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

The Predictive Analytics for Cancer Diagnostic Market was valued at USD 16 billion in 2023 and is projected to reach a market size of USD 80.67 Billion by the end of 2030. Over the forecast period of 2024-2030, the market is projected to grow at a CAGR of 26%. 

Petabytes and terabytes of data are being generated daily in the healthcare industry. We refer to this enormous volume of data as "Big Data." Pathologists, medical professionals, and other experts examine data to extract valuable information that aids in the accurate diagnosis of a disease. "Big Data Analytics" refers to this type of analysis done on a massive scale. Analysis might be prescriptive, diagnostic, descriptive, or predictive. Predictive analytics in healthcare is a difficult endeavor that requires machine learning. However, it also helps doctors treat patients accurately, saving them money, time, and most crucially, lives. Machine Learning (ML) facilitates effective decision-making and aids in the quick and accurate diagnosis of illness. AI in Cancer Diagnostics refers to the use of artificial intelligence technology, such as machine learning, predictive analysis, etc., across the entire process of treating cancer in a human body, from diagnosis to post-treatment surveillance.

Key Market Insights

Medical scientists' attention has been drawn back to the use of predictive analytics in early cancer screening and diagnosis as a result of the concerning and steadily increasing numbers. because early cancer detection and treatment can lower cancer mortality and morbidity.

The International Agency for Research on Cancer (IARC) has released data on worldwide cancer epidemiology through Globocon 2020, one of its major cancer surveillance programs. 19,292,789 new cases of cancer were recorded in 2020, which is two times more than the number of cases reported in 2018. 9,958,133 cancer-related fatalities were reported in the same year as the over 19 million instances of cancer that were documented.  According to the International Agency for Research on Cancer (IARC) projections, one in five people will get cancer throughout their lifetime.

As to the research report titled "Global Artificial Intelligence (AI) in Cancer Diagnostic Market Analysis, 2021," the automated functionalities of AI hold the potential to improve the quality of diagnosis and therapy. Because of this, there is a growing need to include AI in cancer diagnostics, which will drive the market's expansion as a whole in the upcoming years.

Predictive Analytics for Cancer Diagnostic Market Drivers:

The application of predictive analytics to medical decision-making has shown to be highly beneficial. Every kind of treatment has a particular effect on a patient, especially for chronic conditions.

Determining who is in danger, gives medical practitioners the chance to prevent chronic diseases and intervene early. Providers can use predictive analytics to identify patients who may be at high risk for developing certain chronic illnesses, including diabetes, obesity, renal disease, cancer, and cardiovascular disease. Researchers at the University of Michigan Rogel Cancer Centre are developing a blood test that, in comparison to standard imaging scans, can indicate, months in advance, whether a certain treatment approach for HPV-positive throat cancer is effective. Numerous subtypes of cancer have been identified, making it a diverse disorder. Since early detection of cancer aids in patient therapy, prompt cancer screening and treatment are essential prerequisites for early cancer research. Numerous scientific groups investigated the use of machine learning (ML) and deep learning techniques in biomedicine and bioinformatics to categorize cancer patients into high- or low-risk groups. As a result, the genesis and therapy of cancer have been modeled after these strategies.

The growing number of cancer patients is the main driver propelling the market for predictive analytics for cancer diagnostics.

One practical medical issue is determining the appropriate classification scheme for cancer. One of the main causes of death in the world today is cancer. As a result, the scientific community now views research into cancer diagnosis as crucial. Early detection of cancer is the main challenge in its therapy. Frequently, cancer is discovered in its advanced stages, when it has spread throughout the body and interfered with the ability of essential organ systems to operate. Therefore, it's critical to diagnose cancer early. AI algorithms offer quantitative measures for improved analysis and improve diagnostic accuracy.

The market is expanding as a result of rising consumer awareness of the value of early cancer detection.

People are going for routine diagnostic exams and screenings to keep an eye on their health. A lot of public education programs regarding the advantages of early intervention are also being organized by healthcare organizations and providers, encouraging proactive health-seeking behavior. Beyond this, early detection lowers the entire healthcare burden related to advanced-stage cancer while also improving treatment outcomes. Around the world, governments and medical facilities are highlighting the value of early cancer diagnosis and offering easily accessible screening programs. By encouraging people to get regular cancer screenings, these programmers are having a positive impact on the market.

Predictive Analytics for Cancer Diagnostic Restraints and Challenges:

Lack of Understanding of AI's Use in the Healthcare Sector Could Limit Market Growth

AI in cancer diagnostics requires working with huge datasets and having an understanding of the possible benefits and limitations of deep learning technologies. Therefore, the largest obstacle that can prevent the market from growing in the upcoming years is a lack of information and training regarding AI systems. Furthermore, because AI may be used to obtain sensitive personal data like genetic sequences, privacy may potentially be violated, which could further impede market expansion.

The adoption of the technology is currently one of artificial intelligence's largest obstacles.

Today, one of the largest obstacles facing artificial intelligence is getting the technology accepted in the real world. The deployment of analytical instruments and infrastructure for laboratory analysis of cancer samples requires a major financial investment, expertise, and time commitment, which hinders the market's growth. This is especially noticeable when setting up pricey machines like next-generation sequencing platforms, PET, SPECT, and MRI scanners. Furthermore, despite the significant expenses connected with their creation and deployment, these goods are not economically viable. One of the main things holding back the market's expansion is this uncertainty. This is especially true when it comes to diagnosing terminally sick patients, when the patient's ability to make decisions may ultimately determine how long they live. This issue is exacerbated by the AI black box dilemma. Programmers can only observe input and output data; the inner workings of algorithms are unknown. This is referred to as AI black box.

Predictive Analytics for Cancer Diagnostic Market Opportunities:

One of the biggest problems in the detection and treatment of cancer has been addressed by the application of predictive analytics. It can aid in the early detection of precancerous lesions and lower the cancer patient death rate. By accurately detecting and predicting cancer, AI/ML helps to lower the rate of overdiagnosis, false positives, and false negatives. When using radiotherapy and immunotherapy, these methods can also be utilized to monitor the malignancies' prognosis. By creating tailored treatments for individual tumors, AI and ML may potentially be used to create personalized medications.

The prognosis for cancer treatment would be significantly enhanced by early and accurate cancer detection. Early cancer detection will have a significant financial influence on expensive cancer therapies. Since early cancer discovery may significantly reduce mortality rates, this could potentially have a significant effect on cancer survival rates.

Predictive analytics provides a better way to find a solution, and when the term "Data Science is everywhere" gained traction, biomedical researchers began exploring AI and ML. Renowned artificial intelligence (AI) professor Regina Barzilay, who also survived breast cancer, shared an inspirational story about how her diagnosis changed her research focus. She proposed that greater clinical data may be extracted by AI and ML systems, enabling doctors to make informed judgments. To assist physicians in interpreting radiological diagnostic images, she gathered information from medical reports and created machine-learning algorithms.

PREDICTIVE ANALYTICS FOR CANCER DIAGNOSTIC MARKET REPORT COVERAGE:

Predictive Analytics for Cancer Diagnostic Market Segmentation: By tools

• Artificial intelligence

• Machine learning

Predictive analytics can benefit greatly from artificial intelligence (AI), which is an area of computer science that deals with simulating intelligent behavior in computers. Put otherwise, it refers to a machine's ability to mimic intelligent human behavior 1950, Alan Turing, one of the forerunners in the field of artificial intelligence, wrote a paper titled "Computing Machinery and Intelligence." To ascertain whether a computer is capable of displaying the same degree of intellect as humans, it created the so-called Turing test. Machine Learning (ML) techniques are used by the majority of Artificial Intelligence (AI) applications to identify patterns in the datasets. Future results are predicted using these patterns.

The process by which a computer can constantly improve its performance by incorporating new data into an existing statistical model is known as machine learning (ML), and it is another tool for predictive analytics. It enables the system to update itself as new data is added, thereby improving the task's accuracy. The radiodiagnosis of precancerous lesions and tumors has long made use of the AI/ML idea as a predictive analytics tool. To help clinicians make informed judgments on the diagnosis and course of tumors, the AI system scans the images produced by several radiological procedures, such as MRIs and PET scans, and interprets the information contained in them. The AI system logs people's speech patterns, evaluates the information, and alerts the patient to potential early warning indicators of the illness. Although the app is unable to provide a formal diagnosis, users can bring the material to their physician for assessment.

With a revenue share of 40.68% in 2022, the biomarker development segment led the next-generation cancer diagnostic market based on application. This was because biomarker tests have shown high levels of accuracy, up to 90% in investigational studies, and they can improve sensitivity in tumor screening.

Predictive Analytics for Cancer Diagnostic Market Segmentation: By Applications

• Breast cancer

• Cervical cancer

• Lung Cancer

• Thyroid Cancer

• Prostate Cancer

Breast cancer, prostate cancer, lung cancer, colorectal cancer, cervical cancer, and other cancers such as pancreatic, skin, blood, etc. are the market categories based on the kind of cancer. The market for breast cancer diagnostics was estimated to be worth USD 4.3 billion in 2022, and between 2023 and 2030, it is projected to expand at a compound annual growth rate (CAGR) of 7.4%. As one of the most common diseases in the world, breast cancer is expected to become more common, which will drive the market's expansion. The first AI-based predictive analytics system for breast cancer diagnosis has received approval from the FDA's Centre for Devices and Radiological Health (CDRH). Qlarity Imaging (Paragon Biosciences LLC) created QuantX. QuantX is a computer-aided diagnostic (CAD) software system that helps radiologists use magnetic resonance imaging (MRI) data to diagnose and characterize breast abnormalities.

With a revenue share of 44.28%, the other cancer category led the next-generation cancer diagnostic market in terms of cancer type in 2022. The domination of the market is a result of the growing developments in next-generation technologies, which have made it possible to identify and profile different kinds of biomarkers with more accuracy and comprehensiveness.

Therefore, it strongly suggests that artificial intelligence (AI) is needed for cancer diagnosis and breast cancer treatment. Furthermore, the World Health Organization states that early cancer diagnosis can significantly lower the possibility of death and raise the likelihood of a full recovery. This can be readily accomplished by incorporating AI technology into the screening and detection of breast cancer. The National Cancer Institute has also created a computer-aided program (CAD) that examines digital photos of a woman's cervix to spot alterations that may be precancerous and need to be checked out right away. Automated Visual Evaluation is the name of this Artificial Intelligence-based method (AVE).

Predictive Analytics for Cancer Diagnostic Market Segmentation: Regional Analysis

• North America

• Asia-Pacific

• Europe

• South America

• Middle East and Africa

North America's enhanced healthcare infrastructure allowed it to hold the largest market share. Aside from this, the increasing emphasis on early illness identification in people is fostering the expansion of the business in the area. Most of the newly established businesses in the same industry have a good foothold in the area. Prostate cancer imaging and analysis results have been proved by one of Microsoft system's Inner Eyes in a matter of minutes. Accordingly, the market is expanding as a result of rising cancer awareness and advantageous reimbursement practices. In addition, the North American market is expanding due to the presence of major producers of diagnostic technology, research institutes, and a robust network of healthcare facilities.

Several variables, including the expansion of healthcare reforms, are likely to contribute to the Asia Pacific market's expected profitable growth. The expansion of several well-known companies into the Asia Pacific region is anticipated to have a favorable effect on the market for cancer diagnostics. The governments of Singapore and Taiwan provide financial and regulatory support that is advantageous.

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

There was a 30% decline in nearly every economy in the world. As the healthcare industry shifted its focus to solving the current issue of the coronavirus epidemic, the use of AI in cancer diagnosis also saw a minor decline. Cancer patients' visits for treatment were impacted by the COVID-19 outbreak.

In addition, screening program suspensions, health system closures, delayed diagnosis, and restricted access to medical facilities were all consequences of the epidemic. Short-term decreases in cancer incidence, an increase in diagnoses for advanced stages of the disease, and a rise in cancer mortality were caused by these causes.

Latest Trends/ Developments:

Researchers at New York University supported by the NCI employed Deep Learning (DL) algorithms in conjunction with Predictive Analytics to detect gene alterations from pathophysiological pictures of lung tumors. An algorithm that can identify particular gene mutations based on visual examination of the pathophysiological images was developed using the pathophysiological images of lung cancers that were gathered from the Cancer Genome Atlas. Based on the image analysis, this method can predict the various types of lung tumors and the associated gene alterations with high accuracy.

Using Deep Convoluted Neural Network (DCNN) models, ultrasound image analysis was utilized to create an accurate diagnostic tool for thyroid malignancies.

Key Players:

There are many domestic and foreign competitors in the highly fragmented cancer diagnostics market. To increase their market position, major players are implementing a variety of growth tactics, including alliances, partnerships, joint ventures, new product launches, geographic expansions, mergers, and acquisitions.

1. Abbott Laboratories

2. F. Hoffmann-La Roche Ltd.

3. Thermo Fisher Scientifica Inc

4.  Qiagen

5. bioMérieux