The “rise of the machines” has played a critical role in advancing medical research and improving accuracy of diagnostic precision for decades, transforming the way we monitor new and existing diseases. However, despite the advantages medical imaging has brought to the healthcare system – in early detection of diseases and non-intrusive examination – the stark reality is that long delays and shortages of staff continue to put major limitations on the overall quality of service for patients. However, until the integration of AI and cloud-powered medical imaging looks to have the answer, unlocking significant breakthroughs in patient care.
Legacy issues continue to plague our health services
Lack of healthcare workers, technicians and equipment – both a cause and product of lack of funding – has left national health services overstretched and under-resourced, causing delays in screenings and therefore diagnoses and treatment. In fact, one of the biggest issues facing national healthcare systems today is inability to meet rising patient demand for medical imaging. To give a sense of the scale of the issue, about 133,000 scans are performed daily in the UK, however since 2020 there has been a 10x increase in the number of patients waiting for more than six weeks for a CT or MRI scan¹.
The result? Supply-demand discrepancies and longer wait times for scans, can mean delays in diagnosis, treatment, and worse outcomes for patients. Adding to the issue, the UK now has a 29% shortfall of clinical radiologists, which will rise to 40% in five years without action. By 2027, an additional 3,365 clinical radiologists will be needed to keep up with demand for services².
However, the answer lies not simply in adding capacity and resource, but improving speed and efficiency. This is where the integration of AI and cloud-enabled frameworks can play a transformative role in reshaping healthcare.
Hope on the horizon: Cloud-based medical imaging can unlock meaningful benefits for patient care
As the science and technology supporting our healthcare systems becomes increasingly sophisticated and ever more complex, so do the data sets that sit behind it. This provides time-pressed consultants with the challenge of analysing mass volumes of data, giving way to yet further delays and creating room for error. AI-based automation can analyse larger sets of medical images much more quickly, with greater accuracy and efficiency, leaving clinicians with more time to plan treatment and optimise patient care methods.
Not to mention, AI-assisted medical imaging programmes can flag issues humans cannot, leading to faster and more precise diagnoses. Cloud-based programmes powered by AI and machine learning also have the added capability to automatically map previous scan results and examinations – and can therefore flag abnormalities or anomalies in one image, based on learnings from thousands of images previously analysed. Open-sourced, open framework medical imaging tools powered by machine learning will only improve in efficacy and precision as they analyse more images – so, unlike human consultants, the higher the volume of scans the technology analyses, the more efficient and faster it becomes, saving critical time for patients.
Generative AI, or GenAI, benefits from the same characteristics, with Foundations Models now trained on several terabytes of data. Differentiating itself from Machine Learning, GenAI creates new data resembling its training data, instead of only analyzing data to find patterns and make accurate predictions.
When it comes to medical imaging, GenAI presents an abundance of new solutions. Improved image quality, deep learning-based image reconstruction, detection of diseases, real-time decision support: the potential of GenAI is nearly limitless and has the potential to revolutionise the medical imaging field, ultimately saving time and precious resources for healthcare professionals and organisations.
Less cost, more value: AI enables cost reduction without compromise
Spiralling costs continue to beset progress in the healthcare industry, and there remain inaccurate, preconceived myths about the cost of implementation of new technologies.
For instance, widespread AI adoption in the next five years using currently available technology could result in savings of 5–10% of healthcare spending, the equivalent $200 to $360 billion annually³, whilst vastly upscaling quality of diagnoses, treatment plans, aftercare, and overall patient experience.
Through cloud technology, it is possible to lower costs from streamlining efficiencies across on-site data storage, reduced time processing manual errors, higher levels of security and greater computational power to process large volumes of data sets. Simultaneously, the time saved on wrote analyses frees up clinicians’ time for more meaningful, complex activities.
In medical imaging, the efficiency of AI allows radiologists to automate or semi-automate their work, therefore reducing time and costs spent on image sorting, diagnostics, and treatment planning. Combined with the scalability and flexibility of cloud technology, AI-powered radiology can provide informed opinions from peers from all over the world and offer clinical decision support.
Borderless healthcare: Increasing access to patient care outside the hospital
Medical imaging services can be provided in a variety of health facilities including hospitals, day surgery centers, diagnostic centers, and outpatient care facilities⁴. However, having to go to a healthcare facility isn’t always easy for people undergoing treatment. The ability to bring medical imaging equipment right to the patient’s door would enable doctors to catch chronic diseases early.
With the cloud, that is possible. Philips has developed an app and highly mobile ultrasound transducer – a small probe that enables imaging of the body using sound waves– which allows healthcare practitioners to use this technology inside patients’ homes, and perform lung, heart, abdomen, or pre-natal ultrasounds. The live ultrasound images can be shared through the cloud in real-time with a remote doctor thousands of miles away, helping to advance patient care by bringing experts into an ultrasound exam anywhere in the world. Philips’ point of care mobile ultrasound solution has touched more than 14 million lives in 100 countries around the world.
Supporting doctors in making the right call
In the daily life of a healthcare professional, accurate and timely decisions can be a matter of life and death. Technology has been taking an increase role in assisting doctors in their decision-making.
For example, clinical AI company, Aidoc’s, impact can be felt throughout healthcare system, from reduced time to diagnosis and treatment to shorter report turnaround times⁵. The company aids physicians with AI by enabling real-time alerts of time-sensitive cases and expediting patient care. Further, Aidoc helps clinicians effectively manage patient data and workflows, enabling collective action across service lines to benefit health systems, clinicians, and patients in numerous ways. Looking to medical imaging, Aidoc’s decision-support software analyses CT scans to flag acute abnormalities and prioritize life-threatening cases. With more than 2 million patients scanned each month, Aidoc is impact health system and patients in numerous ways with studies having shown significant reductions in length of stay (11-36%) and turnaround time (22-55%), and significant increases in quality improvement (4-45%).
Revolutionising In Vitro Fertilization (IVF) with AI
Care Fertility, a leading IVF provider, has partnered with BJSS and AWS to introduce artificial intelligence (AI) into their laboratories for embryo selection. With the existing manual approach relying on embryologists inspecting numerous time-lapse images, Care Fertility sought to leverage AI to transform the process. Working closely with the embryologists, BJSS developed a robust deep learning model using computer vision and time-series analysis techniques. The model, based on a vast dataset of over 500 million images and 2 million annotated events, performed on par with the manual process. Within just 18 months, the AI tool is now being implemented across Care ‘s laboratories. AWS services such as Fargate, S3, Step Functions, API Gateway, and more were utilised in the project. This innovation has the potential to improve and expedite embryo selection, and streamline the IVF process for patients.
Cloud-based AI is already making strong contributions to healthcare professionals and patients, from bringing medical imaging to your doorstep and fast-tracking triage at hospitals, to accelerating embryo selection. AWS is partnering with healthcare organizations worldwide, helping them streamline and advance their operations, and to deliver the best possible patient experience.
1 RCR UK Workforce Census 2020 Report
2 RCR UK Workforce Census 2022 Report
3 CEPR
4 International Health Facility Guidelines
5 Aidoc
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