Artificial intelligence has found widespread application in the field of healthcare but radiology and medical imaging are two areas where AI is seeing significant progress.
AI and machine learning can help identify patterns in data more quickly than humans. This combined with image recognition abilities can improve patient outcomes while reducing costs associated with medical imaging.
The rise of AI in the era of big data can assist physicians in improving the quality of patient care and provide radiologists with tools for improving the accuracy and efficiency of diagnosis and treatment.
A recent research report found that AI has the potential to improve the patient outcomes by 30-40 percent while reducing the treatment costs by up to 50 percent.
Ways in which AI is helping reduce cost of medical imaging
1. Introducing efficiency in the field of radiology
Artificial intelligence is playing a vital role in improving operational efficiency in radiology.
By analyzing vast amounts of data in patient’s scans at great speeds and accuracy, AI is successfully supplementing the skills of the radiologist.
In a 2017 study on using deep learning to detect tuberculosis in chest x-rays, AI could identify the lesions with 96 percent accuracy rate, which is even higher than human radiologists.
With more and more data being used to train the AI/ML algorithms, the accuracy rate is likely going to go even higher.
By freeing up radiologists’ time from mundane and repetitive work, AI can make it possible for them to engage in specialised tasks. Increase in efficiency directly relates to cost savings for the hospitals as well as the patients.
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2. Intraoperative MRIs for better patient outcomes.
Intraoperative MRIs help the surgeons get a better understanding of the tumour size, type and malignancy when resecting cancerous tissues.
The Advanced Multimodality Image Guided Operating Suite (AMIGO) is a great tool to assess the completeness of surgery in order to ascertain that all the tumour lesions have been successfully removed.
This reduces the future need for future surgical interventions and other treatments for cancer patients.
Hospital’s such as Brigham and Women’s added mass spectrometers to AMIGO equipment for real-time tissue characterization.
This mass spectrometry data can further be analysed using Machine Learning algorithms which is helpful in avoiding costly and invasive procedures in the future.
AI can overlay mass spectrometry data over the segmented MRI images to give a complete intraoperative picture helping in avoiding costs associated with future procedures.
3. Elimination of human bias in radiodiagnosis
Radiologists are human at the end of the day and are prone to human error and bias when making radiological judgement. Repeated tasks over and over again may result in decision fatigue and incorrect diagnosis.
AI, on the other hand, is well-suited to handle repetitive work processes, managing large amounts of data, and can provide another layer of decision support to mitigate errors.
Use of AI in aiding radiological diagnosis can help mitigate medical error. Using its ability to integrate information and analyze data at lightning fast speeds, AI has already proved its merit in radiological diagnosis of medical images such as mammograms with high level of accuracy.
Using AI to diagnose breast cancer is 30 times faster than the human counterpart with an accuracy rate of 99 percent.
Our healthcare software development team recently worked on building a surgical implant database by training AI algorithms to detect surgical implants in radiological images.
Use of technology to carry out such routine tasks not only helps prevent medical errors that may lead to adverse outcomes, but also helps in optimizing radiologists’ time which can be better utilized interacting with patients and physicians, an aspect of medicine that AI can’t take over.
4. Using AI to prepare 3D models
AI algorithms are being used to construct 3D models out of 2D images. Zebra medical vision is one company that is leveraging machine learning algorithms to create 3D models out of X-ray images.
This helps in avoiding the need for MRIs, CT and PET scans. This not only cuts down on the cost of medical imaging but it also ensures that the patients aren’t unnecessarily subjected to high levels of radiation and associated risk of cancer.
The aim of this technology is to provide cost effective diagnostic and surgical planning tools to hospitals and healthcare practitioners.
In cases in which MRIs and CT scans aren’t an absolute necessity, these models can help recreate a complete clinical picture.
While eliminating MRI and CT scans isn’t a likely outcome, AI can certainly help cut down on it significantly, thus reducing the costs at the same time.
5. Efficient data management for reduced repeated scans
Inefficient data management is one problem that all hospitals struggle with. The same holds true for radiological images as well. Repeat scans are often ordered when doctor’s or patients are unable to access the prior images. This adds to the cost of medical imaging.
A Canadian study found that a diagnostic imaging repository was successful in reducing the instances of repeat exams for patients suffering from biliary cancer by 15-19 percent.
AI’s ability to organize, store and retrieve large amounts of data comes in helpful in reducing repeated scans. With effective data management comes cost saving as well as reduced exposure of the patients to radiation.
The potential impact of harnessing AI to analyze medical imaging is huge. With well established medical companies to up and coming health-tech startups looking to leverage the power of AI, the field is bound to witness tremendous growth in the years to come.
If you have questions about how AI can help you streamline operations and cut down on the cost of medical imaging for your practice, get in touch with our team of experts.
With over a decade of specialized experience in healthcare software development and experience of building AI powered software solutions, Arkenea is a trusted brand in the health industry. Contact our experts to transform your medical software ideas into reality