Artificial intelligence is a buzzword in the world of medicines and healthcare. Among the top beneficiaries is the field of radiology in which the AI tools have just begun to prove absolutely beneficial in comprehending the diagnostic imaging results for improved decision-making and patient care. At the 2017 meeting of Radiological Society of North America (RASNA), it was anticipated that the radiologists that use AI would soon replace the radiologists who don’t.
Also, there are many market studies and surveys that confirm that AI would experience exponential growth in its contribution towards medical imaging. According to Signify Research, the global market for AI and machine learning in medical imaging would grow to $2 billion by 2023. The market data and insights provider goes further to present the following picture:
The marketing intelligence firm Tractica, in its report titled Artificial Intelligence for Healthcare Applications, reported that medical imaging’s share in the AI industry is likely to be $19 billion by 2025. In a report published in October 2018, The National Center for Biotechnology Information confirmed that there has been a dramatic increase in the number of publications on AI and radiology from 100-150 per year in 2007-08 to 700-800 per year in 2016-17. All these facts indicate that radiology is at the vanguard of utilizing AI innovations.
In October 2018, the American College of Radiology Data Science Institute presented numerous use cases in which the AI tools can be utilized to enhance the diagnostic imaging procedures, results and analysis. Some of these cases include:
- Recognizing the cardiovascular issues by detecting the left atrial enlargement, left ventricle wall thickening and other similar situations which cause cardiovascular abnormalities.
- Identifying the bone and joint issues from fractures and dislocations to injuries in the muscles and soft tissues.
- Detecting thoracic conditions like pneumothorax and pneumonia.
- Identifying the cancers through more accurate screening of malignancies
- Diagnosing neurological conditions like amyotrophic lateral sclerosis
Going further, AI can also be of great benefit in improving the efficiency of the systems like PACS (Picture Archiving and Communication System) and EMR (Electronic Medical Record). For example, while analyzing the latest diagnostic imaging reports of a patient, the AI tool can be used to instantly recall and refer to the relevant data from the patient’s past medical records, tests, imaging exams, lab tests as well as patient’s medical history. Then, the AI and the radiologist would have a better understanding and analysis of the possible issues and that too, without any delay.
So, what are the concerns? At the annual RASNA meeting of 2018, the concern about the possibility of using AI to hack the hospitals’ devices and medical records as discussed. The likelihood of someone developing an AI algorithm to tamper with mammograms and other medical imaging reports can’t be ruled out in the future.
In order to address these vulnerabilities, it is important that the software and hardware vendors, as well as the hospitals and clinics and other medical facilities, take proactive actions. In fact, machine learning and deep learning can be utilized to develop systems that can detect malicious hacking attempts. Only then, the full potential of AI can be harnessed to take medical imaging to the next level.
sepStream® offers cloud-based PACS and EMR solutions that are reliable safe and capable of establishing compatibility with the AI tools. Using these solutions, radiologists can establish an efficient radiology information system to securely store and communicate patient records in real times. Patient tracking and easy management of billing information and insurance information are among the salient features of these systems.