Report Overview

According to the deep-dive market assessment study by Growth Plus Reports, the global AI in oncology market was valued at US$ 850.72 million in 2022 and is expected to register a revenue CAGR of 33% to reach US$ 11077.75 million by 2031.

AI In Oncology Market Fundamentals

AI in oncology refers to the utilization of artificial intelligence (AI) technologies and techniques in the field of oncology, which is focused on the study and treatment of cancer. AI in oncology involves the development and application of algorithms and computational models that can analyze and interpret complex medical data, including imaging scans, genetic profiles, clinical records, and research literature. These AI systems aim to assist healthcare professionals in tasks such as cancer detection, diagnosis, treatment planning, prognostication, and personalized therapy recommendations. By leveraging machine learning, deep learning, natural language processing, and other AI techniques, AI in oncology has the potential to improve cancer care and outcomes by providing accurate and timely insights, supporting clinical decision-making, and enabling more precise and targeted interventions.

AI In Oncology Market Dynamics

The prevalence of cancer is increasing worldwide, which is generating vast amounts of data from various sources, including medical imaging, genomic sequencing, electronic health records, and scientific literature. AI can analyze and interpret this data more efficiently and accurately than traditional methods, enabling healthcare professionals to derive meaningful insights and make informed decisions. According to Cancer Research UK, approximately 18 million cancer cases were detected in 2020. AI algorithms have demonstrated the potential to improve the accuracy of cancer detection and diagnosis. They can analyze medical images, such as mammograms or pathology slides, to identify subtle patterns or anomalies that may be indicative of cancer. By assisting radiologists and pathologists in their assessments, AI can help reduce diagnostic errors and enable earlier detection of cancer, which is increasing the AI in oncology market demand. AI algorithms can analyze vast amounts of biomedical and genetic data to identify potential targets for drug development. By simulating and predicting the efficacy and safety of new compounds, AI can accelerate the discovery and development of novel cancer therapies, potentially reducing the time and cost involved in bringing new drugs to market. AI can analyze patient-specific data, including genetic profiles and treatment outcomes, to identify patterns and make personalized treatment recommendations. AI-powered systems can provide real-time clinical decision support to healthcare professionals by analyzing patient data, guidelines, and research literature. This can help clinicians in treatment planning, selecting the most appropriate therapies, predicting treatment response, and monitoring patient progress. AI-based decision support systems can augment clinical expertise and improve patient outcomes, which is also expected to boost the AI in oncology market growth. 

However, AI models in oncology heavily rely on high-quality, diverse, and well-annotated data for training and validation. However, there are challenges in accessing large and representative datasets due to issues of data privacy, data sharing, and data heterogeneity. Incomplete or biased data can lead to suboptimal performance, which is restricting the AI in oncology market growth. The implementation of AI in oncology may involve significant costs related to infrastructure, hardware, software, and expertise. Healthcare organizations may require substantial investments to acquire, maintain, and update AI technologies, which is also hindering the AI in oncology market growth. 

AI In Oncology Market Ecosystem

The global AI in oncology market is analyzed from three perspectives: component, application, and region. 

AI In Oncology Market by Component

Based on the components, the global AI in oncology market is segmented into software solutions, hardware, and services.

The software solutions segment accounted for the largest AI in oncology share in 2022, with a 40% market share. Software solutions offer greater flexibility and scalability compared to hardware-based solutions. AI algorithms and models can be developed and deployed as software applications, making them adaptable to different healthcare settings and easily upgradable as new advancements occur. Software-based solutions can be integrated into existing clinical workflows and accessed remotely, allowing for wider adoption and scalability. AI algorithms in oncology primarily focus on analyzing and interpreting complex medical data, such as medical images, genomic data, and patient records. Software solutions can efficiently process and analyze large volumes of data, enabling accurate and timely insights for healthcare professionals. These solutions can leverage advanced machine learning and deep learning techniques, such as convolutional neural networks (CNNs), to extract valuable information from medical data, aiding in cancer detection, diagnosis, and treatment planning. AI in oncology relies on iterative learning and improvement. Software-based solutions allow for continuous updates and refinement of AI models as new data and knowledge become available. Software solutions can seamlessly integrate into existing healthcare systems, such as electronic health records (EHRs) and picture archiving and communication systems (PACS). This integration enables AI algorithms to access and analyze patient data directly, providing real-time insights and decision support to healthcare professionals without disrupting established workflows.

AI In Oncology Market by Application 

Based on the applications, the global AI in oncology market is segmented into drug discovery, drug development, cancer detection, and other applications.

The cancer detection segment accounted for the highest revenue share of the AI in oncology market in 2022. Early and accurate detection of cancer is crucial for effective treatment and improved patient outcomes. AI algorithms have demonstrated promising results in detecting cancerous lesions in medical imaging data, such as mammograms, CT scans, MRIs, and pathology slides. By assisting radiologists and pathologists in identifying suspicious patterns or abnormalities, AI can enhance the sensitivity and specificity of cancer detection, potentially leading to earlier diagnoses and improved survival rates. Medical imaging generates a significant volume of data in oncology. AI algorithms excel at analyzing and interpreting large-scale imaging datasets, enabling efficient and consistent evaluation of images for potentially cancerous lesions. The advancements in medical imaging technologies, such as high-resolution imaging and 3D imaging modalities, have led to increased complexity and richness in the imaging data. AI algorithms can leverage these advancements to extract intricate patterns and features that may be indicative of cancer, enhancing the accuracy of cancer detection. Diagnostic errors can have significant consequences in oncology. AI algorithms in cancer detection can serve as a valuable second opinion, assisting healthcare professionals in reducing diagnostic errors and increasing confidence in their assessments.

The drug discovery segment is expected to register a lucrative growth rate during the forecasting period from 2023 to 2031. There is a significant need for novel and effective cancer therapies. The traditional drug discovery process is time-consuming, expensive, and often involves a high failure rate. AI-based approaches offer the potential to expedite the drug discovery process and identify new targets and compounds that may have therapeutic potential for cancer treatment. AI algorithms can analyze vast amounts of genomic, proteomic, and clinical data to identify potential therapeutic targets for cancer. By examining patterns, genetic variations, and molecular interactions, AI can identify biomarkers and genetic alterations that may be associated with cancer progression or drug response. The pharmaceutical and biotechnology industry recognizes the potential of AI in drug discovery and has made significant investments in this area. Collaborations and partnerships between AI technology companies and pharmaceutical giants have emerged, combining expertise in AI algorithms with extensive resources, clinical data, and drug development infrastructure. For instance, Insilico Medicine launched Life Star, a 6th generation Intelligent Robotics Drug Discovery Laboratory – in Suzhou BioBAY Industrial Park in December 2022. Target identification, chemical screening, precision medicine creation, and translational research are all performed in the fully automated AI-powered robotics laboratory. The lab will enable Insilico to speed end-to-end drug discovery and optimize drug development success rates as it advances its innovative therapies through clinical trials. AI algorithms can analyze large databases of approved drugs, clinical trials, and scientific literature to identify existing drugs that may have the potential for repurposing in oncology. By leveraging AI-driven knowledge mining, researchers can identify drug candidates that have shown promise in other indications and explore their potential for cancer treatment.

AI In Oncology Market by Region

Geographically, the global AI in oncology market has been segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. 

The North America region has the largest AI in oncology market size in terms of revenue generation accounting for around 56.2% share of the market. North America is known for its strong technological infrastructure and capabilities. The region has witnessed significant advancements in AI technologies, including machine learning, deep learning, and natural language processing. North America benefits from access to large and diverse datasets in oncology, including electronic health records, medical imaging data, genomics data, and clinical trial data. These extensive datasets provide a valuable resource for training and validating AI algorithms in oncology. North America is home to world-renowned research institutions, academic centers, and medical facilities that focus on cancer research and treatment. These institutions have been at the forefront of AI applications in oncology, conducting cutting-edge research and collaborating with industry partners. North America has a robust healthcare infrastructure and high healthcare expenditure. The region's healthcare system encourages innovation and investments in advanced technologies. AI in oncology is seen as a potential tool to improve cancer diagnosis, treatment, and patient outcomes, leading to increased investment and support from healthcare providers and institutions.

AI In Oncology Market Competitive Landscape

The prominent players operating in the global AI in oncology market are:

• Azra AI
• IBM Corporation
• GE HealthCare
• Siemens Healthcare GmbH
• Intel Corporation
• NVIDIA Corporation
• ConcertAI
• Digital Diagnostics Inc.
• PathAI
• Median Technologies

AI In Oncology Market Strategic Developments

• In March 2023, Qritive launched QAi Prostate, an advanced AI-powered prostate cancer diagnosis tool for pathologists. QAi Prostate can accurately identify prostatic adenocarcinoma regions as well as classify malignant and benign tumor areas in biopsy tissue samples by leveraging modern machine learning (ML) algorithms.
• In January 2023, Massive Bio announced the launch of a new AI-powered drug-matching product in 2023 that will enable oncologists to proactively identify more cancer treatment options for their patients, including recently approved drugs as well as active clinical trials. The new drug matching software will also assist biotech and pharmaceutical businesses in better targeting which patients and physicians require the most recent treatments, hence enhancing their go-to-market efficiency. Massive Bio intends to offer its initial drug-matching product in the first half of 2023, with general availability later that year.

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