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Written by : Aishwarya Sarthe

January 18, 2024

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This collaboration aims to enhance the efficiency and consistency of evaluating cancer treatment effectiveness, ultimately improving patient care quality., medical AI, and Project Data Sphere®, a nonprofit initiative of the CEO Roundtable on Cancer, have joined forces to integrate AI solutions into tumour assessments for clinical trials and cancer care.

This collaboration aims to enhance the efficiency and consistency of evaluating cancer treatment effectiveness, ultimately improving patient care quality.

Commenting on the development, Prashant Warier, CEO and cofounder of said, "Partnering with Project Data Sphere signifies a crucial milestone in the progression of cancer treatment, tapping into the transformative capabilities of artificial intelligence."

He further emphasised that the creation of autoRECIST underscores’s dedication to fight cancer. This AI technology seeks to transform the assessment of treatment effectiveness in cancer patients.

“With a determined emphasis on early detection, we are utilising our proficiency in data science to notably progress clinical trial methodologies, ultimately reshaping the terrain of cancer care," he added.

The collaboration centres around autoRECIST, a product developed by Project Data Sphere's Images and Algorithms program. Designed in consultation with the US Food and Drug Administration, autoRECIST addresses the crucial need to automate and standardise tumour response assessments in medical imaging, contributing to improved cancer treatment and research.

This AI clinical tool follows current imaging guidelines, assisting radiologists in detecting, selecting, measuring, and tracking lesions using defined criteria.

autoRECIST: A Crucial AI Clinical Tool

Features and benefits of this AI tool - autoRECIST include:

1. Enhanced Efficiency and Accuracy: Automation of medical image analysis, such as CT scans, reduces variability in measuring tumour size and changes over time, ensuring improved accuracy.

2. Faster Decision-Making: Accelerating the assessment of tumour response enables rapid determination of treatment effectiveness, facilitating quicker adjustments to patient care plans.

Sharing thoughts, Jon McDunn, PhD, president, Project Data Sphere, said, "The autoRECIST program holds the promise of improving these processes and accelerating the delivery of lifesaving treatments to patients."

Project Data Sphere is an independent initiative that aims to improve outcomes for cancer patients by openly sharing data, convening world-class experts, and collaborating across industry and regulators.

It provides an open-access platform where the global research community can broadly share, integrate, and analyse patient-level data from academic and industry cancer clinical trials. The project also manages research programs that explore leading issues in oncology using machine learning tools and big data analytics.’s Recent Innovations has previously received FDA clearance for multiple AI-enabled medical imaging solutions. Their commitment to AI-powered advancements includes qXR-LN, a chest X-ray-based solution designed to identify and localise lung nodules, marking a significant milestone in the company's pursuit of medical imaging excellence.

In another noteworthy development, on October 23, Siemens Healthineers collaborated with and the Global Fund to Fight AIDS, Tuberculosis, and Malaria to leverage AI for improving the diagnosis of tuberculosis. This collaborative effort aims to utilise AI and machine learning technologies to swiftly identify TB-related lung abnormalities from chest X-rays.

Back in September 2023, FDA-cleared findings include crucial aspects such as Endotracheal Tube location, Tracheostomy tube location, tracheostomy tube location, Pneumothorax, Pleural Effusion identification for CXR, as well as qER for intracranial haemorrhage detection on head CT scans and qER Quant for quantifying critical abnormalities on head CT scans.

Also, in August 23, joined forces with xWave Technologies, a cloud-based Clinical Decision Support (CDS) system catering to radiology referrals. The objective was to enhance market accessibility, jointly develop products, and integrate efforts to provide cutting-edge global healthcare solutions targeting crucial healthcare issues.

This partnership is anticipated to result in expedited patient outcomes and decreased unnecessary diagnostic appointments. The combination of accurate initial testing and the efficiency of AI has the potential to assist hospitals in strategically addressing projected healthcare demands.


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