Summary

The global AI in Oncology market is projected to reach USD 11.52 billion by 2030 from USD 2.45 billion in 2024, at a CAGR of 29.4% from 2024 to 2030. The market's growth is fuelled by the growing demand for cost-effective cancer treatments & solutions, streamlining of the drug discovery process, rapid digitization of healthcare records and patient data, the growing volume of cancer cases, and regulatory compliance requirements.
In March 2024, the journal published by the American Cancer Society stated the following key points:
• More than 80% of AI devices that are FDA-approved are used in cancer detection & diagnosis. These devices have applications in the following: pathology (19.7%), radiology (54.9), and radiation oncology (8.5%).
• AI aided in decreasing the workload of radiologists in breast cancer screening by 30% and in comparison to healthcare professionals, AI maintained more accuracy.
• AI combined with human evaluations improved cancer detection rates by 8% in various studies.
• Precision medicine tools powered by AI contributed to the 33% decline in cancer mortality rates over the past 32 years by enabling better diagnoses, tailored treatments, and optimized clinical decision-making.
However, integration with existing healthcare systems, data privacy, and security constraints pose a significant challenge within this market.
“Machine learning held the largest share in technology type in the AI in oncology market in 2023.”
The AI in oncology market is segmented based on technology into machine learning, natural language processing (NLP), context-aware processing and computing, computer vision, and image analysis (including optical character recognition). The machine learning segment held the largest market share in 2023. Further, the machine learning segment includes deep learning (including convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), graph neural networks (GNN), others), supervised learning, reinforcement learning, unsupervised learning, other machine learning technologies. Among these, deep learning is the largest segment owing to its capability to analyze and process vast and complex datasets including medical images with improved efficiency. Within deep learning technologies such as CNNs are effective for image-based cancer detection, while RNNs and GANs are used to improve the temporal pattern analysis and data synthesis. Moreover, deep learning's scalability, adaptability and precision in analyzing and identifying the subtle patterns in cancer helped in improving the diagnosis, risk predictions and treatment optimization.
“By player type, the integrated solution segment is the largest and is also expected to register the fastest growth over the forecast period.”
By player type, the AI in oncology market is divided into niche/point solution providers (including platform & service), integrated suite/platform providers (including platform & service), technology providers (only software), and business process service providers. The integrated suite/platform providers segment accounts for the largest and is projected to be the fastest-growing segment over the forecast year. “By player type, the integrated solution segment is the largest and is also expected to register the fastest growth over the forecast period.” The growth is attributed to the fact that these providers offer comprehensive end-to-end solutions to streamline workflows across all treatment sectors of cancer such as detection, diagnosis, monitoring, and treatment planning. Such platforms help to integrate technologies including NLP, computer vision, and machine learning resulting in better clinical decision-making and offering seamless data interoperability.
Moreover, integrated suite/platform helps in decreasing the need for multiple vendors as they are unified systems due to their scalability and flexibility which results in cost effective solution. This holistic approach drives adoption and fuels rapid growth.
“Asia Pacific is estimated to register the highest CAGR over the forecast period.”
The AI in Oncology market is segmented mainly into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. The AI in oncology market in Asia Pacific is projected to register at the highest CAGR rate during the forecast period. The growth of this region is due to the development of healthcare infrastructure, and government initiatives to modernize and digitalize the healthcare industry particularly due to rising cancer cases, growth in minimally invasive cancer treatments, and to increase in the survival rate of cancer patients. Countries such as Japan, China, and India are focusing on developing cost-effective solutions in cancer care emphasizing the importance of AI-driven data management to handle sensitive patient information and ensure compliance with regulatory mandates for healthcare data standardization. Various key players and startups in the countries are promoting AI use in cancer such as Niramai, a Bangalore-based health tech startup, developed Thermalytix, an AI-driven breast cancer screening solution. The technology uses non-invasive, radiation-free thermal imaging and machine learning algorithms to detect breast cancer at an earlier stage compared to traditional methods. The solution is designed for all ages and ensures privacy, portability, and high accuracy. It is available in over 30 cities across 200+ hospitals in India and is expanding globally to different countries, thereby, transforming preventive cancer care.

Breakdown of supply-side primary interviews by company type, designation, and region:
• By Company Type: Tier 1 (40%), Tier 2 (35%), and Tier 3 (25%)
• By Designation: Directors (35%), Managers (40%), and Others (25%)
• By Region: North America (40%), Europe (30%), Asia Pacific (20%), Latin America (5%) and Middle East Africa (5%)
List of Companies Profiled in the Report
o Certara USA. (US)
o Siemens Healthineers (Germany)
o GE Healthcare (US)
o ConcertAI (US)
o Medtronic (Ireland)
o F. Hoffmann-La Roche Ltd (Switzerland)
o Oracle(US)
o NVIDIA Corporation(US)
o Koninklijke Philips N.V. (Netherlands)
o PathAI, Inc. (US)
o CureMetrix, Inc. (US)
o Mindpeak GmbH (Germany)
o Paige AI, Inc. (US)
o Predictive Oncology (US)
o Exscientia (UK)
o Insilico Medicine (US)
o Iktos (Paris)
o Tempus (US)
o Azra AI (US)
o CureMatch, Inc. (US)
o OncoLens (US)
o Triomics (US)
o Clinakos. (US)
o Perthera, Inc (US)
o Cellworks Group, Inc. (US)
o biomy, Inc. (Japan)

Research Coverage
This research report categorizes the AI in oncology market by player type [niche/point solution providers (including platform & service), integrated suite/platform providers (including platform & service), technology providers (only software), and business process service providers], by application [drug discovery {target identification & validation, lead identification & optimization, de novo drug design}, drug development {preclinical testing, predictive modeling for human trials, clinical trial optimization, adaptive trial design & monitoring}, diagnosis & early detection {imaging & radiology (mammography, computed tomography, magnetic resonance imaging (MRI), nuclear imaging (PET & SPECT), X-ray imaging, ultrasound, others), digital pathology & histopathology, liquid biopsy & biomarker detection, genetic risk prediction}, treatment planning & personalization {personalized treatment planning (precision medicine & genomic analysis, radiomics and radiogenomics, predictive models for treatment response, treatment recommendation systems), radiation therapy, chemotherapy, immunotherapy, targeted therapy (combination & dose optimization, AI-guided drug delivery), surgical planning & assistance (preoperative imaging and 3D modeling, intraoperative guidance and robotics, postoperative analysis & recovery)}, patient engagement & remote monitoring {symptom management & virtual assistance, remote patient monitoring, patient education & empowerment}, post-treatment surveillance & survivorship care {recurrence monitoring, long-term outcome prediction, mental health & support systems}, data management & analytics, other applications, by cancer type (solid tumors [including breast cancer lung cancer, prostate cancer, colorectal cancer, brain tumors, and other tumors], hematologic malignancies (including leukemia, lymphoma, multiple myeloma, other hematologic malignancies), by technology [machine learning {deep learning (convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), graph neural networks (GNN), others), supervised learning, reinforcement learning, unsupervised learning, other machine learning technologies}, natural language processing (NLP), context-aware processing and computing, computer vision, image analysis (including optical character recognition)], by deployment [on-premises model, cloud-based model, and hybrid model], by end user [healthcare providers {hospitals & clinics, specialty centers, laboratories & diagnostic centers, others}, pharmaceutical & biotechnology companies, medical device/equipment companies, academic & research institutions, government & regulatory agencies, healthcare payers, and others}, and region. The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI in oncology market. A thorough analysis of the key industry players has been done to provide insights into their business overview, offerings, and key strategies such as acquisitions, collaborations, partnerships, mergers, product/service launches & enhancements, and approvals in the AI in oncology market. Competitive analysis of upcoming startups in the AI in oncology market ecosystem is covered in this report.
Reasons to Buy the Report
The report will help market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in oncology market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to better position their businesses and plan suitable go-to-market strategies. The report also helps stakeholders understand the market pulse and provides information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (supportive regulations, growing necessity to reduce healthcare costs, reduction in costs and improved operational efficiency with AI in oncology platforms, rising demand for streamlined clinical trials, technological advancements in AI algorithms, rising cancer prevalence globally), restraints (ensuring data security is a major concern for both patients and users, elevated costs associated with adoption of AI, resistance to adoption), opportunities (focus on personalized treatment plans, collaborative efforts, AI-driven drug discovery), and challenges (limited availability of datasets, interoperability issues) influencing the growth of the AI in oncology market
• Solution Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in oncology market
• Market Development: Comprehensive information about lucrative markets – the report analyses the AI in oncology market across varied regions.
• Market Diversification: Exhaustive information about new solutions, untapped geographies, recent developments, and investments in the AI in oncology market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as Siemens Healthineers (Germany), GE Healthcare (US), ConcertAI (US), Medtronic (Ireland), F. Hoffmann-La Roche Ltd (Switzerland), Oracle(US), NVIDIA Corporation(US), Koninklijke Philips N.V. (Netherlands), PathAI, Inc. (US), CureMetrix, Inc. (US), Mindpeak GmbH (Germany), Paige AI, Inc. (US), Predictive Oncology (US), Exscientia (UK), and Insilico Medicine (US), among others in AI in oncology market.

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Table of Contents

1 INTRODUCTION 46
1.1 STUDY OBJECTIVES 46
1.2 MARKET DEFINITION 46
1.3 STUDY SCOPE 47
1.3.1 MARKET SEGMENTATION AND GEOGRAPHIC SPREAD 47
1.3.2 INCLUSIONS AND EXCLUSIONS 48
1.3.3 YEARS CONSIDERED 51
1.4 CURRENCY CONSIDERED 51
1.5 STAKEHOLDERS 52
2 RESEARCH METHODOLOGY 53
2.1 RESEARCH DATA 53
2.1.1 SECONDARY DATA 54
2.1.1.1 Key data from secondary sources 55
2.1.2 PRIMARY DATA 55
2.1.2.1 Primary sources 56
2.1.2.1.1 Key data from primary sources 57
2.1.2.1.2 Key industry insights 58
2.1.2.2 Breakdown of primary interviews 58
2.2 MARKET ESTIMATION METHODOLOGY 59
2.3 MARKET SIZE ESTIMATION 60
2.4 MARKET BREAKDOWN AND DATA TRIANGULATION 68
2.5 RESEARCH ASSUMPTIONS 69
2.5.1 MARKET SIZING ASSUMPTIONS 69
2.5.2 OVERALL STUDY ASSUMPTIONS 69
2.6 RISK ASSESSMENT 70
2.7 RESEARCH LIMITATIONS 70
2.7.1 METHODOLOGY-RELATED LIMITATIONS 70
2.7.2 SCOPE-RELATED LIMITATIONS 70
3 EXECUTIVE SUMMARY 71
4 PREMIUM INSIGHTS 76
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS AI IN ONCOLOGY MARKET 76
4.2 AI IN ONCOLOGY MARKET, BY REGION 77
4.3 NORTH AMERICA: AI IN ONCOLOGY MARKET, BY DEPLOYMENT MODEL AND COUNTRY 77
4.4 AI IN ONCOLOGY MARKET, BY COUNTRY 78
4.5 AI IN ONCOLOGY MARKET: DEVELOPED MARKETS VS. EMERGING MARKETS 78
5 MARKET OVERVIEW 79
5.1 INTRODUCTION 79
5.2 MARKET DYNAMICS 79
5.2.1 DRIVERS 80
5.2.1.1 Increasing incidence of cancer disease 80
5.2.1.2 Growing need for early detection and diagnosis 80
5.2.1.3 Advancements in precision cancer treatment 81
5.2.1.4 Support from regulatory authorities 81
5.2.1.5 Increasing investments and funding 82
5.2.2 RESTRAINTS 83
5.2.2.1 High initial costs 83
5.2.2.2 Data integrity and algorithm validation 83
5.2.2.3 Integration with existing systems 83
5.2.3 OPPORTUNITIES 84
5.2.3.1 Radiomics and imaging analysis 84
5.2.3.2 Clinical trial optimization 86
5.2.3.3 Personalized treatment plans 86
5.2.3.4 Integration of multi-omics data 87
5.2.4 CHALLENGES 87
5.2.4.1 Limited availability of datasets 87
5.2.4.2 Data privacy and security 88
5.3 ECOSYSTEM ANALYSIS 89
5.4 CASE STUDY ANALYSIS 91
5.4.1 SIEMENS HEALTHINEERS IMPLEMENTED SYNGO.VIA RT IMAGE SUITE POWERED BY NVIDIA GPU-BASED SHERLOCK AI SUPERCOMPUTER 91
5.4.2 AI IN ONCOLOGY FOR PERSONALIZED TREATMENT PLANNING 91
5.4.3 PERSONALIZED OUTREACH FOR ONCOLOGISTS WITH TAKEDA'S AI SOLUTION 92
5.5 VALUE CHAIN ANALYSIS 93
5.6 PORTER'S FIVE FORCES ANALYSIS 95
5.6.1 BARGAINING POWER OF SUPPLIERS 96
5.6.2 BARGAINING POWER OF BUYERS 96
5.6.3 THREAT OF SUBSTITUTES 96
5.6.4 THREAT OF NEW ENTRANTS 97
5.6.5 INTENSITY OF COMPETITIVE RIVALRY 97
5.7 REGULATORY LANDSCAPE 97
5.7.1 NORTH AMERICA 97
5.7.2 EUROPE 98
5.7.3 ASIA PACIFIC 99
5.7.4 MIDDLE EAST & AFRICA 100
5.7.5 LATIN AMERICA 100
5.7.6 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 100
5.8 PATENT ANALYSIS 103
5.8.1 PATENT PUBLICATION TRENDS FOR AI IN ONCOLOGY 103
5.8.2 JURISDICTION ANALYSIS 104
5.8.3 MAJOR PATENTS IN AI IN ONCOLOGY MARKET 105
5.9 TECHNOLOGY ANALYSIS 106
5.9.1 KEY TECHNOLOGIES 106
5.9.1.1 Machine learning 106
5.9.1.2 Natural language processing 106
5.9.1.3 Computer vision 106
5.9.2 COMPLEMENTARY TECHNOLOGIES 107
5.9.2.1 High-performance computing 107
5.9.2.2 Next-generation sequencing 107
5.9.2.3 Digital twins 107
5.9.2.4 Real-world evidence/real-world data 107
5.9.3 ADJACENT TECHNOLOGIES 107
5.9.3.1 Cloud computing 107
5.9.3.2 Theranostics 108
5.9.3.3 Augmented and virtual reality 108
5.10 INDUSTRY TRENDS 108
5.10.1 SHIFT TOWARD PERSONALIZED ONCOLOGY 108
5.10.2 EXPANSION OF AI-BASED CLINICAL TRIALS 108
5.11 PRICING ANALYSIS 109
5.11.1 INDICATIVE PRICING OF AI IN ONCOLOGY SOFTWARE,
BY DEPLOYMENT MODEL 109
5.11.2 AVERAGE SELLING PRICE OF AI IN ONCOLOGY PLATFORMS,
BY REGION (2023) 109
5.12 KEY CONFERENCES AND EVENTS, 2025 110
5.13 KEY STAKEHOLDERS AND BUYING CRITERIA 111
5.13.1 KEY STAKEHOLDERS 111
5.13.2 BUYING CRITERIA 112
5.14 TRENDS AND DISRUPTIONS IMPACTING CUSTOMER BUSINESS 113
5.15 END USER ANALYSIS 113
5.15.1 UNMET NEEDS 113
5.15.2 END USER EXPECTATIONS 114
5.16 INVESTMENT AND FUNDING SCENARIO 114
5.17 IMPACT OF GENERATIVE AI ON AI IN ONCOLOGY MARKET 115
5.17.1 KEY USE CASES 116
5.17.2 CASE STUDIES OF GENERATIVE AI IMPLEMENTATION 116
5.17.2.1 Case Study 1: Accelerated drug discovery with Generative AI and streamlined workflows 116
 
5.17.3 IMPACT OF GENERATIVE AI ON INTERCONNECTED AND ADJACENT ECOSYSTEMS 117
5.17.3.1 Pharmaceutical research and development market 117
5.17.3.2 Radiology and medical imaging market 117
5.17.3.3 Healthcare delivery systems market 118
5.17.4 USER READINESS AND IMPACT ASSESSMENT 118
5.17.4.1 User readiness 118
5.17.4.1.1 Use A: Healthcare providers 118
5.17.4.1.2 User B: Pharmaceutical & biotechnology companies 118
5.17.4.2 Impact assessment 118
5.17.4.2.1 User A: Healthcare providers 118
5.17.4.2.2 User B: Pharmaceutical & biotechnology companies 119
6 AI IN ONCOLOGY MARKET, BY TECHNOLOGY 120
6.1 INTRODUCTION 121
6.2 MACHINE LEARNING 121
6.2.1 DEEP LEARNING 124
6.2.1.1 Need to streamline clinical workflows, reduce delays, and improve patient outcomes to drive market 124
6.2.1.2 Convolutional neural networks 125
6.2.1.3 Recurrent neural networks 126
6.2.1.4 Generative adversarial networks 126
6.2.1.5 Graph neural networks 126
6.2.1.6 Others 126
6.2.2 SUPERVISED LEARNING 127
6.2.2.1 Surge in demand for accurate predictions and tailored treatments to drive market 127
6.2.3 REINFORCEMENT LEARNING 128
6.2.3.1 Extensive use in drug discovery to drive market 128
6.2.4 UNSUPERVISED LEARNING 129
6.2.4.1 Ability to perform complex tasks and uncover potential drug candidates to drive market 129
6.2.5 OTHER MACHINE LEARNING TECHNOLOGIES 130
6.3 NATURAL LANGUAGE PROCESSING 131
6.3.1 EMERGING DEVELOPMENTS IN ONCOLOGY CARE TO DRIVE MARKET 131
6.4 CONTEXT-AWARE PROCESSING AND COMPUTING 132
6.4.1 ABILITY TO OPTIMIZE CLINICAL WORKFLOWS TO DRIVE MARKET 132
6.5 COMPUTER VISION 133
6.5.1 ELEVATED DEMAND FOR PRECISION MEDICINE TO DRIVE MARKET 133
6.6 IMAGE ANALYSIS 134
6.6.1 AUTOMATION OF COMPLEX IMAGING TASKS TO DRIVE MARKET 134
 
7 AI IN ONCOLOGY MARKET, BY APPLICATION 136
7.1 INTRODUCTION 137
7.2 DRUG DISCOVERY 138
7.2.1 TARGET IDENTIFICATION & VALIDATION 139
7.2.1.1 Emphasis on avoiding last-stage failure in drug discovery to boost growth 139
7.2.2 HIT IDENTIFICATION & PRIORITIZATION 141
7.2.2.1 Need for large-scale data analysis in HTS screening to drive adoption 141
7.2.3 HIT-TO-LEAD IDENTIFICATION/LEAD GENERATION 142
7.2.3.1 AI-driven lead generation to improve selectivity and binding mechanisms 142
7.2.4 LEAD OPTIMIZATION 143
7.2.4.1 Need to accelerate make-design-test cycles and high possibility of clinical drug failure to spur market 143
7.2.5 CANDIDATE SELECTION & VALIDATION 144
7.2.5.1 Candidate selection and validation to facilitate early drug discovery 144
7.3 DRUG DEVELOPMENT 145
7.3.1 PRECLINICAL TESTING 147
7.3.1.1 Need to identify risks and optimize candidates to boost growth 147
7.3.2 PREDICTIVE MODELING FOR HUMAN TRIALS 148
7.3.2.1 Need for leveraging AI for accurate dose selection and safety assessments to boost growth 148
7.3.3 CLINICAL TRIAL OPTIMIZATION 149
7.3.3.1 Need to enhance trial efficiency and outcomes with AI-driven insights to propel market 149
7.3.4 ADAPTIVE TRIAL DESIGN & MONITORING 150
7.3.4.1 AI-driven adaptive trial design & monitoring help improve flexibility and success rates 150
7.4 DIAGNOSIS & EARLY DETECTION 151
7.4.1 IMAGING & RADIOLOGY 152
7.4.1.1 Mammography 154
7.4.1.1.1 Need for accurate diagnosis of breast cancer to propel market 154
7.4.1.2 Computed tomography (CT) 154
7.4.1.2.1 Need for early diagnosis of solid tumors in lungs, liver, and brain to drive growth 154
7.4.1.3 Magnetic resonance imaging (MRI) 155
7.4.1.3.1 Need for optimizing imaging and enhancing tumor detection by integrating AI into MRI to propel demand 155
7.4.1.4 Nuclear imaging 156
7.4.1.4.1 Need for empowering AI-enhanced PET and SPECT imaging for precision oncology to drive growth 156
7.4.1.5 X-ray Imaging 157
7.4.1.5.1 Integrating AI-powered X-rays to automate detection of lung nodules to boost market 157
7.4.1.6 Ultrasound 158
7.4.1.6.1 Focus on integrating AI with ultrasound imaging to boost growth 158
7.4.1.7 Other imaging modalities 159
7.4.2 DIGITAL PATHOLOGY & HISTOPATHOLOGY 160
7.4.2.1 Focus on examining tissue samples to diagnose diseases to boost market 160
7.4.3 LIQUID BIOPSY & BIOMARKER DETECTION 161
7.4.3.1 Advancements in non-invasive diagnostic technologies to propel growth 161
7.4.4 GENETIC RISK PREDICTION 162
7.4.4.1 Increased awareness of people regarding hereditary cancer risk to encourage growth 162
7.5 TREATMENT PLANNING & PERSONALIZATION 163
7.5.1 PERSONALIZED TREATMENT PLANNING 164
7.5.1.1 Precision medicine & genomic analysis 166
7.5.1.1.1 Need for adopting personalized therapies to improve treatment response to boost growth 166
7.5.1.2 Radiomics & radiogenomics 167
7.5.1.2.1 Emphasis on optimizing radiomics and radiogenomics for disease characterization to propel demand 167
7.5.1.3 Predictive models for treatment response 168
7.5.1.3.1 Adoption of predictive modeling to analyze genetic information to improve growth 168
7.5.1.4 Treatment recommendation systems 168
7.5.1.4.1 Need for enhancing treatment decisions with data-driven insights to propel growth 168
7.5.2 RADIATION THERAPY 169
7.5.2.1 Need for effective tumor targeting to boost growth 169
7.5.3 CHEMOTHERAPY 170
7.5.3.1 Focus on optimizing chemotherapy for targeted treatment and risk prediction to boost segmental growth 170
7.5.4 IMMUNOTHERAPY 171
7.5.4.1 Use of immunotherapy for personalized and effective cancer care to boost growth 171
7.5.5 TARGETED THERAPY 172
7.5.5.1 Combination & dose optimization 173
7.5.5.1.1 Need for enhancing personalized dosing to augment segment growth 173
7.5.5.2 AI-guided drug delivery 174
7.5.5.2.1 Emphasis on achieving robust AI-powered drug delivery system to drive market 174
7.5.6 SURGICAL PLANNING & ASSISTANCE 175
7.5.6.1 Preoperative imaging & 3D modeling 176
7.5.6.1.1 AI-driven 3D models for enhanced oncology care 176
 
7.5.6.2 Intraoperative guidance and robotics 177
7.5.6.2.1 Focus on integrating robotic surgery to enhance precision in treatment to drive market 177
7.5.6.3 Postoperative analysis & recovery 178
7.5.6.3.1 Emphasis on enhancing AI in postoperative care to drive demand 178
7.6 PATIENT ENGAGEMENT & REMOTE MONITORING 179
7.6.1 SYMPTOM MANAGEMENT & VIRTUAL ASSISTANCE 180
7.6.1.1 Symptom management & virtual assistance tools are beneficial for chronic disease management 180
7.6.2 REMOTE PATIENT MONITORING 181
7.6.2.1 Need for AI-enhanced, real-time monitoring to augment growth 181
7.6.3 PATIENT EDUCATION & EMPOWERMENT 182
7.6.3.1 Improved health literacy and engagement with AI-curated insights 182
7.7 POST-TREATMENT SURVEILLANCE & SURVIVORSHIP CARE 183
7.7.1 RECURRENCE MONITORING 184
7.7.1.1 Need to improve cancer surveillance and accurate recurrence detection and prognosis to drive market 184
7.7.2 LONG-TERM OUTCOME PREDICTION 186
7.7.2.1 Need for personalized care plans and chronic side-effect management to augment market 186
7.7.3 MENTAL HEALTH & SUPPORT SYSTEMS 187
7.7.3.1 Prioritizing mental health support in cancer care to augment segmental growth 187
7.8 DATA MANAGEMENT & ANALYTICS 188
7.8.1 INTEGRATION OF GENOMIC AND CLINICAL DATA TO ACCELERATE DEMAND FOR AI-POWERED ANALYTICS 188
7.9 OTHER APPLICATIONS 189
8 AI IN ONCOLOGY MARKET, BY CANCER TYPE 190
8.1 INTRODUCTION 191
8.2 SOLID TUMORS 191
8.2.1 RISING PREVALENCE OF SOLID TUMORS TO BOOST NEED FOR AI-DRIVEN INNOVATIONS 191
8.2.2 BREAST CANCER 193
8.2.3 LUNG CANCER 194
8.2.4 PROSTATE CANCER 195
8.2.5 COLORECTAL CANCER 196
8.2.6 BRAIN TUMOR 197
8.2.7 OTHER SOLID TUMORS 198
8.3 HEMATOLOGIC MALIGNANCIES 199
8.3.1 RISING CASES OF BLOOD CANCER TO DRIVE MARKET 199
8.3.2 LEUKEMIA 201
8.3.3 LYMPHOMA 202
8.3.4 MULTIPLE MYELOMA 203
8.3.5 OTHER HEMATOLOGIC MALIGNANCIES 204
8.4 OTHER CANCER TYPES 205
9 AI IN ONCOLOGY MARKET, BY END USER 206
9.1 INTRODUCTION 207
9.2 HEALTHCARE PROVIDERS 207
9.2.1 NEED FOR IMPROVED DIAGNOSTIC ACCURACY, PERSONALIZED TREATMENT PLANNING, AND ENHANCED WORKFLOW EFFICIENCY TO BOOST MARKET 207
9.2.2 HOSPITALS & CLINICS 209
9.2.3 SPECIALTY CENTERS 210
9.2.4 LABORATORIES & DIAGNOSTIC CENTERS 211
9.2.5 OTHER HEALTHCARE PROVIDERS 212
9.3 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES 213
9.3.1 NEED TO LEVERAGE AI FOR ACCELERATED ONCOLOGY DRUG DISCOVERY AND CLINICAL TRIALS TO BOOST GROWTH 213
9.4 MEDICAL DEVICE/ EQUIPMENT COMPANIES 214
9.5 ACADEMIC & RESEARCH INSTITUTIONS 216
9.6 GOVERNMENT & REGULATORY AGENCIES 217
9.7 HEALTHCARE PAYERS 218
9.8 OTHER END USERS 219
10 AI IN ONCOLOGY MARKET, BY PLAYER TYPE 221
10.1 INTRODUCTION 222
10.2 NICHE/POINT SOLUTION PROVIDERS 222
10.2.1 NICHE/POINT SOLUTION PROVIDERS ACCELERATE CANCER DRUG DISCOVERY AND DEVELOPMENT 222
10.3 INTEGRATED SUITE/PLATFORM PROVIDERS 224
10.3.1 INTEGRATED SUITE/PLATFORM PROVIDERS REDUCE NEED FOR MULTIPLE VENDORS AND ACCELERATE WORKFLOWS 224
10.4 TECHNOLOGY PROVIDERS 225
10.4.1 DEMAND FOR IMPROVED ONCOLOGY WORKFLOWS TO DRIVE MARKET 225
10.5 BUSINESS PROCESS SERVICE PROVIDERS 227
10.5.1 FOCUS ON OPTIMIZING NON-CLINICAL ONCOLOGY WORKFLOWS TO PROPEL MARKET GROWTH 227
11 AI IN ONCOLOGY MARKET, BY DEPLOYMENT MODEL 228
11.1 INTRODUCTION 229
11.2 CLOUD-BASED MODEL 229
11.2.1 NEED FOR ADVANCED CANCER RESEARCH AND TREATMENT TO BOOST USE OF CLOUD-BASED AI PLATFORMS 229
11.3 ON-PREMISES MODEL 231
11.3.1 NEED FOR ENHANCED DATA SECURITY AND COMPLIANCE TO PROPEL ADOPTION OF ON-PREMISES MODEL 231
11.4 HYBRID MODEL 232
11.4.1 NEED FOR ENHANCING SCALABILITY AND DATA SECURITY IN DIAGNOSTICS TO DRIVE USE OF HYBRID-BASED AI PLATFORMS 232
12 AI IN ONCOLOGY MARKET, BY REGION 234
12.1 INTRODUCTION 235
12.2 NORTH AMERICA 236
12.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 239
12.2.2 US 249
12.2.2.1 Rising number of clinical trials and drug discovery to drive market 249
12.2.3 CANADA 260
12.2.3.1 Pharmaceutical giants advancing innovation and expanding access to clinical trials to fuel market 260
12.3 EUROPE 271
12.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 272
12.3.2 GERMANY 283
12.3.2.1 Advanced healthcare system and collaborative efforts to boost market 283
12.3.3 UK 293
12.3.3.1 Government support for developing new AI platforms to drive innovation 293
12.3.4 FRANCE 304
12.3.4.1 Growing R&D pipeline for oncology trials to drive market 304
12.3.5 ITALY 314
12.3.5.1 Favorable regulatory scenarios to propel AI adoption in oncology 314
12.3.6 SPAIN 325
12.3.6.1 Established network of research centers to propel market 325
12.3.7 REST OF EUROPE 336
12.4 ASIA PACIFIC 346
12.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 347
12.4.2 CHINA 359
12.4.2.1 Increasing healthcare expenditure to drive demand for oncology solutions 359
12.4.3 INDIA 369
12.4.3.1 Growing cancer burden and healthcare disparities to fuel adoption of AI in oncology 369
12.4.4 JAPAN 380
12.4.4.1 Aging population and rising cancer rates to drive growth 380
12.4.5 REST OF ASIA PACIFIC 390
12.5 LATIN AMERICA 401
12.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 402
12.5.2 BRAZIL 412
12.5.2.1 Rising cases of breast cancer to support market growth 412
 
12.5.3 MEXICO 422
12.5.3.1 Use of AI in pediatric cancer treatment and chemotherapy complications to fuel market growth 422
12.5.4 REST OF LATIN AMERICA 432
12.6 MIDDLE EAST & AFRICA 443
12.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 444
12.6.2 GCC COUNTRIES 454
12.6.2.1 Growing cancer cases and increasing clinical trials to drive growth 454
12.6.3 REST OF MIDDLE EAST & AFRICA 465
13 COMPETITIVE LANDSCAPE 476
13.1 INTRODUCTION 476
13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 476
13.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY PLAYERS 477
13.3 REVENUE ANALYSIS OF KEY PLAYERS 478
13.4 MARKET SHARE ANALYSIS 479
13.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 481
13.5.1 STARS 481
13.5.2 EMERGING LEADERS 481
13.5.3 PERVASIVE PLAYERS 481
13.5.4 PARTICIPANTS 481
13.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 483
13.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 488
13.6.1 PROGRESSIVE COMPANIES 488
13.6.2 RESPONSIVE COMPANIES 488
13.6.3 DYNAMIC COMPANIES 488
13.6.4 STARTING BLOCKS 488
13.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 490
13.7 COMPANY VALUATION AND FINANCIAL METRICS 492
13.8 BRAND/SOFTWARE COMPARISON 493
13.9 COMPETITIVE SCENARIO 493
13.9.1 PRODUCT LAUNCHES & ENHANCEMENTS 493
13.9.2 DEALS 494
13.9.3 EXPANSIONS 495
13.9.4 OTHER DEVELOPMENTS 495
14 COMPANY PROFILES 496
14.1 KEY PLAYERS 496
14.1.1 NVIDIA CORPORATION 496
14.1.1.1 Business overview 496
14.1.1.2 Products/Solutions offered 497
 
14.1.1.3 Recent developments 498
14.1.1.3.1 Deals 498
14.1.1.4 MnM view 498
14.1.1.4.1 Right to win 498
14.1.1.4.2 Strategic choices 499
14.1.1.4.3 Weaknesses and competitive threats 499
14.1.2 GE HEALTHCARE 500
14.1.2.1 Business overview 500
14.1.2.2 Products/Solutions offered 501
14.1.2.3 Recent developments 502
14.1.2.3.1 Product launches & approvals 502
14.1.2.3.2 Deals 502
14.1.2.4 MnM view 503
14.1.2.4.1 Right to win 503
14.1.2.4.2 Strategic choices 503
14.1.2.4.3 Weaknesses and competitive threats 503
14.1.3 SIEMENS HEALTHINEERS AG 504
14.1.3.1 Business overview 504
14.1.3.2 Products/Solutions offered 505
14.1.3.3 Recent developments 506
14.1.3.3.1 Product launches & approvals 506
14.1.3.3.2 Deals 506
14.1.3.3.3 Expansions 506
14.1.3.4 MnM view 507
14.1.3.4.1 Right to win 507
14.1.3.4.2 Strategic choices 507
14.1.3.4.3 Weaknesses and competitive threats 507
14.1.4 F. HOFFMANN-LA ROCHE LTD 508
14.1.4.1 Business overview 508
14.1.4.2 Products/Solutions offered 509
14.1.4.3 Recent developments 510
14.1.4.3.1 Product launches & approvals 510
14.1.4.3.2 Deals 510
14.1.4.4 MnM view 511
14.1.4.4.1 Right to win 511
14.1.4.4.2 Strategic choices 511
14.1.4.4.3 Weaknesses and competitive threats 511
14.1.5 INSILICO MEDICINE 512
14.1.5.1 Business overview 512
14.1.5.2 Products/Solutions offered 512
14.1.5.3 Recent developments 513
 
14.1.5.4 MnM view 520
14.1.5.4.1 Right to win 520
14.1.5.4.2 Strategic choices 520
14.1.5.4.3 Weaknesses and competitive threats 520
14.1.6 CONCERTAI 521
14.1.6.1 Business overview 521
14.1.6.2 Products/Solutions offered 521
14.1.6.3 Recent developments 522
14.1.6.3.1 Product launches & approvals 522
14.1.6.3.2 Deals 522
14.1.7 MEDTRONIC 523
14.1.7.1 Business overview 523
14.1.7.2 Products/Solutions offered 524
14.1.7.3 Recent developments 525
14.1.7.3.1 Product launches & approvals 525
14.1.7.3.2 Deals 525
14.1.8 ORACLE 526
14.1.8.1 Business overview 526
14.1.8.2 Products/Solutions offered 527
14.1.8.3 Recent developments 528
14.1.8.3.1 Product launches & approvals 528
14.1.8.3.2 Deals 528
14.1.9 KONINKLIJKE PHILIPS N.V. 529
14.1.9.1 Business overview 529
14.1.9.2 Products/Solutions offered 530
14.1.9.3 Recent developments 531
14.1.9.3.1 Deals 531
14.1.10 PREDICTIVE ONCOLOGY 532
14.1.10.1 Business overview 532
14.1.10.2 Products/Solutions offered 533
14.1.10.3 Recent developments 533
14.1.10.3.1 Product launches & approvals 533
14.1.10.3.2 Deals 533
14.1.11 EXSCIENTIA 534
14.1.11.1 Business overview 534
14.1.11.2 Products/Solutions offered 535
14.1.11.3 Recent developments 536
14.1.11.3.1 Product launches & approvals 536
14.1.11.3.2 Deals 536
14.1.11.3.3 Expansions 541
14.1.11.3.4 Other developments 542
 
14.1.12 PATHAI, INC. 543
14.1.12.1 Business overview 543
14.1.12.2 Products/Solutions offered 543
14.1.12.3 Recent developments 544
14.1.12.3.1 Product launches & approvals 544
14.1.12.3.2 Deals 544
14.1.13 CUREMETRIX, INC. 545
14.1.13.1 Business overview 545
14.1.13.2 Products/Solutions offered 545
14.1.13.3 Recent developments 545
14.1.13.3.1 Other developments 545
14.1.14 MINDPEAK GMBH 546
14.1.14.1 Business overview 546
14.1.14.2 Products/Solutions offered 546
14.1.14.3 Recent developments 547
14.1.14.3.1 Product launches & approvals 547
14.1.14.3.2 Deals 547
14.1.14.3.3 Other developments 547
14.1.15 PAIGE AI, INC. 548
14.1.15.1 Business overview 548
14.1.15.2 Products/Solutions offered 548
14.1.15.3 Recent developments 549
14.1.15.3.1 Product launches & approvals 549
14.1.15.3.2 Deals 549
14.1.15.3.3 Other developments 550
14.1.16 TEMPUS AI, INC. 551
14.1.16.1 Business overview 551
14.1.16.2 Products/Solutions offered 551
14.1.16.3 Recent developments 552
14.1.16.3.1 Product launches & approvals 552
14.1.16.3.2 Deals 553
14.1.16.3.3 Expansions 555
14.1.16.3.4 Other developments 556
14.1.17 IKTOS 557
14.1.17.1 Business overview 557
14.1.17.2 Products/Solutions offered 557
14.1.17.3 Recent developments 558
14.1.17.3.1 Deals 558
14.1.17.3.2 Other developments 561
 
14.2 OTHER PLAYERS 562
14.2.1 AZRA AI 562
14.2.2 CUREMATCH, INC. 563
14.2.3 ONCOLENS 563
14.2.4 TRIOMICS 564
14.2.5 CLINAKOS 565
14.2.6 PERTHERA, INC. 566
14.2.7 CELLWORKS GROUP, INC. 566
14.2.8 BIOMY, INC. 567
15 APPENDIX 568
15.1 DISCUSSION GUIDE 568
15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 577
15.3 CUSTOMIZATION OPTIONS 579
15.4 RELATED REPORTS 579
15.5 AUTHOR DETAILS 580