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To enhance healthcare through sustainable integration of innovative AI solutions.
Growing demand for medical imaging due to soaring prevalence of neuro-degenerative diseases, stroke, and cancer.
Shortage of expert radiologists leading to increasing turn-around-time (TAT) to diagnosis and treatment
Increasing backlogs, poorer patient outcomes and inflated healthcare costs.
Staffing and physician burnout, patients falling through the cracks of the healthcare system.
In Bioicon AI Medical Imaging, we believe that cooperation among key players in technology, research, radiology, and the regulatory bodies can safeguard the effective and swift integration of AI into clinical practice. Our seamless collaboration with research institutes, clinical centres, and technology hubs of excellence aims at setting new benchmarks in diagnostic medical imaging.
We focus on creating efficient and fast-performing diagnostic tools, fully integrated into the clinical radiological workflow, that will expand the radiologist’s efficiency and decision-making capabilities, leading to earlier and better detection of disease and improved outcomes for our patients.
We are endeavouring to enable higher automation and productivity in a growing number of critical areas in the work-flow realm to help radiologists cope with the increasing demands and the limited capacity. Our AI-tools are designed to standardise workflow, improve efficiency, and address the need for more quantitative imaging.
Our approach is to make intelligent imaging, the automatic selection and application of the appropriate imaging tests to patients depending on their underlying condition and disease symptoms. We envisage our AI-tools to harness the unprecedented amount of information retrieved by high-density imaging and its post-processing is challenging for the human cognitive capacity.
We develop algorithms that are central component of the integrated diagnostics platform, a platform aiming at elevating the level of diseases diagnosis by combining clinical, pathological, and imaging information mined in the medical records.
Our solutions launch a disruptive and informative change in clinical radiology. The paradigm shift from volume to value-based care is our inspiration.
Our tool provides a thorough capture of the MRI suite recording and analysing imaging exams duration, appropriate protocol choice, gadolinium use and dosage, energy applied to the patient’s body during the exam, and compares the results to the pre-defined benchmarks and guidelines ensuring efficient workflow, optimal use of the available resources and improving patient safety and satisfaction.
Decision-making analytical tools post-process the data, make predictions, and optimise continuously in a successive way the offered diagnostic services in the hospital department, diagnostic centre or network of radiology practices.
35% less rescheduling
No wasted time
60% less aquisition time
Data driven
A radiographer-centric AI approach to adapt in real-time the scanning protocol, including any gadolinium injection, according to the diagnosed condition. The patient receives a tailored personalised MRI protocol that ensures high diagnostic yield for the radiologist and maximal therapeutic benefit.
No need to recall the patient for additional scans - optimised use of gadolinium and the radiology department resources. Efficient use of the allocated scanning slots. Indispensable aid to radiographer to spot ‘on-the-fly’ incidental and pathologic findings and instantly adapt the planned MRI sequences to capture perfectly the condition.
A radiologist-centric tool that assists the user and relieve from the burden of repetitive, cumbersome work in calculating volume and extent of disease. Smart algorithms take up the role to do the time-consuming tasks and present quantitative results to assist the radiologist in the report and diagnosis.
Automatic annotation of lesions and anomalies shortens substantially the image reading time by the radiologist, who focuses on the details and in-depth analysis of the images. A comprehensive differential diagnosis is offered to the radiologist weighted by the probability of each suggested diagnosis.
Supervised AI-assisted diagnosis is now reality. The radiologist guarantees the scientific integrity of the diagnosis by authorising the automatically generated report. The radiologists have now more time to interact with the referrers and patients enhancing their role on a healthcare team.
Automatic red-flagging of unexpected abnormal findings and triaging for prompt reporting.
