The use of computers, mobile applications, wearable devices, and other biosensors to gather and store cancer-related data has been rapidly accelerating. This data holds the potential to enable healthcare professionals to better design and conduct clinical trials and studies in the cancer care setting to answer questions previously thought infeasible.
Real-world evidence generated from data from wearable devices and mobile apps and novel clinical trials is transforming the future of cancer care. Wearable devices are also emerging as a vital tool in the collection of real-world data. Wearable devices are clinically useful for monitoring cancer in real-time and long-term, dynamic, and medical processes. A vast amount of patient-generated data through wearable devices can be used to monitor cancer patients at home in their own environment, compared to monitoring only at in-clinic visits
Artificial intelligence (AI), especially machine- and deep-learning (ML/DL) methods, has been increasingly used across many stages of the drug development process. Advancements in AI have also provided new strategies to analyze large, multidimensional RWD. AI is driving ground-breaking leaps in protein structure identification, and advances in regulations are providing healthcare research organizations with access to real-world data to accelerate clinical trial processes.
Companies operating the market have launched AI-based medical devices specifically for oncology-related fields. In January 2021, the U.S. FDA’s Center for Devices and Radiological Health (CDRH) granted Seno Medical Instruments, Inc. (U.S.) premarket approval (PMA) for Imagio Breast Imaging System, which is a diagnostic breast cancer imaging system that uses AI to interpret the medical images and support physicians better differentiate between benign and malignant breast lesions. Furthermore, in December 2020, Hologic, Inc. (U.S.) announced U.S. FDA clearance and commercial availability of Genius AI Detection technology, an AI-based (deep learning) software with high sensitivity and low false-positive rate designed to help radiologists detect potential cancers in breast tomosynthesis.
The innovative technology advancements of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) offer pharmaceutical, biotechnology, and medical device companies the power to generate enhanced RWE output, decrease time to insights, and make the most out of the vast real-world data sources available.
Companies operating in the real-world evidence oncology solutions market have also adopted various strategies to align themselves with the changing dynamics of the real-world solutions space. For instance, in October 2021, SYNEOS HEALTH, INC. (U.S.) acquired RxDataScience (U.S.), a healthcare-focused data analytics, Artificial Intelligence (AI), and data management company. This acquisition was aimed at strengthening the company’s position in the real-world evidence solutions market. Furthermore, in September 2020, Parexel International Corporation (U.S.) acquired Roam Analytics, Inc. (U.S.), a healthcare software company, to leverage its Artificial Intelligence (AI) and Machine Learning (ML) capabilities to drive innovations across drug development and life sciences. The acquisition also enhanced Parexel’s Pharmacovigilance and other real-world data capabilities.
In addition, the integration of artificial intelligence also helps to improve the visibility of cancer treatment. Life sciences companies are adopting advanced tools, such as AI and big data to prepare organizations to adapt to this RWE market. Combining these technologies with RWE can make life sciences organizations more effective in developing better cancer drugs/devices in a shorter duration. Similarly, Natural language processing (NLP), an AI technology, can be useful in processing and evaluating large amounts of unstructured data to derive RWE.
Meticulous Research®, in its latest publication on the RWE oncology solutions market, states that this will grow at a CAGR of 14.2% from 2022–2029 to reach $1.65 billion by 2029.
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