AI-Driven Drug Delivery Market Size, Share, Opportunities, And Trends By Technology (Machine Learning, Deep Learning, Natural Language Processing (Nlp), Computer Vision, Others), By Type Of Drug Delivery (Oral Drug Delivery, Injectable Drug Delivery, Transdermal Drug Delivery, Inhalation Drug Delivery, Implantable Drug Delivery, Others), By Application (Oncology, Diabetes, Cardiovascular Diseases, Respiratory Disorders, Neurological Disorders, Autoimmune Diseases, Others), By End-User (Hospitals And Clinics, Pharmaceutical Companies, Research Institutes And Academic Centers, Home Care Settings, Others), And By Geography - Forecasts From 2023 To 2028

The AI-driven drug delivery market is estimated to grow at a CAGR of 34.54% during the forecast period.

The AI-driven drug delivery market is a game changer in the pharmaceutical and healthcare sectors. This developing industry intends to revolutionise medicine delivery methods by using the power of artificial intelligence (AI) and optimising treatment efficacy and patient outcomes. AI-powered medication delivery systems analyse patient data using sophisticated algorithms, enabling personalised dosage and drug administration. These systems may alter medication release rates by merging real-time patient monitoring with adaptive dosage, guaranteeing accurate and timely therapeutic treatments. Furthermore, AI's predictive powers expedite drug research, formulation, and administration, reducing time-to-market for new treatments. The AI-driven drug delivery market, with the ability to increase treatment adherence, decrease side effects, and target particular disease areas, promises to usher in a new age of precision medicine and enhanced patient care.

Advancements in Artificial Intelligence (AI) and Machine Learning (ML) Technologies Enhance the AI-Driven Drug Delivery Market Growth.

Artificial intelligence (AI) and machine learning (ML) advancements are significant development drivers in the AI-driven drug delivery market. The constant progress of AI and ML algorithms has enabled more precise and sophisticated data analysis, resulting in better medicine discovery, formulation, and delivery techniques. Large datasets, including patient information and medication interactions, may be analysed by AI-powered algorithms to anticipate appropriate dosage regimens and personalised treatment plans. AI-driven medication delivery systems may alter drug release rates and dosage regimens by finding patterns and correlations in real-time patient data, maximising therapeutic efficacy while minimising unwanted effects. The fast advancement of AI and ML technologies has opened up new avenues for medication delivery, altering the landscape of pharmaceutical research and patient care.

Improved Drug Formulation and Delivery Optimization in AI-Driven Drug Delivery Market.

In the AI-driven drug delivery market, improved medication formulation and delivery optimisation are important growth factors. Researchers can analyse complicated data sets using artificial intelligence (AI) and machine learning (ML) techniques to get insights about pharmacological characteristics and interactions. This sophisticated analysis contributes to the development of more efficient medication formulations with improved bioavailability and stability. AI-powered medication delivery systems optimise dose regimens as well, guaranteeing accurate and personalised administration customised to specific patient demands. These systems can alter medication release rates and administration modalities by incorporating real-time patient data, resulting in enhanced treatment results and lower side effects. The capacity to use AI to optimise medication compositions and delivery systems opens the door to more effective and patient-centred pharmacological therapies.

Real-Time Patient Monitoring and Adaptive Dosing Boosts the  AI-Driven Drug Delivery Market Size.

In the AI-driven drug delivery market, real-time patient monitoring and adaptive dosage are critical growth factors. Drug delivery systems can continually monitor patient reactions and adjust dose regimens by utilising powerful artificial intelligence (AI) and machine learning (ML) technology. Real-time monitoring provides for the early detection of changes in patient circumstances, allowing for appropriate drug delivery modifications. To personalise medicine dose and optimise treatment performance, AI-driven adaptive dosing evaluates individual patient factors such as age, weight, and medical history. This dynamic strategy increases treatment accuracy, decreases side effects, and improves patient outcomes. The combination of real-time patient monitoring and adaptive dosage transforms medication administration, ushering in a new era of patient-centred and responsive pharmacological treatments.

North America is the Market Leader in the AI-Driven Drug Delivery Market.

North America was regarded as the market leader in the AI-driven drug delivery market. Several reasons contribute to the region's supremacy, including its well-established pharmaceutical and biotechnology sectors, substantial research and development skills, and a strong emphasis on embracing innovative technologies. Furthermore, with multiple collaborations between AI startups and major pharmaceutical corporations, North America has been at the forefront of AI applications in healthcare. The region's favourable regulatory framework and significant expenditures in AI research have hastened the development of AI-driven drug Delivery systems. However, market dynamics may change the top region in the AI-driven drug delivery market.

Increased Research and Development Investments in AI-Driven Drug Delivery Market.

Increased R&D investments are driving factors in the AI-driven drug delivery market. Pharmaceutical businesses, biotechnology firms, and research institutes are investing heavily in developing and integrating artificial intelligence (AI) technologies into drug delivery systems. These investments seek to capitalise on the promise of AI in optimising medication formulations, dosage tactics, and personalised therapies. Furthermore, cooperation between industry heavyweights and AI companies is fuelling creative advances in medicine delivery, garnering further financing. The increased interest in AI-driven solutions reflects the industry's acknowledgement of AI's potential to revolutionise medication development and improve patient care. As R&D efforts develop, the AI-driven drug delivery market evolves, providing novel ways to pharmaceutical therapies.

Key Developments:

• In June 2023, Merck, known as MSD outside of the United States and Canada, completed the purchase of Prometheus Biosciences, Inc. ("Prometheus"). Prometheus has become a wholly owned subsidiary of Merck, and its common stock will no longer be listed or traded on the Nasdaq Global Market.
• In June 2023, GSK plc and BELLUS Health Inc. established a collaboration. GSK has finalised its purchase of BELLUS, a biopharmaceutical business dedicated to improving the lives of patients suffering from refractory chronic cough (RCC), under a plan of arrangement under Section 192 of the Canada Business Corporations Act (the "Arrangement"). Camlipixant, a possible best-in-class and highly selective P2X3 antagonist now in phase III research for the first-line treatment of adult patients with RCC, was announced as part of the BELLUS purchase.
• In April 2023, Sanofi completed its acquisition of ProventionBio, Inc. ("Provention Bio"). The purchase expands Sanofi's core asset portfolio in General Medicines with the addition of TZIELD (teplizumab-Azov), a novel, wholly owned, first-in-class medication in type 1 diabetes, and furthers the company's strategy shift towards medicines with a distinctive profile. 

Company Products:

• Targeted Drug Delivery: Boehringer Ingelheim may have been investigating AI-driven drug delivery systems that allow for targeted and site-specific medication release, hence increasing drug concentration in certain parts of the body.
• Predictive Analytics: Roche may have used artificial intelligence to anticipate drug reactions and identify probable side effects, allowing for pre-emptive treatments and enhanced patient safety.
• Personalized Treatment Plans: GSK might have been working on AI-powered systems to personalise medicine doses and treatment strategies based on specific patient variables including genetics, medical history, and treatment response.
• Precision Drug Delivery: Merck may have been investigating AI-powered medication delivery systems that allow for personalized dosage and tailored drug release, hence maximizing therapeutic effects.
• AI-Optimized Drug Formulations: Novartis may be using AI algorithms to analyze medication characteristics and interactions, which might lead to the creation of optimized drug formulations with enhanced bioavailability and stability.

Segmentation:

• By Technology
  • Machine Learning
  • Deep Learning
  • Natural Language Processing (Nlp)
  • Computer Vision
  • Others   
• By Type Of Drug Delivery
  • Oral Drug Delivery
  • Injectable Drug Delivery
  • Transdermal Drug Delivery
  • Inhalation Drug Delivery
  • Implantable Drug Delivery
  • Others  
• By Application
  • Oncology
  • Diabetes
  • Cardiovascular Diseases
  • Respiratory Disorders
  • Neurological Disorders
  • Autoimmune Diseases
  • Others
• By End-User
  • Hospitals And Clinics
  • Pharmaceutical Companies
  • Research Institutes And Academic Centers
  • Home Care Settings
  • Others               
•  By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain    
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • Japan
    • China
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others

1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base, and Forecast Years Timeline

2. RESEARCH METHODOLOGY  

2.1. Research Data

2.2. Sources

2.3. Research Design

3. EXECUTIVE SUMMARY

3.1. Research Highlights

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porters Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

5.  AI-DRIVEN DRUG DELIVERY MARKET, BY TECHNOLOGY

5.1. Introduction

5.2. MACHINE LEARNING

5.3. DEEP LEARNING

5.4. NATURAL LANGUAGE PROCESSING (NLP)

5.5. COMPUTER VISION

5.6. OTHERS    

6. AI-DRIVEN DRUG DELIVERY MARKET, BY TYPE OF DRUG DELIVERY

6.1. Introduction

6.2. ORAL DRUG DELIVERY

6.3. INJECTABLE DRUG DELIVERY

6.4. TRANSDERMAL DRUG DELIVERY

6.5. INHALATION DRUG DELIVERY

6.6. IMPLANTABLE DRUG DELIVERY

6.7. OTHERS  

7. AI-DRIVEN DRUG DELIVERY MARKET, BY APPLICATION

7.1. Introduction

7.2. ONCOLOGY

7.3. DIABETES

7.4. CARDIOVASCULAR DISEASES

7.5. RESPIRATORY DISORDERS

7.6. NEUROLOGICAL DISORDERS

7.7. AUTOIMMUNE DISEASES

7.8. OTHERS

8. AI-DRIVEN DRUG DELIVERY MARKET, BY END-USER

8.1. Introduction

8.2. HOSPITALS AND CLINICS

8.3. PHARMACEUTICAL COMPANIES

8.4. RESEARCH INSTITUTES AND ACADEMIC CENTERS

8.5. HOME CARE SETTINGS

8.6. OTHERS               

8.7.  AI-DRIVEN DRUG DELIVERY MARKET, BY GEOGRAPHY

8.8. Introduction

8.9. North America

8.9.1. United States

8.9.2. Canada

8.9.3. Mexico

8.10. South America

8.10.1. Brazil

8.10.2. Argentina

8.10.3. Others

8.11. Europe

8.11.1. United Kingdom

8.11.2. Germany

8.11.3. France

8.11.4. Italy

8.11.5. Spain

8.11.6. Others

8.12. Middle East and Africa

8.12.1. Saudi Arabia

8.12.2. UAE

8.12.3. Others

8.13. Asia Pacific

8.13.1. Japan

8.13.2. China

8.13.3. India

8.13.4. South Korea

8.13.5. Indonesia 

8.13.6. Taiwan

8.13.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Emerging Players and Market Lucrativeness

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Vendor Competitiveness Matrix

10. COMPANY PROFILES

10.1. MEDTRONIC PLC

10.2. F. HOFFMANN-LA ROCHE AG

10.3. GLAXOSMITHKLINE PLC

10.4. NOVARTIS AG

10.5. ELI LILLY AND COMPANY

10.6. ASTRAZENECA PLC

10.7. MERCK & CO., INC.

10.8. PFIZER INC.

10.9. SANOFI S.A.

10.10. JOHNSON & JOHNSON        

Medtronic Plc

F. Hoffmann-La Roche Ag

Glaxosmithkline Plc

Novartis Ag

Eli Lilly And Company

Astrazeneca Plc

Merck & Co., Inc.

Pfizer Inc.

Sanofi S.A.

Johnson & Johnson