Predictive Analytics for Cancer Diagnostic Market Research Report Segmented By Application (Breast cancer, Cervical cancer, Lung Cancer, Thyroid Cancer, Prostate Cancer); By tools (Artificial intelligence and Machine learning); and Region- Size, Share, Growth Analysis | Forecast (2024 2030)

Predictive Analytics for Cancer Diagnostic Market Size (2024 – 2030)

The market size of Predictive Analytics for Cancer Diagnostic reached USD 16 billion in 2023 and is anticipated to attain USD 80.67 Billion by the conclusion of 2030. Projected to grow at a CAGR of 26% during the forecast period of 2024-2030.

The healthcare sector generates vast amounts of data daily, commonly referred to as "Big Data." Professionals, including pathologists and medical experts, analyze this extensive data to extract valuable insights, utilizing "Big Data Analytics" on a large scale. The analysis can be prescriptive, diagnostic, descriptive, or predictive. Predictive analytics, a challenging venture necessitating machine learning, contributes to accurate patient treatment, saving time, money, and, most importantly, lives. AI in Cancer Diagnostics encompasses the use of artificial intelligence technologies, such as machine learning and predictive analysis, throughout the entire cancer treatment process, from diagnosis to post-treatment surveillance.

Key Market Insights

In response to escalating and worrisome cancer statistics, medical scientists are re-focusing on predictive analytics for early cancer screening and diagnosis. Early detection and treatment significantly reduce cancer mortality and morbidity.

Globocon 2020, a major cancer surveillance program by the International Agency for Research on Cancer (IARC), reported 19,292,789 new cancer cases in 2020, double the number recorded in 2018. The same year saw 9,958,133 cancer-related fatalities. IARC projections indicate that one in five individuals will experience cancer in their lifetime.

According to the research report "Global Artificial Intelligence (AI) in Cancer Diagnostic Market Analysis, 2021," AI's automated functionalities have the potential to enhance diagnosis and therapy quality, driving the market's growth in the coming years.

Predictive Analytics for Cancer Diagnostic Market Drivers:

Predictive analytics in medical decision-making proves highly advantageous, tailoring treatments for specific patient conditions, particularly chronic illnesses.

Identification of individuals at high risk for chronic diseases, including diabetes, obesity, renal disease, cancer, and cardiovascular disease, enables timely intervention. Researchers at the University of Michigan Rogel Cancer Centre are developing a blood test for HPV-positive throat cancer, indicating treatment effectiveness months in advance. Machine learning and deep learning techniques are employed to categorize cancer patients into risk groups, aiding in cancer research and therapy.

The escalating number of cancer patients propels the predictive analytics for cancer diagnostics market.

The appropriate classification scheme for cancer is a crucial medical challenge, considering cancer as a leading global cause of death. Early cancer detection remains imperative, and AI algorithms provide quantitative measures for improved analysis and diagnostic accuracy.

The market expands as rising awareness prompts individuals to undergo routine diagnostic exams for early cancer detection.

Healthcare organizations and providers organize public education programs to encourage proactive health-seeking behavior, reducing the burden of advanced-stage cancer and improving treatment outcomes. Governments worldwide emphasize the value of early cancer diagnosis, offering accessible screening programs.

Predictive Analytics for Cancer Diagnostic Restraints and Challenges:

Limited understanding of AI's role in healthcare may impede market growth.

Working with large datasets in AI-driven cancer diagnostics requires awareness of deep learning technologies' potential benefits and limitations. The lack of information and training regarding AI systems poses a significant obstacle to market growth. Privacy concerns related to AI handling sensitive personal data may further hinder market expansion.

Technology acceptance remains a substantial obstacle for artificial intelligence.

The real-world adoption of AI in cancer diagnostics involves substantial financial investments, expertise, and time commitment for laboratory analysis infrastructure. Deploying expensive machines, such as next-generation sequencing platforms and imaging scanners, hampers market growth. The uncertainty of economic viability and the AI black box dilemma, where algorithms' inner workings are unknown, are key factors impeding market expansion.

Predictive Analytics for Cancer Diagnostic Market Opportunities:

Predictive analytics addresses significant challenges in cancer detection and treatment, facilitating early detection of precancerous lesions and reducing patient mortality rates.

AI and ML assist in accurate cancer detection, minimizing overdiagnosis, false positives, and false negatives. These technologies aid in monitoring malignancies' prognosis during radiotherapy and immunotherapy. Personalized medications can be developed through AI and ML for individual tumors.

Early and accurate cancer detection positively impacts cancer treatment prognosis.

The financial impact of expensive cancer therapies can be significantly reduced with early cancer detection. Improved survival rates are achievable through timely cancer discovery. AI and ML offer enhanced clinical data extraction, aiding physicians in making informed decisions. The convergence of Data Science with biomedical research explores the potential of AI and ML for interpreting radiological diagnostic images. Renowned AI professor Regina Barzilay, a breast cancer survivor, underscores the transformative potential of AI and ML in clinical data analysis for informed medical decisions.

Predictive Analytics for Cancer Diagnostic Market Segmentation: By Tools

• Artificial Intelligence (AI)
• Machine Learning (ML)

Predictive analytics can derive significant advantages from artificial intelligence (AI), a field within computer science dedicated to emulating intelligent behavior in computers. In 1950, Alan Turing, a pioneer in artificial intelligence, authored a paper titled "Computing Machinery and Intelligence," introducing the Turing test to assess a computer's intellectual capabilities comparable to humans. The majority of AI applications utilize machine learning (ML) techniques to identify patterns in datasets, predicting future outcomes based on these patterns.

Machine learning, a process where a computer continuously enhances its performance by integrating new data into an existing statistical model, is another tool for predictive analytics. It enables systems to update themselves with new data, thereby enhancing accuracy. The application of AI and ML in radiodiagnosis aids clinicians in assessing tumor diagnosis and progression based on images from procedures such as MRIs and PET scans. While AI systems cannot provide formal diagnoses, they analyze speech patterns, alerting patients to potential early warning signs. In 2022, the biomarker development segment led the cancer diagnostic market with a 40.68% revenue share, emphasizing its accuracy in investigational studies and improved sensitivity in tumor screening.

Predictive Analytics for Cancer Diagnostic Market Segmentation: By Applications

• Breast Cancer
• Cervical Cancer
• Lung Cancer
• Thyroid Cancer
• Prostate Cancer

The market categories are based on cancer types such as breast, prostate, lung, colorectal, cervical, and others. The breast cancer diagnostic market was valued at USD 4.3 billion in 2022, projected to grow at a CAGR of 7.4% from 2023 to 2030. The first FDA-approved AI-based system, QuantX, assists radiologists in diagnosing breast abnormalities using MRI data. In 2022, the "other cancer" category led the market in cancer types with a 44.28% revenue share, attributed to advancements in next-generation technologies facilitating accurate biomarker identification.

Predictive Analytics for Cancer Diagnostic Market Segmentation: Regional Analysis

• North America
• Asia-Pacific
• Europe
• South America
• Middle East and Africa

North America holds the largest market share due to enhanced healthcare infrastructure and a focus on early illness identification. The presence of major diagnostic technology producers, research institutes, and a robust healthcare network contributes to market growth. Factors like healthcare reforms contribute to the Asia-Pacific market's anticipated growth, supported by financial and regulatory support from governments like Singapore and Taiwan.

COVID-19 Impact Analysis on the Predictive Analytics for Cancer Diagnostic Market:

The COVID-19 pandemic led to a 30% decline in global economies, impacting AI use in cancer diagnosis. Disruptions in cancer treatment, screening program suspensions, and restricted access to medical facilities resulted in short-term decreases in cancer incidence and increased diagnoses at advanced stages.

Latest Trends/Developments:

Researchers at New York University, with NCI support, utilized Deep Learning algorithms alongside predictive analytics to detect gene alterations in lung tumors. Deep Convoluted Neural Network models were employed for ultrasound image analysis, creating an accurate diagnostic tool for thyroid malignancies.

Key Players:

1. Abbott Laboratories
2. F. Hoffmann-La Roche Ltd.
3. Thermo Fisher Scientific Inc
4. Qiagen
5. bioMérieux

In the highly fragmented cancer diagnostics market, domestic and foreign competitors employ growth strategies such as alliances, partnerships, new product launches, mergers, and acquisitions to enhance their market position.