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市場調査レポート
商品コード
1276418
製薬業界における機械学習の世界市場規模、シェア、産業動向分析レポート:コンポーネント別(ソリューション、サービス)、展開形態別(クラウド、オンプレミス)、組織規模別、地域別展望と予測、2023年~2029年Global 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 |
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製薬業界における機械学習の世界市場規模、シェア、産業動向分析レポート:コンポーネント別(ソリューション、サービス)、展開形態別(クラウド、オンプレミス)、組織規模別、地域別展望と予測、2023年~2029年 |
出版日: 2023年04月28日
発行: KBV Research
ページ情報: 英文 200 Pages
納期: 即納可能
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製薬業界における機械学習市場規模は、予測期間中に34.4%のCAGRで成長し、2029年には114億米ドルに達すると予測されます。
機械学習は、事例のフォローアップやプラスアルファの提案を支援することで、再犯を防ぐこともできます。AIは電子カルテと統合されています。医師が不定期に使用すると、特定の遺伝的特徴が患者の病状にどのように影響するか、あるいは新しい薬が患者の健康をどのように増進するかについて説明するポップアップが表示されます。医師はポップアップをクリックすることで、病気への理解を深め、最適な治療方針を勧めることができます。
このような電子記録は、時間とスペースの節約になるだけでなく、医師がより良い治療法を提案したり、目の前の詳細を教育したりするのに積極的に役立っているのです。肺がん患者の多い一部の国では、X線やCTスキャンを分析し、疑わしい結節や病変を発見することで、医師が肺がん患者をよりよく診断するためのAIプログラムの導入が始まっています。
COVID-19の影響分析
製薬業界における機械学習市場は、COVID-19からプラスの影響を受けました。機械学習の活用は、医薬品分野での治療法やワクチンの進歩に役立っています。また、機械学習の活用により、COVID-19の有望な医薬品候補が発見されています。機械学習アルゴリズムは、遺伝子データベースや臨床試験から得られる膨大なデータをふるいにかけ、ウイルスに有効な可能性のある化合物を特定することができます。これにより、通常数年かかる創薬プロセスのスピードアップに貢献し、多くの新規COVID-19治療薬の早期開発につながっています。
市場の成長要因
流行を事前に予測
事業者はAIや機械学習を活用し、デング熱のように今後発生する正確な場所と日時を数カ月前にユーザーに提供しています。また、このプログラムは、汚染された地域の周囲数百メートルのデング熱対策を提案します。このように、機械学習を利用することで、研究者は差し迫った伝染病の発生時期や場所を予見し、関係当局に警告し、一般の人々に知らせることができます。この能力は、相当数の人命を救う可能性があるため、機械学習の導入が進み、市場に新たな成長機会をもたらすと期待されています。
医療業界におけるテクノロジー活用の拡大
患者の治療は、紙の代わりに電子サマリーを使用することで、よりシンプルで生産的になります。今後、ゲノム(および共生細菌の膨大なゲノム)とテーラーメイド治療の進歩により、利用可能な情報量が大幅に増加します。より多くの患者データが収集されれば、より多くの洞察にアクセスできるようになります。製薬業界における機械学習の利用の増加は、コスト削減、管理、将来参照するための膨大な患者データの収集など、さまざまな利点があるため、市場の成長を促進すると予想されます。
市場の抑制要因
データの非一貫性
多くのデータソースが使用されている場合、すべてのデータを調和させ、データセットに対して分析を実行することは困難です。ポイントソリューションを選択したり、堅牢なデータ分析システムを持たない企業は、分析レポートやインサイトを手作業でまとめなければなりません。このような手順には多くの時間がかかり、実際のビジネスと関連性のある洞察が得られない可能性もあります。このように、データに関連する問題は、製薬業界の機械学習市場の拡大の妨げになると予想されます。
コンポーネントの展望
コンポーネントに基づき、製薬業界における機械学習市場は、ソリューションとサービスに区分されます。2022年の製薬業界における機械学習市場では、ソリューションセグメントが最も高い収益シェアを占めています。これは、製薬業界が新薬を創製・発見する際に膨大な量のデータを生成していることに起因しています。MLアルゴリズムは、このデータを処理・分析し、医薬品開発の意思決定の指針となるパターンや関連性、洞察を見出すことができます。製薬業界における機械学習ソリューションの需要は、医薬品の研究開発プロセスをより迅速かつ安価に行いたいという願望によってさらに高まっています。
組織規模の展望
組織規模に基づき、製薬業界の機械学習市場は、中小企業と大企業に分けられます。大企業セグメントは、2022年の製薬業界における機械学習市場で最大の収益シェアを獲得しました。これは、大製薬企業が機械学習技術を使用して、電子カルテ、臨床試験、遺伝子情報などの多数のソースからの膨大な量のデータを評価し、見込みのある薬物標的の発見、患者の転帰予測、臨床試験デザインの改善を行うことができるからです。
デプロイメントモードの展望
医薬品業界における機械学習市場は、導入モード別に、クラウドとオンプレミスに分類されます。2022年の製薬業界における機械学習市場では、オンプレミスセグメントが突出した収益シェアを獲得しました。これは、オンプレミスサービスはクラウドサービスよりも資本を節約できるためです。データの使用と配布はCPU/GPUを多用するため、パブリッククラウドでMLプロセスを従量制で維持するにはコストがかかるためです。パブリッククラウドに移行するためには、データセットを大きくする必要がある場合もあり、複雑さとコストが増します。
地域別の展望
地域別に見ると、製薬業界における機械学習市場は、北米、欧州、アジア太平洋、LAMEAで分析されています。北米地域は、2022年に最大の収益シェアを獲得し、製薬業界における機械学習市場をリードしました。研究開発に重点を置く北米の製薬事業は、市場にかなりの貢献をしています。同市場は近年、イノベーションの促進、生産性の向上、薬の発見と開発の迅速化のために機械学習を導入しています。
List of Figures
FIG
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