The worldwide “AI-Driven Drug Repurposing Solutions Market” 2025 Research Report presents a professional and complete analysis of the Global AI-Driven Drug Repurposing Solutions Market in the current situation. This report includes development plans and policies along with AI-Driven Drug Repurposing Solutions manufacturing processes and price structures. the reports 2025 research report offers an analytical view of the industry by studying different factors like AI-Driven Drug Repurposing Solutions Market growth, consumption volume, Market Size, Revenue, Market Share, Market Trends, and AI-Driven Drug Repurposing Solutions industry cost structures during the forecast period from 2025 to 2032. It encloses in-depth Research on the AI-Driven Drug Repurposing Solutions Market state and the competitive landscape globally. This report analyzes the potential of the AI-Driven Drug Repurposing Solutions Market in the present and future prospects from various angles in detail.

The global AI-Driven Drug Repurposing Solutions market report is provided for the international markets as well as development trends, competitive landscape analysis, and key region’s development status. Development policies and plans are discussed as well as manufacturing processes and cost structures are also analyzed. This report additionally states import/export consumption, supply and demand Figures, cost, price, revenue, and gross margins. The Global AI-Driven Drug Repurposing Solutions market 2025 research provides a basic overview of the industry including definitions, classifications, applications, and industry chain structure.

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The report further explores the key business players along with their in-depth profiling:

BenevolentAI Limited

Insilico Medicine Inc.

Atomwise Inc.

Healx Ltd.

Cyclica Inc.

Recursion Pharmaceuticals Inc.

BioXcel Therapeutics Inc.

Iktos SAS

Standigm Inc.

Berg LLC

Valo Health Inc.

twoXAR Pharmaceuticals Inc.

Evaxion Biotech A/S

Lantern Pharma Inc.

Exscientia plc

Verge Genomics Inc.

Deep Genomics Inc.

Owkin Inc.

Segmentation by Type:

Machine learning platforms

Knowledge graphs

Deep learning models

Segmentation by Applications:

Rare diseases

Oncology

Infectious diseases

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Report Drivers & Trends Analysis:

The report also discusses the factors driving and restraining market growth, as well as their specific impact on demand over the forecast period. Also highlighted in this report are growth factors, developments, trends, challenges, limitations, and growth opportunities. This section highlights emerging AI-Driven Drug Repurposing Solutions Market trends and changing dynamics. Furthermore, the study provides a forward-looking perspective on various factors that are expected to boost the market’s overall growth.

Competitive Landscape Analysis:

In any market research analysis, the main field is competition. This section of the report provides a competitive scenario and portfolio of the AI-Driven Drug Repurposing Solutions Market’s key players. Major and emerging market players are closely examined in terms of market share, gross margin, product portfolio, production, revenue, sales growth, and other significant factors. Furthermore, this information will assist players in studying critical strategies employed by market leaders in order to plan counterstrategies to gain a competitive advantage in the market.

Key Data Covered in the AI-Driven Drug Repurposing Solutions Market:

✤ CAGR of the Market during the forecast period.

✤ Detailed information on factors that will drive the growth of the Market between 2025 and 2032.

✤ Precise estimation of the size of the Market size and its contribution to the market in focus on the parent market.

✤Accurate predictions about upcoming trends and changes in consumer behavior.

✤ Growth of the Market across APAC, North America, Europe, Middle East and Africa, and South America.

✤ A thorough analysis of the market’s competitive landscape and detailed information about vendors.

✤ Comprehensive analysis of factors that will challenge the growth of the AI-Driven Drug Repurposing Solutions Market vendors.

Here is an overview of the different factual statements covered by the study:

➤ The learn-about consists of an area that breaks down strategic traits in present and upcoming R&D, new product launches, collaborations, regional expansion, and mergers & acquisitions.

➤ The lookup focuses on essential market traits such as revenue, product cost, potential and utilization rates, import/export rates, supply/demand figures, market share, and CAGR.

➤ The learn-about is a series of analyzed records and a variety of barrels of the house bought via a mixture of analytical equipment and an inside look-up process.

➤ The Market can be divided into 4 areas in accordance with the regional breakdown: North American Markets, European Markets, Asian Markets, and the Rest of the World

Reason to Buy:

✅ Save and reduce time carrying out entry-level research by identifying the growth, size, leading players, and segments in the global AI-Driven Drug Repurposing Solutions Market.

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✅ The key findings and recommendations highlight crucial progressive industry trends in the AI-Driven Drug Repurposing Solutions Market, thereby allowing players to develop effective long-term strategies in order to garner their market revenue.

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✅ Scrutinize in-depth global market trends and outlook coupled with the factors driving the market, as well as those restraining the growth to a certain extent.

✅ Enhance the decision-making process by understanding the strategies that underpin commercial interest with respect to products, segmentation, and industry verticals.

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FAQ’s:

[1] Who are the global manufacturers of AI-Driven Drug Repurposing Solutions, what are their share, price, volume, competitive landscape, SWOT analysis, and future growth plans?

[2] What are the key drivers, growth/restraining factors, and challenges of AI-Driven Drug Repurposing Solutions?

[3] How is the AI-Driven Drug Repurposing Solutions industry expected to grow in the projected period?

[4] How has COVID-19 affected the AI-Driven Drug Repurposing Solutions industry and is there any change in the regulatory policy framework?

[5] What are the key areas of applications and product types of the AI-Driven Drug Repurposing Solutions industry that can expect huge demand during the forecast period?

[6] What are the key offerings and new strategies adopted by AI-Driven Drug Repurposing Solutions players?

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Austin, July 11, 2025 (GLOBE NEWSWIRE) — Legal AI Market Size & Growth Insights:

According to the SNS Insider,“The Legal AI Market Size was valued at USD 1.1 billion in 2023 and is projected to reach USD 4.9 billion by 2032, expanding at a CAGR of 18.13% between 2024 and 2032.”

Growth is driven by early adoption of AI in legal research, contract analytics, and compliance management. Rising legal costs and demand for automation in corporate legal departments are expected to further accelerate market expansion.

Get a Sample Report of Legal AI Market Forecast @ https://www.snsinsider.com/sample-request/6862 

Leading Market Players with their Product Listed in this Report are:

• IBM Corporation
• Thomson Reuters
• LexisNexis
• ROSS Intelligence
• Luminance Technologies
• LawGeex
• Casetext
• Cognitiv+
• Ayfie Group
• Ravn Systems (iManage)
• Kira Systems
• Evisort
• Onna Technologies
• Legalsifter
• Veritone Inc

Legal AI Market Report Scope:

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Key Industry Segmentation

By Component: Solution Dominates, Services Fastest Growing

The solution segment dominated the market and held the largest market share of more than 74% in 2023, as increased deployment of AI software for legal research, contract analytics, and risk management. Such solutions allow for the automation of tedious tasks and help practices stay compliant with changing regulations, thus providing high ROI. As generative AI continues to evolve, legal models that were trained on large data sets of written documents are being tailored toward specific legal functions.

The services segment is expected to grow at the fastest CAGR during the forecast period due to organizations opting for consulting, integration, and maintenance services. There is also a high demand for managed services to support cloud-based AI platforms and ensure seamless legal operations.

By Technology: Machine Learning Leads, NLP Fastest Growing

Machine Learning and Deep Learning technologies dominated the market in 2023 and accounted for 65% of revenue share, owing to their widespread use in case prediction, outcome analysis, or compliance tracking. All these technologies are being utilized by law firms to classify documents, detect fraud, sentiment, and you name it; all of which ultimately translate into better outcomes for clients along with greater operational efficiency for law firms.

Natural Language Processing (NLP) is forecasted to witness the fastest growth as it is vital for language understanding, document summarization, and conversational legal assistance via chatbots. The concept of NLP-powered tools is changing the way legal professionals review contracts and break down long documents, resulting in quicker speeds and lower costs.

By Application: Legal Research Dominates, Legal Chatbots Growing Fastest

Legal research dominated the market in 2023 and accounted for a significant revenue share. This is attributable to the widespread adoption of AI to process case law, statutes, and regulations much more efficiently than traditional methods. AI tools help decrease research time and increase the precision of insights, leading legal teams to spend more time on strategy and more time in front of their clients.

legal chatbots are poised for the fastest growth as firms and legal departments are implementing conversational AI to help answer client questions, assist with first-phase client intake, and get quick access to legal facts. Chatbot applicability is on the rise through their increased implementation as legal customer service tools as well as public legal aid platforms in various jurisdictions.

By End-Use (Law Firms & Corporate Legal Departments): Law Firms Dominate, Corporate Legal Growing Fastest

Law firms’ segment dominated the Legal AI market in 2023 and accounted for a significant revenue share, attributed to the early adoption and investment of AI tools by law firms due to the need to stay competitive and enhance client satisfaction. AI helps them derive insights from case histories, model legal drafting, and develop the best litigation strategies.

The Corporate Legal Departments segment is growing at the fastest pace, as they need to process large amounts of contracts, regulatory documents, and internal compliance checks. It delivers better control, risk mitigation, and internal efficiency of the third-party process to these departments using AI.

Do you have any specific queries or need any customized research on Legal AI Market? Submit your inquiry here @ https://www.snsinsider.com/enquiry/6862 

By Region: North America Dominates, Asia-Pacific Growing Fastest

North America led the Legal AI market in 2023 and accounted for 44% of revenue share, driven by robust technological infrastructure, heavy investments in legal tech, and early adoption by top-tier law firms and legal service providers. U.S.-based firms are leveraging AI to boost client servicing, litigation analysis, and internal compliance.

The Asia-Pacific region is poised to grow at the highest CAGR through 2032. Countries like India, China, and Singapore are witnessing a rise in legal digitization, backed by government reforms and increasing adoption of AI in corporate legal sectors. Rising legal complexities and the globalization of law firms are fueling AI integration in the region.

Table of Contents – Major Points

1. Introduction

2. Executive Summary

3. Research Methodology

4. Market Dynamics Impact Analysis

5. Statistical Insights and Trends Reporting

6.  Competitive Landscape

7. Legal AI Market Segmentation, by Component

8. Legal AI Market Segmentation, by Technology

9. Legal AI Market Segmentation, by Application

10. Legal AI Market Segmentation, by End-Use

11. Regional Analysis

12. Company Profiles

13. Use Cases and Best Practices

14. Conclusion

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company’s aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate diagnoses, racial bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Data-sharing concerns, unrealistic expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

Incremental change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.