New Platform Deploys Data Science Robots to Improve Medical Decision-Making, Speed up Diagnosis and Improve Patient Outcomes
Bantham RADAR is an AI Machine Learning platform which has been developed in collaboration with AGXIO which seeks to help healthcare professionals obtain improved insights and recognise data patterns that enhance clinical, operational and financial decision-making; improving patient outcomes, whilst lessening the burden on clinical staff.
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It comes at a time when the NHS recently announced waiting lists have hit a 14-year record high of 4.7 million people. RADAR works by deploying ‘automated data science robots’ that operate at extremely high-precision performance. These robots evaluate data to produce predictive models, which are then analysed to identify patterns, parameters or problems in every aspect of healthcare. RADAR has been specifically tailored for use by clinicians and key decision makers in their everyday operations. By automating the role of the data scientist, clinicians can now make full use of all of their data and make better informed decisions, without the need for specialist skills or extensive retraining.
“Bantham RADAR focuses on problems that are beyond human scale in dimension or complexity,” says Edward Belgeonne, CEO & founder, Bantham Technologies. “Typically, a problem may have tens or even hundreds of millions of data points which must be analysed during the data modelling phase. In addition, there are tens of thousands of different machine learning models that may need to be considered before selecting the optimum model. Bantham RADAR makes this so much easier than before and brings AI into the realm of the everyday domain user, not just the statistical expert.”
RADAR has been built in partnership with the award winning AI and Data Science company Agxio Limited. Agxio is a leading specialist in the bio-tech, life-sciences and agricultural science industries backed by the Development Bank of Wales. The CEO of Agxio, Dr Stephen Christie, stated that “Automated machine learning solutions delivered with a range of explainable metrics will provide a quantum leap for the healthcare industry. We are delighted to be collaborating with Bantham Technologies to provide the underlying engine, Apollo, on this ground-breaking Medical eXplainable AI platform (MXAITM).
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MXAITM has the potential to transform how AI is used in healthcare; delivering a range of explainable metrics which is both comprehensible to the practitioner and meets the GDPR requirements in terms of transparency of decision making and explainability. Crucially MXAITM will enable practitioners and regulators alike to extend the application of image analytics to a far broader practitioner space. Applications such as Bantham RADAR must not simply be confined to a small number of specialist super-users, they must be relevant, comprehensible and practical to a far wider user group than is currently the case, if they are to have real impact and effect positive change.”
The RADAR platform is entirely data agnostic which means it can ingest any data structure, be they numerical, textual or images, thus enabling a broad range of use cases – anything from image analysis to treatment predictions. By automating processes that would otherwise take weeks or months to achieve, RADAR delivers results within hours, sometimes even minutes. In a recent test, RADAR ingested 2,500 brain scans, delivering its results (identifying different types of brain tumour) within three minutes with an initial efficacy rate of 84%. This was achieved after only two hours of ‘training’. Subsequent fine tuning of the model saw efficacy rates rise as high as 99.83% in some cases.
Belgeonne states that, “RADAR helps healthcare professionals see what has happened, why has it happened and what is likely to happen in the future as a result. It is a truly intelligent, unbiased, neural engine that is highly configurable, infinitely scalable and capable of having a huge and positive impact on patient outcomes.”
RADAR plans to release three modules in the coming months, namely Community Data, Acute Data and Digital Pathology. The Community Module delivers insights into clinical trends from District Nurse & Outpatient activities, the Acute Module identifies clinical trends from patient data sets for use by on-site clinicians; whilst RADAR Pathology ingests diagnostic images such as X-rays, MRI and CT scans to improve the speed and accuracy of diagnosis in a variety of settings.
Belgeonne concludes, “As the pressure on the NHS begins to subside to more manageable levels, it is becoming increasingly clear that COVID has created an enormous backlog within mainstream healthcare activities; one which simply cannot be managed downwards without the wide-scale adoption of technology and new ways of doing things. We sincerely hope that RADAR will play a part in that.”
Earlier this year, the UK Parliamentary Office of Science and Technology published its AI and Healthcare Research report, which repeatedly points to the role AI will play in advancing the healthcare industry. With nearly 5 million cancelled operations since the outbreak of Covid 19, Belgeonne believes that the NHS will need to embrace technology like never before, if it is ever to return to pre-pandemic normality.
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