change management, and appreciate the realities of delivering patient care under tight margins and high stakes.
Successful AI vendors recognize that even the best technology must fit into a highly human-centered and often unpredictable environment. Long-term partnerships, not short-term sales, are the goal.
Delivering true value with AI
To earn their trust and investment, AI developers must focus relentlessly on solving real problems, demonstrating proven results, integrating without friction, and maintaining transparency and compliance.
Those that deliver on these expectations will have the chance to help shape the future of health care.
This content was produced by Mayo Clinic Platform. It was not written by MIT Technology Review’s editorial staff.
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