市場調查報告書
商品編碼
1276418
製藥行業機器學習的全球市場規模、份額和行業趨勢分析報告:按組件、按部署類型、按組織規模、按地區、展望和預測,2023 年-2029Global Machine Learning in Pharmaceutical Industry Market Size, Share & Industry Trends Analysis Report By Component (Solution and Services), By Deployment Mode (Cloud and On-premise), By Organization size, By Regional Outlook and Forecast, 2023 - 2029 |
預計在預測期內,製藥行業的機器學習市場規模將以 34.4% 的複合年增長率增長,到 2029 年將達到 114 億美元。
機器學習還可以通過支持案例跟進和其他建議來幫助防止再犯。人工智能與電子病歷相結合。醫生偶爾會使用彈出窗口來解釋某些遺傳特徵如何影響患者的醫療狀況,或者新藥如何改善患者的健康。醫生可以點擊彈窗來更好地瞭解疾病並推薦最佳療程。
這些電子記錄不僅節省時間和空間,還能積極幫助醫生提出更好的治療建議,並讓他們瞭解手頭的細節。一些肺癌高發國家已經開始實施人工智能程序,通過分析 X 光和 CT 掃瞄來檢測可疑結節和病變,幫助醫生更好地診斷肺癌患者。
COVID-19 的影響分析
COVID-19 對製藥行業的機器學習市場產生了積極影響。機器學習的使用有助於推進製藥領域的療法和疫苗。此外,機器學習的使用正在發現有前途的 COVID-19 候選藥物。機器學習算法可以篩選來自基因數據庫和臨床試驗的大量數據,以識別可能對病毒有效的化合物。這有助於加快通常需要數年時間的藥物發現過程,從而促成許多新型 COVID-19 療法的早期開發。
市場增長因素
提前預測趨勢
運營商正在利用人工智能和機器學習提前數月向用戶提供登革熱等即將爆發的確切地點、日期和時間。該計劃還建議在污染區域周圍數百米範圍內採取登革熱控制措施。通過這種方式,機器學習可以幫助研究人員預測即將發生的流行病何時何地發生,提醒有關當局並告知公眾。這種能力有可能挽救大量生命,並有望推動機器學習的採用並為市場帶來新的增長機會。
擴大技術在醫療行業的應用
使用電子摘要而不是紙質摘要,患者護理變得更簡單、更高效。展望未來,基因組(以及大量共生細菌基因組)和定制療法的進步將大大增加可用信息量。隨著收集到更多的患者數據,可以獲得更多的見解。由於機器學習具有降低成本、管理和收集大量患者數據以供未來參考等各種好處,預計在製藥行業中越來越多地使用機器學習將推動市場增長。
市場製約
數據不一致
當使用許多數據源時,很難協調所有數據並對數據集執行分析。選擇單點解決方案或沒有強大數據分析系統的公司必須手動編譯分析報告和見解。此類過程非常耗時,並且可能不會產生真正的業務相關見解。因此,預計與數據相關的問題將阻礙製藥機器學習市場的擴張。
組件視角
基於組件,製藥行業機器學習細分為解決方案和服務。到 2022 年,解決方案部分將在製藥行業的機器學習市場中佔據最高的收入份額。這是由於製藥業在發現和發現新藥時產生的大量數據。ML 算法可以處理和分析這些數據,以找到可以指導藥物開發決策的模式、關係和見解。製藥行業對機器學習解決方案的需求是由使藥物研發過程更快、更便宜的願望推動的。
組織規模展望
根據組織規模,製藥機器學習市場分為中小企業和大型企業。2022 年,大型企業部門在製藥行業的機器學習市場中佔據了最大的收入份額。這涉及到大型製藥公司使用機器學習技術來評估來自眾多來源的海量數據,例如電子病歷、臨床試驗和遺傳信息,以發現潛在的藥物靶點,改善患者的治療效果,並改善患者的預後。因為它可以做出預測並改進臨床試驗設計。
部署模式展望
製藥行業市場的機器學習按部署模式分為雲和本地。2022 年,製藥行業的機器學習市場由本地部分主導。這是因為本地服務可以比雲服務節省資金。由於數據使用和分發是 CPU/GPU 密集型的,因此在公共雲中維護按需付費的 ML 流程成本很高。遷移到公共雲可能需要更大的數據集,從而增加複雜性和成本。
區域展望
按地區劃分,對北美、歐洲、亞太地區和 LAMEA 的製藥行業機器學習市場進行了分析。北美地區將在 2022 年以最大的收入份額引領製藥行業的機器學習市場。專注於研發的北美製藥企業對市場貢獻巨大。近年來,市場已經採用機器學習來推動創新、提高生產力並加速藥物發現和開發。
The Global Machine Learning in Pharmaceutical Industry Market size is expected to reach $11.4 billion by 2029, rising at a market growth of 34.4% CAGR during the forecast period.
The purpose of machine learning in the pharmaceutical industry is to advance medical knowledge, not to replace a doctor. A physician's whole body of knowledge, which includes everything they acquired in medical school and during their training, in addition to their experience treating patients, is scaled to unprecedented levels by artificial intelligence algorithms.
The ability to obtain and process the vast quantity of data available to doctors-information on new treatments, disease symptoms, drug interactions, and how different patients treated in the same way can have different outcomes-is quickly emerging as a crucial talent. And machine learning makes it possible for them to make inferences from that data and put them into action. For instance, machine learning systems may quickly identify a rare ailment, browse the available treatments, and prescribe by compiling data from many patient visits and thousands of doctors. As a result, time is saved, which leads to increased effectiveness and decreased expenses.
Machine learning can also prevent recidivism by helping to follow up on instances and providing extra recommendations. AI is integrated with electronic medical records. When a doctor uses them irregularly, a pop-up appears explaining how particular genetic features can affect the patient's condition or how a new medication could enhance their health. A doctor can better understand the illness and recommend the best course of treatment by clicking the pop-up.
Not only are these electronic records saving time and space, but they are also actively assisting doctors in formulating better treatment recommendations and educating them on the details in front of them. Some countries with a high lung cancer patient population are beginning to deploy AI programs to help doctors better diagnose lung cancer patients by analyzing X-rays and CT scans and spotting suspicious nodules and lesions.
COVID-19 Impact Analysis
Machine learning in pharmaceutical industry market, was positively affected by the COVID-19. The utilization of machine learning has been instrumental in the advancement of treatments and vaccines within the pharmaceutical sector. In addition, prospective COVID-19 drug candidates have been found due to the use of ML. Machine learning algorithms can sift through huge amounts of data from genetic databases and clinical trials to identify compounds potentially effective against the virus. This has contributed to speeding the drug discovery process, which ordinarily takes years, and has led to the quick development of many novel COVID-19 medications.
Market Growth Factors
Predicting epidemic beforehand
Businesses are utilizing AI and machine learning to provide users with the precise place and date of the upcoming outbreak, like a dengue outbreak, a few months in advance. This program also suggests anti-dengue measures a few hundred meters around the contaminated area. Thus, using machine learning, researchers can foresee the timing and location of impending epidemics, alert the relevant authorities, and inform the general public about it. This capability has the potential to save a significant number of lives, which is expected to increase machine learning's adoption and open up new growth opportunities for the market.
Increasing use of technologies in the medical industry
Patient treatment is made simpler and more productive using electronic summaries instead of paper. Future advances in genomes (and the enormous genomics of the symbiotic bacteria) and tailored therapy will greatly increase the amount of information available. As more patient data is gathered, more insights will become accessible. The increased use of machine learning in the pharmaceutical industry is anticipated to drive market growth due to its various benefits, including cost reduction, management, and the collection of massive patient data for future reference.
Market Restraining Factors
Inconsistency of data
Harmonizing all the data and performing analytics over the data set is challenging when many data sources are used. Companies that choose a point solution or do not have a robust data analytics system must manually compile analytics reports and insights. Such a procedure takes a lot of time and might not produce any insights with practical business relevance. Thus, the issues associated with data are expected to hinder machine learning in pharmaceutical industry market's expansion.
Component Outlook
Based on Component, the machine learning in pharmaceutical industry market is segmented into solution and services. The solution segment held the highest revenue share in the machine learning in pharmaceutical industry market in 2022. This is due to the fact that the pharmaceutical industry produces enormous amounts of data when creating and discovering new medicines. ML algorithms can process and analyze this data to find patterns, connections, and insights that can guide drug development decisions. The demand for machine learning solutions in the pharmaceutical industry is further increased by the desire for quicker and more affordable drug research and development processes.
Organization size Outlook
On the basis Organization size, the machine learning in pharmaceutical industry market is divided into SMEs and large enterprises. The large enterprises segment witnessed the largest revenue share in the machine learning in pharmaceutical industry market in 2022. This is because large pharmaceutical corporations can use machine learning technology to evaluate enormous volumes of data from numerous sources, including electronic health records, clinical trials, and genetic information, to find prospective drug targets, forecast patient outcomes, and improve clinical trial design.
Deployment Mode Outlook
By deployment mode, the machine learning in pharmaceutical industry market is classified into cloud and on-premise. The on-premise segment garnered a prominent revenue share in the machine learning in pharmaceutical industry market in 2022. This is because on-premise services can save more capital than cloud services, as the use and distribution of data can be CPU/GPU intensive, making it expensive to maintain an ML process in a public cloud on a pay-as-you-go basis. The data set might need to be bigger to migrate to the public cloud, adding complexity and cost.
Regional Outlook
Region-wise, the machine learning in pharmaceutical industry market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America region led the machine learning in pharmaceutical industry market by generating the maximum revenue share in 2022. With a strong emphasis on R&D, the pharmaceutical business in North America makes a considerable contribution to the market. The market has adopted machine learning in recent years to spur innovation, boost productivity, and quicken medication discovery and development.
The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Machine Learning in Pharmaceutical Industry Market. Companies such as NVIDIA Corporation, IBM Corporation and Cyclica, Inc. are some of the key innovators in Machine Learning in Pharmaceutical Industry Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google LLC (Alphabet, Inc.), NVIDIA Corporation, IBM Corporation, Microsoft Corporation, Cyclica, Inc., BioSymetrics Inc., Cloud Pharmaceuticals, Inc., Deep Genomics Incorporated and Atomwise, Inc.
Recent Strategies Deployed in Machine Learning in Pharmaceutical Industry Market
Partnerships, Collaborations and Agreements:
Mar-2023: NVIDIA collaborated with AWS, a US-based provider of the cloud-based web platform. The collaboration focuses on developing infrastructure for training large ML models and developing generative AI applications. This collaboration supports customers to make the best use of accelerated computing and generative AI to further explore opportunities.
Oct-2022: BioSymetrics partnered with Deerfield Management, a US-based healthcare investment firm. The partnership focuses on advancing the development of therapeutics. Additionally, as per the agreement, both companies would work on identifying therapeutic targets through BioSymetrics' database and platform.
Jun-2022: Cyclica partnered with Oncocross, a South Korea-based developer of cancer drugs. The partnership includes the discovery and designing of treatments intended for myelofibrosis.
Feb-2022: BioSymetrics came into partnership with Sema4, a US-based health intelligence company. The partnership focuses on drug discovery based on data to accelerate precision medicine. The companies through this partnership aim to deliver an innovative and differentiated method for drug discovery. Further, Sema4's multi-omic data insights and access enhance BioSymetrics' capabilities to discover treatments intended for people with different diseases.
Feb-2022: Microsoft entered into a partnership with Tata Consultancy Services, an Indian company focusing on providing information technology services and consulting. Under the partnership, Tata Consultancy Services leveraged its software, TCS Intelligent Urban Exchange (IUX) and TCS Customer Intelligence & Insights (CI&I), to enable businesses in providing hyper-personalized customer experiences. CI&I and IUX are supported by artificial intelligence (AI), and machine learning, and assist in real-time data analytics. The CI&I software empowered retailers, banks, insurers, and other businesses to gather insights, predictions, and recommended actions in real time to enhance the satisfaction of customers.
Aug-2021: IBM Corporation came into partnership with Cloudera, an American software company providing enterprise data management systems. Through this partnership, both companies would help enterprises with their AI and Data needs. Additionally, this would allow IBM to let Cloudera reside under the IBM Data Fabric which would enable business access to the right data at a better cost, regardless of the data's storage location.
Sep-2021: Deep Genomics announced a partnership with Mila, an AI institute based in Canada. The partnership agreement allows Deep Genomics to join the AI institute's community and make use of the institute's recruitment activities.
Mar-2021: IBM partnered with Cleveland Clinic, a US-based nonprofit medical center. The partnership involves establishing a discovery accelerator, a joint Cleveland clinic, and an IBM center, with the aim to accelerate the speed of discovery in multiple areas including, single-cell transcriptomics, clinical applications, etc. by using high-performance computing on AI, quantum computing technologies, and hybrid cloud.
Product Launches and Expansions:
Nov-2022: NVIDIA joined hands with Microsoft, a US-based tech giant. The collaboration focuses on developing powerful cloud AI computers. The AI supercomputer would be developed by leveraging, Microsoft's Azure supercomputing infrastructure and NVIDIA's GPUs. Further, this collaboration provides advanced AI infrastructure and software to researchers and companies.
May-2021: Google released Vertex AI, a novel managed machine learning platform that enables developers to more easily deploy and maintain their AI models. Engineers can use Vertex AI to manage video, image, text, and tabular datasets, and develop machine learning pipelines to train and analyze models utilizing Google Cloud algorithms or custom training code. After that, the engineers can install models for online or batch use cases all on scalable managed infrastructure.
Mar-2021: Microsoft released updates to Azure Arc, its service that brought Azure products and management to multiple clouds, edge devices, and data centers with auditing, compliance, and role-based access. Microsoft also made Azure Arc-enabled Kubernetes available. Azure Arc-enabled Machine Learning and Azure Arc-enabled Kubernetes are developed to aid companies to find a balance between enjoying the advantages of the cloud and maintaining apps and maintaining apps and workloads on-premises for regulatory and operational reasons. The new services enable companies to implement Kubernetes clusters and create machine learning models where data lives, as well as handle applications and models from a single dashboard.
Mergers and Acquisitions:
Jul-2021: IBM entered into an agreement to acquire Bluetab Solutions Group, an enterprise software, and technical services company. Through this acquisition, Bluetab would become a strategic part of IBM's data services consulting practice to improve its hybrid cloud and AI strategy.
Market Segments covered in the Report:
By Component
By Deployment Mode
By Organization size
By Geography
Companies Profiled
Unique Offerings from KBV Research
List of Figures
FIG