勛圖厙轎煤勛圖窪, launched in 2019 by Jacobus, Paige Severino, and radiologist and researcher Dr. Smith is developing software to more accurately assess cancer treatment effectiveness. The founders set out to leverage augmented intelligence in innovative imaging software solutions, improve radiology workflows, quantitative metrics, and reporting in clinical practice and clinical trials.

Dr. Smith, our initial CEO, will take on the newly created role of Chief Medical Officer while continuing his role as Chairman of the Board. Dr. Smith is currently a Radiologist and Oncologic Imager at the University of Alabama at Birmingham. He is Vice Chair of Clinical Research, Chief of Body CT, and is also the Clinical Director of AI at the University���s Heersink Institute of Biomedical Innovation.

���I am excited to take on the role of Chief Medical Officer at 勛圖厙轎煤勛圖窪, where I will be able to focus on the clinical aspects, the necessary research and new and innovative advancements needed in radiology and cancer treatment evaluation,��� said Dr. Smith. ���勛圖厙轎煤勛圖窪 was created to utilize advanced technologies to help standardize reporting with greater inter-observer agreement, while decreasing variations in impressions ��� making it more useful and easier for oncologists to interpret data and information for their patients.���

Jacobus was the company���s initial COO and has more than 20 years of experience in founding, investing in and raising capital for startups. He is an experienced financial professional, with significant industry experience in investment banking, early stage ventures, financial management, new business development, engineering, operations, and manufacturing. Jacobus also has deep experience in the biotech, medical products manufacturing, software, transportation and insurance sectors.

“I am honored to lead this innovative company that is delivering solutions to radiologists who work on important issues such as saving lives and working with advanced cancers,��� said Jacobus. ���My role as CEO is to lead 勛圖厙轎煤勛圖窪 as it helps solve real radiology pain points using AI technologies to improve accuracy, consistency, and efficiency in radiology.���

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While some of the hype is warranted, some isn’t. And many radiologists are left wondering, “Can AI improve radiology image analysis?” We might be a little biased, but for us, it’s a resounding “yes.”

Benefits of AI for radiology image analysis

Radiologists’ skills and know-how are irreplaceable. The best AI tools recognize this and work to augment your efforts, not replace them. It does this in two ways: faster reads and improved accuracy.

These two benefits eliminate the need for radiologists and oncologists to duplicate their efforts. No one needs to dictate notes or create reports. The AI can handle that, providing standardization and peace of mind for your team.

Faster reads

Faster reads generate higher output. You can complete more reads���and help more patients���each day. But there’s another reason that might be even more beneficial.

If you spend less time reading images, you can play a more effective diagnostic role, incorporating data from various sources, not just images. AI offers the ability to deliver more impactful care, so you can make a more significant difference in patients’ lives.

Improve read accuracy

AI can’t replace your expertise. Instead, it builds on existing knowledge learned from large data sets of previous scans���in some cases, this includes hundreds of thousands of images. With this knowledge, AI software can interpret a new image and detect tumors faster than standard methods.

It can also help when reviewing multiple scans for a single patient. When opening a new image, AI can retrieve previous images and notes automatically. This makes it fast and easy to pick up where you left off and helps you complete accurate reads quickly.

Additional benefits

Faster, more accurate reads have positive ripple effects that can trickle down throughout many areas of your work. From increased inter-observer agreement to reduced burnout, AI has a lot to offer radiologists.

Eliminate major errors and increase inter-observer agreement

AI reduces errors in radiology image analysis. With learned knowledge from large data sets of previous scans, AI can accurately identify, measure, and report on detected tumors. 

It also provides standardization. Without that, reads can vary, and inter-observer agreement dips to an inefficient level. Oncologists wind up repeating the same effort made by radiologists, taking up more time and reducing overall productivity.

Reduce burnout

A showed that only 25% of radiologists felt happy. And 44% experienced some degree of burnout. 

AI allows radiologists to work more efficiently, so burnout becomes less of an issue. Your team can perform at the top of their licensure and enjoy the job satisfaction they need for long-term well-being.

Faster report turnaround times

With data embedded into workflows, it’s more easily extracted to make report development and delivery a quick and easy process. You don���t have to go searching for information. The software can handle that for you.

Smooth integrations

Importantly, AI can assist in integrating patient and image data into other tools like PACS, RIS, and EHRs. This allows for a more holistic diagnosis process and more customized treatment plans.

Collectively, AI is becoming an important tool in the quest to develop multi-faceted patient data for better care and more successful outcomes. 

Support for non-diagnostic tasks

AI also supports a variety of non-diagnostic tasks to improve operations for radiology image analysis. These tasks can include order entry support, patient scheduling, resource allocation, and improved workflows. 

Yes, AI can improve radiology image analysis

The benefits of AI in radiology are seemingly endless. And as technology develops further, new ways of helping radiologists will develop. Thankfully, some of those cutting-edge capabilities are already available today.

Founded by radiologist Dr. Andrew Smith, MD, PhD, 勛圖厙轎煤勛圖窪 is on a mission to leverage artificial intelligence to help radiologists perform at their highest levels. We want to augment your abilities through improved workflows, quantitative metrics, and automated reporting. 

勛圖厙轎煤勛圖窪 workflows are twice as fast as traditional manual image evaluation and dictated text reports. Our solutions allow you to keep your eyes on the images so you can focus on high-level tasks, increase productivity, and reduce burnout.

Contact 勛圖厙轎煤勛圖窪 today to schedule a demo.

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勛圖厙轎煤勛圖窪 the study

During the study, providers compared body CT images from 120 consecutive patients with multiple serial imaging exams and advanced cancer treated with systemic therapy using current-practice methods and AI-Assisted methods. Current practice methods included dictated text-based reports and separately categorized responses (CR, PR, SD, and PD). The AI-Metrics Platform used custom AI algorithms for tumor measurement, target and non-target location labeling, and tumor localization at follow up. The AI-assisted software was able to automatically categorize tumor response per RECIST 1.1 calculations and displayed longitudinal data in the form of a graph, table, and key images. The studies were read independently in triplicate for assessment of inter-observer agreement according to the metrics: major errors, time of image interpretation, and inter-observer agreement for final response category.

What they found

勛圖厙轎煤勛圖窪 increased reporting accuracy by 25%, reduced errors by 99%, cut interpretation time in half, increased inter-observer agreement among oncologists by 58% and among radiologists by 45%, and was preferred by 96% of radiologists and 100% of oncologists compared to current practice with manual image assessments and text reports. In a (called eMASS; included guided workflows and annotation tools, but no AI algorithms), eMASS reduced errors and time of evaluation was twice as fast, which indicated better overall effectiveness than standard of care, manual tumor response evaluation methods for three different therapy response criteria.

The Verdict

AI-assisted advanced cancer longitudinal imaging response evaluation significantly reduced major errors, was nearly twice as fast, and increased inter-observer agreement relative to the current-practice method. The decisive advantages of the 勛圖厙轎煤勛圖窪 Platform establishes a new and improved standard of care in oncology and radiology.

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