开发者Hailey @IndieDevHailey - Twitter Profile

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2 days ago

别死太早！未来10年，人类平均寿命破100，可能真不是一句玩笑。🔥 刚刷到 FutureLivingLab 的讨论，再结合 ICCV 2025 重磅论文《Medical World Model (MeWM)》，有点震撼。这篇论文让我看到的，不只是医疗领域的新进展。世界模型，已经开始进入真实系统。在肝癌介入手术（TACE）中，AI可以基于术前CT，在虚拟病人体内模拟： - 不同栓塞材料如何影响肿瘤演化 - 不同药物组合几个月后的变化 - 哪条治疗路径长期效果最好换句话说，先在模型里把未来跑一遍，再决定现实怎么发生。效果已经可量化： - 辅助医生决策 F1-score 提升约13% - 合成术后影像接近真实 - 已经具备一定“第二阅片人”的能力这是医疗领域第一次出现“可回放未来”的能力。在 FutureLivingLab 的讨论中，这件事被放到了更大的视角下：医疗或许只是最先落地的场景之一。随着世界模型不断成熟，它有机会成为未来决策系统的重要组成部分。同时，其他方向也在加速推进： - senolytics（清除衰老细胞） - 器官再生 / 生物打印 - 个体化健康干预放在一起看，一个明显的趋势正在出现：衰老正在从一个无法干预的自然过程，逐渐变成一个能够被理解、预测和干预的问题。未来，如果世界模型持续进入各行各业，最先被重构的战场可能是： - 医疗、金融、能源、城市治理等依赖未来推演的领域但真正值得关注的是：谁会率先拥有“可模拟未来的决策能力”。 ------------------------------------------------- FutureLivingLab 是阿里旗下实验室，聚焦下一代 AI 系统与现实应用，探索多模态、Agent 与 AI 原生应用在真实世界中的落地，由基础设施驱动。 HappyHorse、HappyOyster 都是它们的产品。关注FutureLivingLab，第一时间了解世界模型前沿动态。

IndieDevHailey's tweet photo. 别死太早！未来10年，人类平均寿命破100，可能真不是一句玩笑。🔥

刚刷到 FutureLivingLab 的讨论，再结合 ICCV 2025 重磅论文《Medical World Model (MeWM)》，
有点震撼。

这篇论文让我看到的，不只是医疗领域的新进展。

世界模型，已经开始进入真实系统。

在肝癌介入手术（TACE）中，AI可以基于术前CT，在虚拟病人体内模拟：

- 不同栓塞材料如何影响肿瘤演化
- 不同药物组合几个月后的变化
- 哪条治疗路径长期效果最好

换句话说，先在模型里把未来跑一遍，再决定现实怎么发生。

效果已经可量化：

- 辅助医生决策 F1-score 提升约13%
- 合成术后影像接近真实
- 已经具备一定“第二阅片人”的能力

这是医疗领域第一次出现“可回放未来”的能力。

在 FutureLivingLab 的讨论中，这件事被放到了更大的视角下：

医疗或许只是最先落地的场景之一。

随着世界模型不断成熟，它有机会成为未来决策系统的重要组成部分。

同时，其他方向也在加速推进：

- senolytics（清除衰老细胞）
- 器官再生 / 生物打印
- 个体化健康干预

放在一起看，一个明显的趋势正在出现：

衰老正在从一个无法干预的自然过程，逐渐变成一个能够被理解、预测和干预的问题。

未来，如果世界模型持续进入各行各业，最先被重构的战场可能是：

- 医疗、金融、能源、城市治理等依赖未来推演的领域

但真正值得关注的是：

谁会率先拥有“可模拟未来的决策能力”。

-------------------------------------------------

FutureLivingLab 是阿里旗下实验室，
聚焦下一代 AI 系统与现实应用，探索多模态、Agent 与 AI 原生应用在真实世界中的落地，
由基础设施驱动。 HappyHorse、HappyOyster 都是它们的产品。

关注FutureLivingLab，第一时间了解世界模型前沿动态。

FutureLivingLab @FutureLab2025

2 days ago

While showbiz bickers over AI video continuity glitches and educators remain stuck debating AI-generated PPTs, World Models are quietly disrupting non-tech sectors, igniting a radical paradigm shift in clinical medicine and surgical simulation. Why healthcare and not Hollywood? Because Hollywood demands visual perfection, but healthcare mandates absolute physical causality. Traditional medical AI could only act as a static periscope—pinpointing a lesion on an existing scan. Yet disease is inherently dynamic. When a physician prescribes a treatment, they historically lacked a patient-specific, long-term window into the exact downstream changes after the patient ingests the drug. Recent breakthroughs showcased at elite computing summits like ICCV have elevated medical AI from passive visual recognition to a predictive, generative "World Simulator" tailored for prognosis and treatment optimization. In validated clinical applications, this technology leverages potent counterfactual reasoning. Take transarterial chemoembolization (TACE) for liver cancer and advanced radiotherapy as prime examples: before finalizing an intervention, a Medical World Model (MeWM) ingests a patient’s current CT imagery to simulate months of dynamic disease progression within its latent space. It cross-aligns multimodal parameters to synthesize high-fidelity visual representations of post-treatment tumor trajectories. Simultaneously, its inverse dynamics model quantifies how varying embolic agents or drug cocktails shift long-term survival curves. Empirically, this "future-simulation" paradigm has propelled clinical decision success rates (F1-score) by 13%, cementing its role as an indispensable AI co-pilot. Today, multimodal medical models are rapidly embedding into hospital HIS/EMR nervous systems, as specialized prognosis simulators push past theoretical boundaries into raw performance validation. The ultimate utility of a World Model isn't coding text or animating fantasy; it is evolving into a rigorous, low-cost simulation infrastructure—serving as a high-stakes safeguard for human decision-making. 【The Grand Forecast】 The successful clinical deployment of Medical World Models proves their unique capacity to "simulate future outcomes before executing current actions." This technical paradigm—trading pure aesthetic appeal for rigid physical and biological causality—is sprawling beyond tech ecosystems at a breakneck speed. Stripping away healthcare, autonomous driving, and media entertainment, which trial-and-error heavy traditional industry do you predict World Models will infiltrate and disrupt next? Will it be macro-climate disaster modeling in modern agriculture, dynamic supply-chain evolution in urban planning, extreme stress-testing in deep-sea aerospace engineering, or an entirely unmapped frontier? Drop your sharpest thesis and reasoning in the comments below. Let’s chart the hidden industrial landscape of the next generation of World Models!

FutureLab2025's tweet photo. While showbiz bickers over AI video continuity glitches and educators remain stuck debating AI-generated PPTs, World Models are quietly disrupting non-tech sectors, igniting a radical paradigm shift in clinical medicine and surgical simulation.

Why healthcare and not Hollywood?

Because Hollywood demands visual perfection, but healthcare mandates absolute physical causality.

Traditional medical AI could only act as a static periscope—pinpointing a lesion on an existing scan.

Yet disease is inherently dynamic. When a physician prescribes a treatment, they historically lacked a patient-specific, long-term window into the exact downstream changes after the patient ingests the drug.

Recent breakthroughs showcased at elite computing summits like ICCV have elevated medical AI from passive visual recognition to a predictive, generative "World Simulator" tailored for prognosis and treatment optimization.

In validated clinical applications, this technology leverages potent counterfactual reasoning.

Take transarterial chemoembolization (TACE) for liver cancer and advanced radiotherapy as prime examples: before finalizing an intervention, a Medical World Model (MeWM) ingests a patient’s current CT imagery to simulate months of dynamic disease progression within its latent space.

It cross-aligns multimodal parameters to synthesize high-fidelity visual representations of post-treatment tumor trajectories. Simultaneously, its inverse dynamics model quantifies how varying embolic agents or drug cocktails shift long-term survival curves. Empirically, this "future-simulation" paradigm has propelled clinical decision success rates (F1-score) by 13%, cementing its role as an indispensable AI co-pilot.

Today, multimodal medical models are rapidly embedding into hospital HIS/EMR nervous systems, as specialized prognosis simulators push past theoretical boundaries into raw performance validation.

The ultimate utility of a World Model isn't coding text or animating fantasy; it is evolving into a rigorous, low-cost simulation infrastructure—serving as a high-stakes safeguard for human decision-making.

【The Grand Forecast】
The successful clinical deployment of Medical World Models proves their unique capacity to "simulate future outcomes before executing current actions." This technical paradigm—trading pure aesthetic appeal for rigid physical and biological causality—is sprawling beyond tech ecosystems at a breakneck speed.

Stripping away healthcare, autonomous driving, and media entertainment, which trial-and-error heavy traditional industry do you predict World Models will infiltrate and disrupt next?

Will it be macro-climate disaster modeling in modern agriculture, dynamic supply-chain evolution in urban planning, extreme stress-testing in deep-sea aerospace engineering, or an entirely unmapped frontier?

Drop your sharpest thesis and reasoning in the comments below. Let’s chart the hidden industrial landscape of the next generation of World Models!

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开发者Hailey

@IndieDevHailey

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