医疗保健中人工智能和加密货币的融合

当今技术发展的突破速度促进了一致的突破，尤其是在健康和金融领域。

The technological and industrial revolutions have always worked hand in hand. As steam engines powered the factories of the 18th century, so too does the speed at which technology is evolving today contribute to consistent breakthroughs, especially in the areas of health and finance. As innovators in the tech and healthcare space deftly navigate the complexities of transforming industries, the investment landscape also begins to shift. The obvious similarities between cryptocurrency and AI are opening the door for wild potential in the future and enhancing opportunities for investors.

技术和工业革命一直齐头并进。随着蒸汽机为18世纪的工厂提供动力，今天技术发展的速度也有助于一致的突破，尤其是在健康和金融领域。随着技术和医疗保健领域的创新者巧妙地导航转型行业的复杂性，投资格局也开始发生变化。加密货币与AI之间的明显相似之处正在为未来的野生潜力和增强投资者的机会打开大门。

While artificial intelligence (AI) is not a new discovery, its role in the healthcare space is relatively novel. AI is revolutionizing spaces such as drug discovery and development, data collection and interpretation, and efficiency improvement in all areas of the pharma industry. Still in their relative infancy for optimal use in healthcare applications, AI-driven solutions present nearly limitless possibilities for improving efficiency, reducing failure rates, and accelerating the time-to-market for new drugs.

尽管人工智能（AI）不是一个新发现，但其在医疗领域的作用是相对新颖的。 AI正在彻底改变药物发现和开发，数据收集和解释以及制药行业所有领域的效率的提高。 AI驱动的解决方案仍处于医疗保健应用中最佳使用的相对婴儿期，几乎可以提高效率，降低失败率并加速新药的市场时间。

Integrating AI technology into the pharma sector can be a complex undertaking, presenting several challenges for those on the front lines of AI innovation. Regulatory and compliance guidelines do not always keep pace with discovery, leading to unnecessary hurdles in the rollout of new drugs and processes. For instance, in the U.S., the Food and Drug Administration (FDA) is tasked with approving new drugs and medical devices, a process that can take an average of 10 to 15 years and cost billions of dollars.

将AI技术集成到制药领域可能是一项复杂的事业，对AI创新前线的人们带来了一些挑战。监管和合规指南并不总是与发现的步伐，从而导致新药和过程的不必要障碍。例如，在美国，食品药品监督管理局（FDA）的任务是批准新药和医疗设备，该过程平均需要10到15年，并且成本数十亿美元。

The availability, quality, and security of data may also be a point of contention. Pharmaceutical data is often fragmented across different institutions, such as hospitals, clinics, and research centers, creating accessibility barriers for optimal integration. Additionally, data privacy regulations, such as HIPAA in the U.S. and GDPR in the EU, limit the availability of real-world patient data for AI training. This is where blockchain technology can assist the emergence of effective AI in healthcare. By leveraging federated learning, AI models can be trained across decentralized data sources without compromising privacy.

数据的可用性，质量和安全性也可能是争论的点。药物数据通常在医院，诊所和研究中心等不同机构中分散，从而为最佳整合创造了可及性障碍。此外，数据隐私法规，例如美国的HIPAA和欧盟的GDPR，限制了现实世界中患者数据的可用性进行AI培训。在这里，区块链技术可以帮助出现在医疗保健中有效的AI。通过利用联合学习，可以在不损害隐私的情况下在分散的数据源中培训AI模型。

The cooperation between crypto and AI investors can also be met with skepticism. Many AI models function as "black boxes," making it difficult for regulators and pharma executives to trust their recommendations. A lack of interpretability in AI-driven drug discovery models can create distrust in clinical and regulatory environments. This skepticism is furthered by the fact that investing in AI models comes with a high price tag. Integrating AI into the pharma sector requires substantial investment in infrastructure, talent acquisition, and computational power. Training AI models on biomedical data is expensive and requires access to cloud computing resources, GPUs, and specialized algorithms. As a result, pharma companies may be hesitant to invest in AI if they do not see an immediate financial return on their investment.

加密和人工智能投资者之间的合作也可以引起怀疑。许多AI模型充当“黑匣子”，这使监管机构和制药高管难以信任他们的建议。在AI驱动的药物发现模型中缺乏可解释性会在临床和调节环境中造成不信任。投资AI模型的价格高昂，这一事实进一步加剧了这种怀疑。将AI集成到药物领域需要大量投资于基础设施，人才获取和计算能力。培训有关生物医学数据的AI模型很昂贵，需要访问云计算资源，GPU和专业算法。结果，如果制药公司没有立即获得投资的财务回报，则可能会犹豫要投资AI。

To combat this issue, many are turning to cost-effective AI-as-a-Service (AIaaS) models, which allow pharma companies to use AI solutions without making a massive upfront investment. Public-private partnerships and grant funding can also support AI-driven research in pharma, or leveraging pre-trained AI models and transfer learning techniques to reduce training costs. For instance, the National Institutes of Health (NIH) has launched several initiatives to encourage the development of AI in medicine, such as the BRAIN Initiative, which aims to revolutionize the understanding of the human brain.

为了解决这个问题，许多人正在转向具有成本效益的AI-AS-AS-Service（AIAAS）模型，该模型使制药公司可以使用AI解决方案而无需进行大量的前期投资。公私合作伙伴关系和赠款资金还可以支持Pharma中的AI驱动研究，或利用预先培训的AI模型和转移学习技术来降低培训成本。例如，美国国立卫生研究院（NIH）发起了几项倡议，以鼓励医学中AI的发展，例如《大脑倡议》，旨在彻底改变对人脑的理解。

With AI poised to redefine the healthcare landscape, it is no wonder cryptocurrency investors have a keen interest in the technology. The interwoven nature of blockchain technology and healthcare AI applications for security and efficiency appeals to the burgeoning crypto market. While overcoming the mentioned challenges in AI integration will be critical for market application success, catering to the investment interests of crypto investors will also be significant in the overall adoption of AI in the pharma space.

随着AI准备重新定义医疗保健景观，难怪加密货币投资者对该技术具有浓厚的兴趣。区块链技术和医疗保健AI的安全性和效率应用程序的交织性质吸引了新兴的加密货币市场。尽管克服了AI集成中提到的挑战对于市场应用成功至关重要，但迎合加密投资者的投资利益也将在Pharma领域的AI总体采用中也很重要。

The DEFI (Decentralized Finance) cryptocurrency market has seen a shift in investor interest toward the metaverse and Web3, leading to less attention on the pharmaceutical and biotech fields. However, considering the pressing issues of an aging global population and the rising prevalence of chronic diseases like cancer and Alzheimer's disease, it is surprising that crypto investors have not yet flocked to this area of the market in droves. Still, the potential for cryptocurrency and AI to converge is promising.

Defi（分散的财务）加密货币市场已将投资者的兴趣转移到Metaverse和Web3上，从而减少了对制药和生物技术领域的关注。然而，考虑到全球人口老龄化的紧迫性问题以及癌症和阿尔茨海默氏病等慢性疾病的普遍性上升，令人惊讶的是，加密货币投资者尚未涌向这个市场领域。尽管如此，加密货币和AI的潜力还是有希望的。

Crypto investors are keenly aware of the importance of trustless systems and are likely to be interested in the role of blockchain technology in ensuring data privacy and security. In the context of AI, this is crucial as it relates to the collection, storage, and use of patient data for the development of new drugs and treatments. For instance, a blockchain-based system could be used to create a decentralized network of hospitals and research institutions, enabling the collective training of AI models for drug discovery without compromising the privacy of patient data.

加密投资者敏锐地意识到无信任系统的重要性，并且可能对区块链技术在确保数据隐私和安全性中的作用感兴趣。在AI的背景下，这至关重要，因为它与用于开发新药和治疗的患者数据的收集，存储和使用有关。例如，可以使用基于区块链的系统来建立一个分散的医院和研究机构网络，从而在不损害患者数据的隐私的情况下对AI的药物发现模型进行集体培训。

This type of initiative could be supported by a DAO (Decentralized Autonomous Organization), focusing on advancing cancer research and drug development. The DAO could pool resources from multiple crypto investors to fund promising startups or research projects in the pharma space. For instance, AxonDAO (AXGT) is a DAO focused on cancer research, and W

这种类型的倡议可以得到DAO（分散的自主组织）的支持，重点是推进癌症研究和药物开发。 DAO可以从多个加密投资者那里汇集资源，以资助制药领域的有希望的初创企业或研究项目。例如，Axondao（AXGT）是专注于癌症研究的DAO，W