醫療保健中人工智能和加密貨幣的融合

當今技術發展的突破速度促進了一致的突破，尤其是在健康和金融領域。

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.

技術和工業革命一直齊頭並進。隨著蒸汽機為18世紀的工廠提供動力，今天技術發展的速度也有助於一致的突破，尤其是在健康和金融領域。隨著技術和醫療保健領域的創新者巧妙地導航轉型行業的複雜性，投資格局也開始發生變化。加密貨幣與AI之間的明顯相似之處正在為未來的野生潛力和增強投資者的機會打開大門。

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.

儘管人工智能（AI）不是一個新發現，但其在醫療領域的作用是相對新穎的。 AI正在徹底改變藥物發現和開發，數據收集和解釋以及製藥行業所有領域的效率的提高。 AI驅動的解決方案仍處於醫療保健應用中最佳使用的相對嬰儿期，幾乎可以提高效率，降低失敗率並加速新藥的市場時間。

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.

將AI技術集成到製藥領域可能是一項複雜的事業，對AI創新前線的人們帶來了一些挑戰。監管和合規指南並不總是與發現的步伐，從而導致新藥和過程的不必要障礙。例如，在美國，食品藥品監督管理局（FDA）的任務是批准新藥和醫療設備，該過程平均需要10到15年，並且成本數十億美元。

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.

數據的可用性，質量和安全性也可能是爭論的點。藥物數據通常在醫院，診所和研究中心等不同機構中分散，從而為最佳整合創造了可及性障礙。此外，數據隱私法規，例如美國的HIPAA和歐盟的GDPR，限制了現實世界中患者數據的可用性進行AI培訓。在這裡，區塊鏈技術可以幫助出現在醫療保健中有效的AI。通過利用聯合學習，可以在不損害隱私的情況下在分散的數據源中培訓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.

加密和人工智能投資者之間的合作也可以引起懷疑。許多AI模型充當“黑匣子”，這使監管機構和製藥高管難以信任他們的建議。在AI驅動的藥物發現模型中缺乏可解釋性會在臨床和調節環境中造成不信任。投資AI模型的價格高昂，這一事實進一步加劇了這種懷疑。將AI集成到藥物領域需要大量投資於基礎設施，人才獲取和計算能力。培訓有關生物醫學數據的AI模型很昂貴，需要訪問云計算資源，GPU和專業算法。結果，如果製藥公司沒有立即獲得投資的財務回報，則可能會猶豫要投資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.

為了解決這個問題，許多人正在轉向具有成本效益的AI-AS-AS-Service（AIAAS）模型，該模型使製藥公司可以使用AI解決方案而無需進行大量的前期投資。公私合作夥伴關係和贈款資金還可以支持Pharma中的AI驅動研究，或利用預先培訓的AI模型和轉移學習技術來降低培訓成本。例如，美國國立衛生研究院（NIH）發起了幾項倡議，以鼓勵醫學中AI的發展，例如《大腦倡議》，旨在徹底改變對人腦的理解。

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.

隨著AI準備重新定義醫療保健景觀，難怪加密貨幣投資者對該技術具有濃厚的興趣。區塊鏈技術和醫療保健AI的安全性和效率應用程序的交織性質吸引了新興的加密貨幣市場。儘管克服了AI集成中提到的挑戰對於市場應用成功至關重要，但迎合加密投資者的投資利益也將在Pharma領域的AI總體採用中也很重要。

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（分散的財務）加密貨幣市場已將投資者的興趣轉移到Metaverse和Web3上，從而減少了對製藥和生物技術領域的關注。然而，考慮到全球人口老齡化的緊迫性問題以及癌症和阿爾茨海默氏病等慢性疾病的普遍性上升，令人驚訝的是，加密貨幣投資者尚未湧向這個市場領域。儘管如此，加密貨幣和AI的潛力還是有希望的。

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.

加密投資者敏銳地意識到無信任系統的重要性，並且可能對區塊鏈技術在確保數據隱私和安全性中的作用感興趣。在AI的背景下，這至關重要，因為它與用於開發新藥和治療的患者數據的收集，存儲和使用有關。例如，可以使用基於區塊鏈的系統來建立一個分散的醫院和研究機構網絡，從而在不損害患者數據的隱私的情況下對AI的藥物發現模型進行集體培訓。

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

這種類型的倡議可以得到DAO（分散的自主組織）的支持，重點是推進癌症研究和藥物開發。 DAO可以從多個加密投資者那裡匯集資源，以資助製藥領域的有希望的初創企業或研究項目。例如，Axondao（AXGT）是專注於癌症研究的DAO，W