AI and the use of the real-world data will define the path to transformation in Oncology.
FREMONT, CA: With the rapid advancements in oncology care, owing to the contributions from artificial intelligence (AI), it is difficult to differentiate between the hype and what can be realized in the near future. The technology is certainly transforming the sector while also amplifying the effect of the existing systems. Here are some of the predictions that are expected to transform the oncology sector in the next few months:
Tumor Radiology will Use AI for A Pre-Tumor Cancer Diagnosis
Accurate and early detection of cancer is essential to both enhancing patient outcomes as well as for offsetting the cost of treatment. Based on a research in Japan, AI can be used to detect colorectal cancer with an accuracy of 86 percent before tumors become malignant and get much difficult to treat. Google is also engaged in this kind of deep learning, searching for ways to spot cancer metastases on gigapixel pathology images.
Utilization of Real-World Data by the FDA for the Approval of a New Drug Application
Real-world data will be used to eliminate contraindications or enhance a label indication for an existing drug in the U.S. A paper published in the Journal of the European Academy of Dermatology and Venereology, advanced this a step closer to reality by utilizing real-world treatment data on 317 patients undergoing treatment for melanoma with the monoclonal antibody ipilimumab. As per the data, the treatment resulted in a 40 percent reduced hazard of dying than those not getting treatment after ipilimumab. The real-world data is available and ready for the FDA to put to use.
Clinical Interpretation Reports and Treatment Recommendations of Tumor Genomics to Include Real-World Data
In the first half of 2018, important changes to the reimbursement of genomic testing occurred. Foundation Medicine’s FoundationOne test got a nod from the Centers for Medicare & Medicaid Services (CMS) as per its new Advanced Diagnostic Laboratory Test (ADLT) status as reimbursement at $3,500 per covered test. Currently, most of the commercial tests have included some editorialized content from clinical trials or publications instead of real-world data. The new reimbursement is making tumor genomics mainstream and creating a boom in the use of real-world data for clinicogenomics.
Predictive AI Models for the Physician Point of Care
Bringing the power of AI analysis to providers will be the most crucial trend in the time to come. Electronic health record (EHR) systems and clinical cloud vendors are eyeing to include predictive AI data around patient outcomes, adverse events, and optimal treatment plans. At present, the primary advances in such kind of predictive analytics are in bettering billing documentation and EHR, but the clinical application is close and is carried along by a shift to value-based care.
The most interesting aspect of the above-mentioned predictions will be the use of real-world data in new drug approvals. It would mean that FDA approvals will better incorporate evidence from diverse populations as compared to the current setup that utilizes small clinical trials with homogeneous patients. The diversity will also help patients and physicians to understand which treatment works, which will ultimately result in improved outcomes and reduced costs.