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精準醫療市場中的人工智慧，2028-全球產業規模、佔有率、趨勢、機會和預測，2018-2028 按技術、組件、治療應用、地區、競爭細分

Artificial Intelligence In Precision Medicine Market, 2028- Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Technology, By Component, By Therapeutic Application, By Region, By Competition
出版日期: | 出版商: TechSci Research | 英文 190 Pages | 商品交期: 2-3個工作天內

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簡介目錄

到 2022 年，全球人工智慧精準醫療市場價值將達到 12.4 億美元，預計到 2028 年，年複合成長率將達到 21.13%。在不斷發展的醫療保健領域，強大的融合正在發生。人工智慧（AI）和精準醫療之間的關係。這種突破性的協同作用有可能改變醫療治療的發展、提供和個人化的方式。精準醫療市場中的全球人工智慧處於這項典範轉移的最前沿，讓我們得以一窺醫療保健創新的未來。精準醫學的特點是根據每位患者的個別特徵制定醫療治療和介入措施，近年來獲得了相當大的關注。這種方法承認患者之間固有的多樣性，並考慮遺傳、環境和生活方式等因素。同時，機器學習和深度學習等人工智慧技術在分析大量資料和提取可行見解方面表現出了卓越的能力。這兩個領域的融合為最佳化診斷、治療選擇和患者結果帶來了巨大的希望。

主要市場促進因素

市場概況
預測期 2024-2028
2022 年市場規模 12.4億美元
2028 年市場規模 39.2億美元
2023-2028 年年複合成長率 21.13%
成長最快的細分市場 腫瘤學
最大的市場 北美洲

慢性病盛行率上升正在推動全球人工智慧在精準醫療市場的發展

慢性病通常稱為非傳染性疾病 (NCD)，涵蓋多種健康狀況，例如心血管疾病、糖尿病、癌症和呼吸道疾病。它們的特點是持續時間長、進展緩慢，並且需要持續的醫療護理和管理。根據世界衛生組織 (WHO) 的數據，全球死亡人數中近 71% 是由慢性病造成的，其中高達 85% 的死亡發生在低收入和中等收入國家。慢性病對社會經濟的影響是深遠的，它給醫療保健系統帶來壓力，降低勞動生產力，並降低個人及其家庭的生活品質。人工智慧，特別是機器學習和深度學習技術，已被證明是醫療保健產業的變革力量。人工智慧具有處理和分析海量資料集、識別複雜模式並產生預測模型的能力。當應用於精準醫療時，人工智慧可以挖掘基因組成、疾病易感性和治療結果之間複雜的關係，從而實現更準確的診斷和個人化的治療介入。人工智慧在精準醫學中的重要應用之一是基因體學研究。人工智慧演算法可以快速分析患者的遺傳訊息，並識別與某些疾病相關的特定突變或生物標記。這些資訊有助於臨床醫生就治療策略做出明智的決定，使他們能夠選擇更有可能有效的藥物並最大限度地減少不良反應。人工智慧驅動的工具也正在徹底改變醫學影像分析。這些工具可以快速解讀 X 光、MRI 和 CT 掃描等影像，有助於早期發現和診斷癌症、心臟病和神經退化性疾病等各種疾病。此外，人工智慧驅動的預測模型可以預測疾病進展，使醫生能夠主動干涉並相應地制定治療計劃。人工智慧與精準醫療的融合帶來了市場的快速擴張。根據市場研究報告，全球人工智慧精準醫療市場預計在未來幾年將大幅成長。研究和開發資金的增加、人工智慧和醫療保健公司之間的合作夥伴關係不斷加強以及對個人化治療的需求不斷增加等因素正在推動這一趨勢。

隨著科技的不斷進步，人工智慧在精準醫療中的應用可能會進一步擴大。電子健康記錄、穿戴式裝置和即時監測的整合將為人工智慧演算法提供連續的資料流進行分析，從而能夠及時干涉和調整治療計劃。此外，人工智慧可以幫助發現新的藥物標靶和開發創新的治療干涉措施，開創精準醫療的新時代。

藥物發現和開發的激增推動了全球人工智慧在精準醫學領域的成長

在藥物發現和開發領域一直是一個複雜且耗時的過程。研究人員花費數年時間來識別潛在的候選藥物，測試它們的安全性和有效性，然後經過漫長的法規核准過程，然後最終到達患者手中。然而，最近的技術進步，特別是人工智慧 (AI) 領域的進步，正在徹底改變藥物的發現和開發方式。這在精準醫療領域不斷成長的全球人工智慧市場中尤其明顯。精準醫療，也稱為個人化醫療，是一種創新的醫療保健方法，考慮到每個人基因、環境和生活方式的個體差異。透過根據每位患者的獨特特徵制定醫療治療和干涉措施，精準醫療旨在實現更好的結果，減少不良反應，並最終改善患者護理。人工智慧在推動精準醫療市場方面發揮著重要作用。人工智慧演算法可以分析大量患者資料，包括遺傳資訊、病史和生活方式因素，以識別潛在的藥物標靶並預測患者對不同治療的反應。這加速了藥物發現過程，使其更快、更有效率。

人工智慧產生重大影響的一個領域是識別潛在的候選藥物。傳統的藥物發現方法通常涉及篩選大型化合物庫，這可能既耗時又昂貴。另一方面，人工智慧演算法可以快速分析大量資料，識別潛在的藥物標靶並預測哪些化合物可能具有治療效果。此外，人工智慧也被用來預測患者對不同治療的反應。透過分析患者資料，人工智慧演算法可以識別生物標記，幫助預測哪些患者更有可能對特定治療產生反應，從而實現更有針對性和個人化的干涉措施。

這一成長的主要驅動力是可用於分析的資料量不斷增加。基因組定序技術的進步帶來了遺傳資料的爆炸性成長，為研究人員提供了有關疾病根本原因的寶貴見解。人工智慧演算法可以篩選這些資料，識別潛在的藥物標靶並預測患者的反應。此外，製藥公司和科技公司之間的合作正在進一步推動人工智慧在精準醫療市場的成長。這些合作夥伴關係正在推動創新人工智慧驅動工具和平台的開發，從而加速藥物發現和開發流程。

主要市場挑戰

數據品質和可近性對市場擴張構成重大障礙

人工智慧驅動的精準醫療市場面臨的主要挑戰之一是需要高品質、多樣化和全面的醫療資料。人工智慧演算法嚴重依賴大型資料集來做出準確的預測和建議。然而，醫療保健資料通常分散在各種來源中，包括電子健康記錄、基因組資料、穿戴式裝置等。整合這些不同的資料來源，同時確保其準確性和安全性仍然是一項艱鉅的挑戰。

資料隱私和安全

由於精準醫療中的人工智慧應用需要存取敏感的患者資料，因此對資料隱私和安​​全性的擔憂已成為人們關注的焦點。平衡人工智慧驅動的洞察力的好處與患者機密性和資料保護法規是一個重大障礙。在用於研究目的的資料共享和維護患者信任之間取得適當的平衡對於市場的永續成長至關重要。

缺乏標準化

將人工智慧融入精準醫療涉及整合多個來源的複雜資料以及開發分析演算法。醫療保健系統和機構之間缺乏標準化資料格式和互通性標準，對無縫資料共享和協作構成了巨大障礙。努力建立通用資料標準對於促進資訊交流和促進創新至關重要。

演算法偏差和可解釋性

人工智慧演算法可能會無意中使訓練資料中存在的偏見永久化，從而導致醫療結果的差異。在精準醫學中，有偏見的演算法可能會導致不準確的診斷或治療，特別是對於代表性不足的人群。此外，一些人工智慧模型的「黑盒子」性質為理解如何做出決策帶來了挑戰，限制了它們的臨床接受度。努力建立透明且可解釋的人工智慧模型對於在醫療保健提供者和患者之間建立信任至關重要。

臨床驗證和監管

為了讓人工智慧驅動的精準醫療解決方案廣泛接受，它們必須經過嚴格的臨床驗證，以證明其安全性、有效性和可靠性。基於人工智慧的醫療產品獲得監管部門批准是一個複雜的過程，需要遵循不斷發展的指導方針並展示現實世界的影響。將人工智慧精準醫療技術推向市場時，平衡創新與病患安全仍然是一個重大障礙。

融入臨床工作流程

將人工智慧解決方案實施到現有的臨床工作流程中可能具有挑戰性。醫療保健專業人員已經被資訊淹沒，在不破壞既定流程的情況下無縫整合新技術至關重要。提供使用者友善的介面、確保最小的干擾並展示切實的好處對於鼓勵採用至關重要。

成本和資源限制

雖然人工智慧在精準醫療領域的潛在長期效益是巨大的，但技術實施和培訓所需的初始投資可能也很大。許多醫療機構，尤其是在資源有限的環境中，可能會發現為人工智慧計畫分配資金具有挑戰性。展示經濟價值和投資回報對於克服這些與成本相關的障礙至關重要。

主要市場趨勢

技術進步

傳統上，醫療和介入措施遵循一刀切的方法，但由於基因組成、生活方式和環境因素的個體差異，往往會導致效果不佳。另一方面，精準醫學透過根據每位患者的特定特徵制定醫療決策和介入措施，擁抱每位患者的獨特性。基因組學、分子生物學和個人化診斷的進步使這種方法成為可能。分析大量患者資料（包括遺傳資訊、病史和生活方式因素）的複雜性需要能夠有效篩選這些資料並提取有意義的見解的工具。這就是人工智慧介入的地方，它提供理解複雜的患者資訊網路所需的運算能力和演算法智慧。精準醫學中的人工智慧涉及利用機器學習演算法和深度學習技術來識別大型資料集中的模式、相關性和關聯性。這些模式可能與疾病風險、治療反應、藥物交互作用等有關。人工智慧演算法接觸的資料越多，它們就越能辨識出可能逃避人類分析的微妙連結。

醫療記錄的數位化以及穿戴式裝置和醫療感測器的爆炸性成長導致患者資料達到前所未有的水平。人工智慧演算法在資料上蓬勃發展，這些豐富的資訊使它們能夠做出更準確的預測和建議。基因組學領域在破解人類基因組和了解疾病的遺傳基礎方面取得了顯著進展。人工智慧可以幫助解釋大量的遺傳資訊並將其與臨床結果聯繫起來。人工智慧驅動的模擬和虛擬藥物篩選可以加快藥物發現和開發，從而創建與患者獨特基因譜相匹配的標靶療法。人工智慧技術可以加速醫療資料分析，從而加快診斷速度、最佳化治療計劃並縮短住院時間。這不僅可以改善患者的治療效果，還可以降低醫療成本。

細分市場洞察

技術洞察

基於該技術，深度學習領域將在 2022 年成為全球精準醫療人工智慧市場的主導者。這可以歸因於精準醫療旨在根據個別特徵量身定做醫療和干涉措施，從而使更有效和個性化的護理。深度學習是機器學習的子集，已被證明非常適合解決該領域的複雜問題。精準醫學涉及分析大量異質資料，包括基因組學、蛋白質組學、醫學影像、電子健康記錄等。深度學習模型，特別是神經網路，擅長從如此多樣化和高維度的資料類型中學習複雜的模式和表示。深度學習的關鍵優勢之一是它能夠從原始資料中自動提取相關特徵。在精準醫學中，可能無法明確定義有意義的特徵，深度學習模型可以識別有助於疾病診斷、預後和治療的微妙關係和特徵。許多疾病都有複雜的潛在機制，其運作的複雜程度各不相同。深度學習的分層架構具有多層互連的神經元，可以捕捉這些複雜的模式和關係，使其非常適合對複雜的疾病過程進行建模。

組件洞察

預計軟體領域將在預測期內經歷快速成長。精準醫學在很大程度上依賴分析大量患者資料，包括基因組、臨床和生活方式資訊。人工智慧演算法能夠處理這些複雜的資料集並提取有意義的見解。軟體應用程式支援這些演算法的開發和部署，使醫療保健專業人員能夠以手動方式無法實現的規模和複雜性分析患者資料。機器學習和深度學習模型等人工智慧演算法對於理解精準醫學資料至關重要。這些演算法需要大量標記資料進行訓練、微調和驗證。軟體平台為研究人員和資料科學家提供了有效設計、開發和訓練這些人工智慧模型的基礎設施。

區域洞察

2022年，北美成為全球精準醫療人工智慧市場的主導者，以價值計算，佔據最大的市場佔有率。北美擁有先進的醫療基礎設施，包括完善的電子健康記錄 (EHR) 系統，該系統提供了大量的患者資料，可用於訓練和驗證精準醫療的人工智慧演算法。獲取高品質資料對於開發準確的人工智慧模型至關重要。該地區為人工智慧新創公司和精準醫療領域的公司提供了大量投資和資金。創投公司和投資者被人工智慧與醫療保健相結合、推動市場創新和成長的潛力所吸引。北美，特別是美國，在人工智慧和醫學領域擁有強大的研究和創新生態系統。該地區領先的研究型大學、醫療機構和科技公司一直處於開發精準醫療應用人工智慧技術的前沿。北美有著醫療保健和技術領域合作的傳統。此次合作促進了人工智慧解決方案融入醫療實踐。醫院、研究機構和科技公司之間的合作加速了人工智慧驅動的精準醫療工具的開發和採用。

目錄

第 1 章：產品概述

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：全球人工智慧精準醫療市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按技術（軟體解決方案、硬體、服務）
    • 依癌症類型（乳癌、肺癌、攝護腺癌、大腸癌、腦腫瘤、其他）
    • 按最終使用者（醫院、外科中心和醫療機構、其他）
    • 按地區
    • 按公司分類 (2022)
  • 市場地圖

第 6 章：北美人工智慧精準醫療市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依技術
    • 按癌症類型
    • 按最終用戶
    • 按形式
    • 按配銷通路
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 7 章：歐洲人工智慧精準醫療市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依技術
    • 按癌症類型
    • 按最終用戶
  • 歐洲：國家分析
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙

第 8 章：亞太地區人工智慧精準醫療市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依技術
    • 按癌症類型
    • 按最終用戶
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

第 9 章：南美洲人工智慧在精準醫療市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依技術
    • 按癌症類型
    • 按最終用戶
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲精準醫療中的人工智慧市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依技術
    • 按癌症類型
    • 按最終用戶
  • MEA：國家分析
    • 南非 精準醫療中的人工智慧
    • 沙烏地阿拉伯人工智慧在精準醫學的應用
    • 阿拉伯聯合大公國人工智慧在精準醫療的應用

第 11 章：市場動態

第 12 章：市場趨勢與發展

第 13 章：精準醫療市場中的全球人工智慧：SWOT 分析

第14章：競爭格局

  • 商業概覽
  • 癌症類型產品
  • 最近的發展
  • 主要人員
  • SWOT分析
    • Medial EarlySign
    • Cancer Center.ai
    • Microsoft Corporation
    • Flatiron Health
    • Path AI
    • Therapixel
    • Tempus Labs, Inc.
    • Paige AI, Inc.
    • Kheiron Medical Technologies Limited
    • SkinVision

第 15 章：策略建議

第 16 章：關於我們與免責聲明

簡介目錄
Product Code: 16237

Global Artificial Intelligence In Precision Medicine Market has valued at USD 1.24 billion in 2022 and is anticipated to project impressive growth in the forecast period with a CAGR of 21.13% through 2028. In the ever-evolving landscape of healthcare, a powerful convergence is taking place between artificial intelligence (AI) and precision medicine. This groundbreaking synergy has the potential to transform the way medical treatments are developed, delivered, and personalized. The Global Artificial Intelligence in Precision Medicine Market is at the forefront of this paradigm shift, offering a glimpse into the future of healthcare innovation. Precision medicine, characterized by tailoring medical treatments and interventions to the individual characteristics of each patient, has gained considerable traction in recent years. This approach acknowledges the inherent diversity among patients, taking into account factors such as genetics, environment, and lifestyle. Meanwhile, AI technologies like machine learning and deep learning have demonstrated remarkable capabilities in analyzing vast amounts of data and extracting actionable insights. The amalgamation of these two domains holds immense promise for optimizing diagnosis, treatment selection, and patient outcomes.

Traditional one-size-fits-all medical approaches are gradually making way for personalized treatments. Patients and healthcare providers alike are recognizing the potential of AI to unlock the intricacies of individual health profiles, enabling tailored therapies. The decreasing cost of genomic sequencing has led to an explosion of genetic data. AI algorithms can swiftly sift through this information, identifying genetic markers associated with diseases, and paving the way for targeted interventions. The digitization of healthcare records and the proliferation of wearable devices have generated an unprecedented volume of patient data. AI can aggregate, analyse, and integrate these diverse data sources, yielding comprehensive insights that were previously unattainable. AI is revolutionizing the drug discovery process by predicting potential drug candidates, simulating drug interactions, and expediting preclinical testing. This not only reduces costs but also accelerates the delivery of innovative therapies to market.

Key Market Drivers

Market Overview
Forecast Period2024-2028
Market Size 2022USD 1.24 Billion
Market Size 2028USD 3.92 Billion
CAGR 2023-202821.13%
Fastest Growing SegmentOncology
Largest MarketNorth America

Rising Prevalence of Chronic Diseases is Driving the Global Artificial Intelligence In Precision Medicine Market

Chronic diseases, often referred to as non-communicable diseases (NCDs), encompass a wide range of health conditions such as cardiovascular diseases, diabetes, cancer, and respiratory illnesses. They are characterized by their prolonged duration, slow progression, and the requirement for ongoing medical attention and management. According to the World Health Organization (WHO), chronic diseases are responsible for almost 71% of all global deaths, with a staggering 85% of these deaths occurring in low- and middle-income countries. The socioeconomic impact of chronic diseases is profound, straining healthcare systems, reducing workforce productivity, and diminishing the quality of life for individuals and their families. Artificial Intelligence, specifically machine learning and deep learning techniques, has proven to be a transformative force in the healthcare industry. AI has the ability to process and analyze massive datasets, recognize complex patterns, and generate predictive models. When applied to precision medicine, AI can mine intricate relationships between genetic makeup, disease susceptibility, and treatment outcomes, leading to more accurate diagnoses and personalized therapeutic interventions. One of the significant applications of AI in precision medicine is in genomics research. AI algorithms can swiftly analyze a patient's genetic information and identify specific mutations or biomarkers associated with certain diseases. This information aids clinicians in making informed decisions about treatment strategies, enabling them to select medications that are more likely to be effective and minimize adverse effects. AI-powered tools are also revolutionizing medical imaging analysis. These tools can rapidly interpret images such as X-rays, MRIs, and CT scans, aiding in the early detection and diagnosis of various conditions like cancer, heart disease, and neurodegenerative disorders. Additionally, AI-driven predictive models can forecast disease progression, allowing physicians to intervene proactively and tailor treatment plans accordingly. The convergence of AI and precision medicine has resulted in a rapidly expanding market. According to market research reports, the Global Artificial Intelligence in Precision Medicine Market is projected to experience substantial growth over the coming years. Factors such as increased funding for research and development, growing partnerships between AI and healthcare companies, and the escalating demand for personalized treatments are driving this trend.

As technology continues to advance, the applications of AI in precision medicine will likely expand further. Integration of electronic health records, wearable devices, and real-time monitoring will provide a continuous stream of data for AI algorithms to analyze, enabling timely interventions and adjustments to treatment plans. Moreover, AI can aid in the discovery of novel drug targets and the development of innovative therapeutic interventions, ushering in a new era of precision medicine.

The Surge of Drug Discovery and Development Fuels Growth in Global Artificial Intelligence in Precision Medicine

In The field of drug discovery and development has always been a complex and time-consuming process. Researchers spend years identifying potential drug candidates, testing them for safety and efficacy, and then going through a lengthy regulatory approval process before they can finally reach patients. However, recent advancements in technology, particularly in the field of artificial intelligence (AI), are revolutionizing the way drugs are discovered and developed. This is particularly evident in the rising global market for AI in precision medicine. Precision medicine, also known as personalized medicine, is an innovative approach to healthcare that takes into account individual variability in genes, environment, and lifestyle for each person. By tailoring medical treatment and interventions to the unique characteristics of each patient, precision medicine aims to achieve better outcomes, reduce adverse effects, and ultimately improve patient care. Artificial intelligence has found a significant role in driving the precision medicine market. AI algorithms can analyze vast amounts of patient data, including genetic information, medical history, and lifestyle factors, to identify potential drug targets and predict how patients will respond to different treatments. This accelerates the drug discovery process, making it faster and more efficient.

One area where AI is making a considerable impact is in identifying potential drug candidates. Traditional methods of drug discovery often involve screening large libraries of chemical compounds, which can be time-consuming and expensive. AI algorithms, on the other hand, can quickly analyze vast amounts of data to identify potential drug targets and predict which compounds are likely to have a therapeutic effect. Additionally, AI is also being used to predict how patients will respond to different treatments. By analyzing patient data, AI algorithms can identify biomarkers that can help predict which patients are more likely to respond to a specific treatment, allowing for more targeted and personalized interventions.

One major driver of this growth is the increasing amount of data available for analysis. Advances in genomic sequencing technology have led to an explosion of genetic data, providing researchers with valuable insights into the underlying causes of diseases. AI algorithms can sift through this data to identify potential drug targets and predict patient responses.In addition, collaborations between pharmaceutical companies and technology firms are further propelling the growth of the AI in precision medicine market. These partnerships are enabling the development of innovative AI-driven tools and platforms that can accelerate drug discovery and development processes.

Key Market Challenges

Data Quality and Accessibility Poses a Significant Obstacle To Market Expansion

One of the primary challenges facing the AI-driven precision medicine market is the need for high-quality, diverse, and comprehensive healthcare data. AI algorithms rely heavily on large datasets to make accurate predictions and recommendations. However, healthcare data is often fragmented across various sources, including electronic health records, genomic data, wearable devices, and more. Integrating these disparate data sources while ensuring their accuracy and security remains a formidable challenge.

Data Privacy and Security

As AI applications in precision medicine require access to sensitive patient data, concerns about data privacy and security have come to the forefront. Balancing the benefits of AI-driven insights with patient confidentiality and data protection regulations is a significant hurdle. Striking the right balance between data sharing for research purposes and maintaining patient trust is crucial for the sustainable growth of the market.

Lack of Standardization

Incorporating AI into precision medicine involves the integration of complex data from multiple sources and the development of algorithms for analysis. The lack of standardized data formats and interoperability standards across healthcare systems and institutions poses a substantial barrier to seamless data sharing and collaboration. Efforts to establish common data standards are essential to facilitate the exchange of information and foster innovation.

Algorithm Bias and Interpretability

AI algorithms can inadvertently perpetuate biases present in training data, leading to disparities in healthcare outcomes. In precision medicine, biased algorithms could result in inaccurate diagnoses or treatments, particularly for underrepresented populations. Additionally, the "black box" nature of some AI models poses challenges in understanding how decisions are reached, limiting their clinical acceptance. Striving for transparent and interpretable AI models is crucial for building trust among healthcare providers and patients.

Clinical Validation and Regulation

For AI-driven precision medicine solutions to gain widespread acceptance, they must undergo rigorous clinical validation to demonstrate their safety, efficacy, and reliability. Achieving regulatory approval for AI-based medical products is a complex process that requires navigating evolving guidelines and demonstrating real-world impact. Balancing innovation with patient safety remains a significant hurdle in bringing AI-enabled precision medicine technologies to market.

Integration into Clinical Workflow

Implementing AI solutions into the existing clinical workflow can be challenging. Healthcare professionals are already inundated with information, and integrating new technologies seamlessly without disrupting established processes is crucial. Providing user-friendly interfaces, ensuring minimal disruption, and demonstrating tangible benefits are essential to encourage adoption.

Cost and Resource Constraints

While the potential long-term benefits of AI in precision medicine are substantial, the initial investment required for technology implementation and training can be significant. Many healthcare institutions, especially in resource-constrained environments, might find it challenging to allocate funds for AI initiatives. Demonstrating the economic value and return on investment is crucial to overcoming these cost-related barriers.

Key Market Trends

Technological Advancements

Traditionally, medical treatments and interventions have followed a one-size-fits-all approach, often resulting in suboptimal outcomes due to individual variations in genetic makeup, lifestyle, and environmental factors. Precision medicine, on the other hand, embraces the uniqueness of each patient by tailoring medical decisions and interventions based on their specific characteristics. This approach has been made possible by advances in genomics, molecular biology, and personalized diagnostics. The complexity of analyzing vast amounts of patient data, including genetic information, medical histories, and lifestyle factors, requires tools that can sift through this data efficiently and extract meaningful insights. This is where artificial intelligence steps in, offering the computational power and algorithmic intelligence needed to make sense of the intricate web of patient information. AI in precision medicine involves the utilization of machine learning algorithms and deep learning techniques to identify patterns, correlations, and associations within large datasets. These patterns could relate to disease risk, treatment response, drug interactions, and more. The more data AI algorithms are exposed to, the better they become at identifying subtle connections that might elude human analysis.

The digitalization of healthcare records, along with the explosion of wearable devices and medical sensors, has led to an unprecedented volume of patient data. AI algorithms thrive on data, and this wealth of information enables them to make more accurate predictions and recommendations. The field of genomics has seen remarkable progress in deciphering the human genome and understanding the genetic basis of diseases. AI can aid in interpreting this vast genetic information and linking it to clinical outcomes. AI-driven simulations and virtual drug screening can expedite drug discovery and development, allowing for the creation of targeted therapies that are aligned with a patient's unique genetic profile. AI technologies can accelerate the analysis of medical data, leading to quicker diagnoses, optimized treatment plans, and shorter hospital stays. This not only improves patient outcomes but also reduces healthcare costs.

Segmental Insights

Technology Insights

Based on the Technology, the Deep Learning segment emerged as the dominant player in the global market for Artificial Intelligence In Precision Medicine in 2022. This can be attributed to the fact that precision medicine aims to tailor medical treatment and interventions to individual characteristics, allowing for more effective and personalized care. Deep Learning, a subset of machine learning, has proven to be exceptionally well-suited for solving complex problems in this field. Precision medicine involves analyzing a vast amount of heterogeneous data, including genomics, proteomics, medical images, electronic health records, and more. Deep Learning models, particularly neural networks, excel at learning intricate patterns and representations from such diverse and high-dimensional data types. One of the key strengths of Deep Learning is its ability to automatically extract relevant features from raw data. In precision medicine, where meaningful features might not be explicitly defined, Deep Learning models can identify subtle relationships and features that contribute to disease diagnosis, prognosis, and treatment. Many diseases have intricated underlying mechanisms that operate at various levels of complexity. Deep Learning's hierarchical architecture, with multiple layers of interconnected neurons, can capture these intricate patterns and relationships, making it well-suited for modeling complex disease processes.

Component Insights

The software segment is projected to experience rapid growth during the forecast period. Precision medicine relies heavily on analyzing vast amounts of patient data, including genomic, clinical, and lifestyle information. AI algorithms are capable of processing and extracting meaningful insights from these complex datasets. Software applications enable the development and deployment of these algorithms, allowing healthcare professionals to analyze patient data at a scale and complexity that would be impossible manually. AI algorithms, such as machine learning and deep learning models, are central to making sense of precision medicine data. These algorithms require large amounts of labeled data for training, fine-tuning, and validation. Software platforms provide the infrastructure for researchers and data scientists to design, develop, and train these AI models effectively.

Regional Insights

North America emerged as the dominant player in the global Artificial Intelligence In Precision Medicine market in 2022, holding the largest market share in terms of value. North America boasts advanced healthcare infrastructure, including well-established electronic health record (EHR) systems, which provide a wealth of patient data that can be used to train and validate AI algorithms for precision medicine. Access to high-quality data is crucial for developing accurate AI models. The region has witnessed substantial investments and funding for AI startups and companies working in the field of precision medicine. Venture capital firms and investors are drawn to the potential of combining AI with healthcare, driving innovation and growth in the market. North America, particularly the United States, has a robust ecosystem for research and innovation in both AI and medicine. Leading research universities, medical institutions, and technology companies in the region have been at the forefront of developing AI technologies for precision medicine applications. North America has a tradition of collaboration between the healthcare and technology sectors. This collaboration has facilitated the integration of AI solutions into medical practice. Partnerships between hospitals, research institutions, and tech companies have accelerated the development and adoption of AI-powered precision medicine tools.

Key Market Players

  • Glanbia Plc
  • BioXcel Therapeutics, Inc.
  • Sanofi S.A.
  • NVIDIA Corp.
  • Alphabet Inc. (Google Inc.)
  • IBM Technology corporation
  • Microsoft Corporation
  • Intel Corp.
  • AstraZeneca plc
  • GE HealthCare
  • Enlitic, Inc.

Report Scope:

In this report, the Global Artificial Intelligence In Precision Medicine Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Artificial Intelligence In Precision Medicine Market, By Technology:

  • Deep Learning
  • Querying Method
  • Natural Language Processing

Artificial Intelligence In Precision Medicine Market, By Component :

  • Hardware
  • Software
  • Service

Artificial Intelligence In Precision Medicine Market, By Therapeutic Application :

  • Oncology
  • Cardiology
  • Neurology
  • Respiratory
  • Other

Artificial Intelligence In Precision Medicine Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
  • Asia-Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Artificial Intelligence In Precision Medicine Market.

Available Customizations:

  • Global Artificial Intelligence In Precision Medicine market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

2. Research Methodology

3. Executive Summary

4. Voice of Customer

5. Global Artificial Intelligence In Precision Medicine Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Technology (Software Solutions, Hardware, Services)
    • 5.2.2. By Cancer Type (Breast Cancer, Lung Cancer, Prostate Cancer, Colorectal Cancer, Brain Tumor, Others)
    • 5.2.3. By End-User (Hospital, Surgical Centers and Medical Institutes, Others)
    • 5.2.4. By Region
    • 5.2.5. By Company (2022)
  • 5.3. Market Map

6. North America Artificial Intelligence In Precision Medicine Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Technology
    • 6.2.2. By Cancer Type
    • 6.2.3. By End-User
    • 6.2.4. By Form
    • 6.2.5. By Distribution Channel
    • 6.2.6. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Technology
        • 6.3.1.2.2. By Cancer Type
        • 6.3.1.2.3. By End-User
    • 6.3.2. Canada Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Technology
        • 6.3.2.2.2. By Cancer Type
        • 6.3.2.2.3. By End-User
    • 6.3.3. Mexico Artificial Intelligence In Precision Medicine Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Technology
        • 6.3.3.2.2. By Cancer Type
        • 6.3.3.2.3. By End-User

7. Europe Artificial Intelligence In Precision Medicine Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Technology
    • 7.2.2. By Cancer Type
    • 7.2.3. By End-User
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Technology
        • 7.3.1.2.2. By Cancer Type
        • 7.3.1.2.3. By End-User
    • 7.3.2. United Kingdom Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Technology
        • 7.3.2.2.2. By Cancer Type
        • 7.3.2.2.3. By End-User
    • 7.3.3. Italy Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecasty
        • 7.3.3.2.1. By Technology
        • 7.3.3.2.2. By Cancer Type
        • 7.3.3.2.3. By End-User
    • 7.3.4. France Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Technology
        • 7.3.4.2.2. By Cancer Type
        • 7.3.4.2.3. By End-User
    • 7.3.5. Spain Artificial Intelligence In Precision Medicine Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Technology
        • 7.3.5.2.2. By Cancer Type
        • 7.3.5.2.3. By End-User

8. Asia-Pacific Artificial Intelligence In Precision Medicine Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Technology
    • 8.2.2. By Cancer Type
    • 8.2.3. By End-User
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Technology
        • 8.3.1.2.2. By Cancer Type
        • 8.3.1.2.3. By End-User
    • 8.3.2. India Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Technology
        • 8.3.2.2.2. By Cancer Type
        • 8.3.2.2.3. By End-User
    • 8.3.3. Japan Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Technology
        • 8.3.3.2.2. By Cancer Type
        • 8.3.3.2.3. By End-User
    • 8.3.4. South Korea Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Technology
        • 8.3.4.2.2. By Cancer Type
        • 8.3.4.2.3. By End-User
    • 8.3.5. Australia Artificial Intelligence In Precision Medicine Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Technology
        • 8.3.5.2.2. By Cancer Type
        • 8.3.5.2.3. By End-User

9. South America Artificial Intelligence In Precision Medicine Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Technology
    • 9.2.2. By Cancer Type
    • 9.2.3. By End-User
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Technology
        • 9.3.1.2.2. By Cancer Type
        • 9.3.1.2.3. By End-User
    • 9.3.2. Argentina Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Technology
        • 9.3.2.2.2. By Cancer Type
        • 9.3.2.2.3. By End-User
    • 9.3.3. Colombia Artificial Intelligence In Precision Medicine Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Technology
        • 9.3.3.2.2. By Cancer Type
        • 9.3.3.2.3. By End-User

10. Middle East and Africa Artificial Intelligence In Precision Medicine Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Technology
    • 10.2.2. By Cancer Type
    • 10.2.3. By End-User
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Technology
        • 10.3.1.2.2. By Cancer Type
        • 10.3.1.2.3. By End-User
    • 10.3.2. Saudi Arabia Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Technology
        • 10.3.2.2.2. By Cancer Type
        • 10.3.2.2.3. By End-User
    • 10.3.3. UAE Artificial Intelligence In Precision Medicine Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Technology
        • 10.3.3.2.2. By Cancer Type
        • 10.3.3.2.3. By End-User

11. Market Dynamics

12. Market Trends & Developments

13. Global Artificial Intelligence In Precision Medicine Market: SWOT Analysis

14. Competitive Landscape

  • 14.1. Business Overview
  • 14.2. Cancer Type Offerings
  • 14.3. Recent Developments
  • 14.4. Key Personnel
  • 14.5. SWOT Analysis
    • 14.5.1. Medial EarlySign
    • 14.5.2. Cancer Center.ai
    • 14.5.3. Microsoft Corporation
    • 14.5.4. Flatiron Health
    • 14.5.5. Path AI
    • 14.5.6. Therapixel
    • 14.5.7. Tempus Labs, Inc.
    • 14.5.8. Paige AI, Inc.
    • 14.5.9. Kheiron Medical Technologies Limited
    • 14.5.10. SkinVision

15. Strategic Recommendations

16. About Us & Disclaimer