September 15, 2021
12 minutes read
The term medical imaging is widely used when we speak about digital help for creating and processing images of different parts of the human body for diagnostic and treatment. It includes different types of medical imaging like X-ray radiography, Fluoroscopy, Magnetic resonance imaging (MRI), Ultrasound (US), Computed tomography (CT), nuclear medicine, hybrid modalities.
AI in medical imaging allows doctors to see the organs, bones, and tissues in a patient’s body. One of the biggest challenges in this field is the time-consuming analyzing, documenting, processing, and evaluating rapidly accumulated visual data from medical examinations and procedures.
AI in medical imaging is capable of rapid scanning and analysis of gathered visual information. This can help reduce medical errors in different medical facilities. AI in medical imaging enables radiologists to inspect medical imaging results as just one source of data to make a complete picture of a patient’s health condition.
Let’s uncover more in detail the benefits of implementing Artificial Intelligence in medical imaging and the most essential use cases where AI has the potential to revolutionize medical imaging.
The biggest issue with data in the healthcare field is that enormous amounts of unstructured visual information (CAT scans, X-rays, MRIs, etc.) are being generated. Manual methods of analysis can not process such quantities of visual data. It is striking the capacities of competent medical workers and results in burnout of overworked radiologists.
According to statistics, radiologists are now reading 12 MRI images per minute compared to 3 a decade ago. It means that more work must be done by quite the same number of radiologists, not to mention the CT and MRI utilization that has remarkably grown in recent years. As a result, overworked medical professionals can make mistakes more often. Not to forget that the work of radiologists consists not only of image analysis but the overloading with one task increasing, other duties become more difficult to find time for. For example, individual radiology reports or other tasks where medical experience and expert knowledge, based on research and informed by judgment, come into play to form a holistic diagnosis.
Artificial Intelligence in radiology can help to uncover patterns and recognize structures based on autonomously extracted features. It brings new value to medical care on multiple levels, from intelligent image analysis for diagnosis, (for example, detection of malignant cancer structures in radiology) to content-based image retrieval.
Artificial Intelligence in radiology can change and improve every part of the medical imaging workflow. AI-based models are created not to replace a doctor’s role, but to enhance it through intelligent assistance, allowing healthcare professionals to focus on more complicated and value-adding tasks.
AI-based applications developed for medical imaging help to make alternative diagnoses or see anatomical structures much sharper and finer than doctors might have previously been able to. Such software does not sharpen images more quickly than previously, but it can better scalable development and allow greater transparency into model design and performance.
If some important medical information has been missing or overlooked, it can result in the wrong diagnosis and harm the patient’s life. Medical imaging helps to detect illnesses at earlier stages improving not only a general level of medical help but saving people’s lives as well.
To significantly affect the efficiency of medical imaging, AI-based applications have to become a daily usage in each healthcare institution. There are some important aspects where AI can be helpful in revolutionizing medical imaging.
Can AI-driven medical imaging applications once replace physicians in the diagnostics process? Sometimes you can find similar thoughts claiming that deep learning and other AI technologies are a big threat to radiologists. Reality shows us that it is just the opposite. The problems described above can in fact be solved more quickly and easily with the digital help of new technologies, allowing radiologists to perform more expertise-based tasks like offering more personal medical care and paying more attention to each case.
So, AI-based medical imaging applications can’t replace radiologists and diagnosticians now, they will stand up for them and help by processing medical visual information. ML-driven models can inspect and process large quantities of information, inspect details, analyze and send the results to doctors.
For instance, if a physician has to make a diagnosis for somebody with chest pain, the following information is essential:
To inspect all this data manually, the doctor will need a lot of valuable time. AI-based software with access to an electronic health record (EHR) can gather the information and make suggestions about possible diagnoses within minutes.
The relevant results will be sent to the doctor in detail and in an easily readable format. In some urgent cases, it can even save patients’ lives. Otherwise, it can free up a doctor’s time for other cases, better the general level of medical help, and boost patient outcomes.
AI-based tools offer many advantages such as time savings, improved performance, case rating by prioritization, and earlier detection of cancers.
AI-based software supports radiologists with complex cases of chest abnormalities, helping them minimize risks. According to studies, AI-supported technology has 97% to 99% accuracy and increases workflow efficiency by decreasing the total report reading time by 34%. Radiologists can customize detectable findings and their visualization methods according to the user’s clinical environment.
AI apps help radiologists detect nodules, atelectasis, cardiomegaly, fibrosis, calcification, consolidation, mediastinal widening, pleural effusion, pneumoperitoneum, and pneumothorax. It also supports tuberculosis screening.
With AI’s help, radiologists can automatically detect small, subtle pulmonary nodules during regular checkups and reduce false-negative cases.
Although AI is doubtlessly changing the healthcare industry, this technology is still relatively new. It causes the knowledge gap that remains an obstacle to AI implementation. Therefore, education will be the most crucial issue in implementing AI.
There are also other limits of AI in Medicine:
Several years ago it happened for the first time that an ML-based application successfully and precisely identified two different types of lung cancer. The diagnostics of cancer and staging cancers still remains a big problem for pathologists. Even very experienced doctors can sometimes make mistakes in the diagnosis or argue about details.
In contrast to people, medical imaging software can easily avoid subjectivity, which is one of the biggest advantages. To make a decision more evidence-based, medical imaging apps can identify on pictures sufficiently more aspects and details than a human eye can observe. With the help of precise medical software, doctors can offer more targeted and personalized cures. The advantages of AI in medical imaging can significantly help researchers in discovering new ways of treating diseases as well.
Medical imaging can be a great help when it comes to retrospective screening. Previously programmed and trained algorithms effectively inspect and compare visual data containing all medical history imaging records. It helps to uncover medical conditions overlooked or not discovered previously.
For instance, a person suffers from symptoms that let a physician think about lung cancer. To make a holistic diagnosis a doctor needs a lot of medical history information about the general condition of a patient’s health, previous treatments, surgeries, labs, and medications. Medical imaging applications can quickly and effectively help to analyze all data from medical history records and review all previous CT scans as well. It allows doctors to prove that a patient may or may not actually have lung cancer. Also, genomic results show a patient’s genetic predisposition to developing certain types of cancer and different other health conditions.
Let us summarize the most significant benefits of using AI for medical imaging and make a list of some use cases. ML-based software can help:
VITech develops AI-powered custom medical imaging apps for the precise analysis of medical images in different file formats. Our medical image analysis software can scan, compare and analyze medical images quickly while avoiding errors made by humans.
Contact our experts to learn more about how artificial intelligence and advanced technology may be used to enhance treatment outcomes and optimize care pathways.
Predictive analytics in healthcare: benefits and challenges
Modern technologies, including Big Data and machine learning, have opened new horizons for the predictive analytics and decision support systems market. With proper use, they will be the next step in the digital transformation of any industry, including healthcare. Today, the market for predictive analytics is exceptionally fast-growing. According to a study by AMR, from…
State of data science and ML in healthcare
VITech is pleased to reveal the results of the State of data science and ML in the healthcare survey that we conducted on LinkedIn in 2019. The survey sought to look into the scope and patterns of data science and machine learning adoption in the healthcare industry. Over 50 qualified respondents represented a variety of…
How is predictive analytics used in healthcare: TOP 10 examples
Smart healthcare is the future of the healthcare system. This revolution is already impacting the daily work of healthcare professionals and the practice of patient care. The changes taking place can provide solutions to many problems. Still, they also require us to rethink how we organize the health system, shifting the focus from treatment to…
ML-based system or why we use сomputer-aided systems in healthcare
Healthcare companies — providers or payers — have historically relied on computers for administrative tasks. However, new use cases have emerged as technology matured and the industry digitized. Today, hardly any clinic operates without a fleet of computers to store and manage patient/facility data, monitor patients and equipment, perform operations, and research. The advance in…
Benefits of EHR: Advantages and disadvantages for patients and medical staff
According to the analytical agency Frost&Sullivan, the market for digital medical solutions in 2021 amounted to $6 billion. At the same time, annual growth approached the 40% mark. This means that in the world’s developed countries, there is a significant growth in electronic medical records, the possibility of remote patient management, and the sale of…
How to provide diagnostics accuracy while lacking time
Poor systems deliver poor results, and, in the case of US healthcare, the pile of problems has been growing for years. From lack of transparency to high costs and administrative inefficiency, the system has created an environment where patients and medical staff suffer.
Human factors in safety control: epidemiological safety at risk
Organizations in every industry must ensure a safe working environment for employees and achieve safety compliance enterprise-wide. However, despite stringent regulations, regular safety drills, and safety management systems, non-fatal and fatal injuries in the workplace are still an issue for businesses. According to National Safety Council research, the total cost of workplace injuries in 2018 reached…