Innovative technologies such as machine learning and artificial intelligence in the healthcare industry reduce costs and achieve better results. Large volumes of image data, combined with faster and more complex computer processing capabilities, have opened the possibilities for machine learning and pattern matching to be applied in medical imaging.
The world market for artificial intelligence (AI)-based clinical applications for use in medical imaging is set to reach almost $1.5 billion by 2024 despite a slower-than-expected uptake of these products and the impact of the COVID-19 pandemic. This is according to a new report from Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry.
Table of Contents
- Where does Artificial Intelligence Will Help in Medical Imaging?
- 3 Key Benefits of Using AI in Enterprise Imaging
- Faster, Better Patient Outcomes
- Automated Study Prioritization
- Measured and Improved Performance
- Final Words
- Build AI-enabled Medical Imaging Software with Dash
The introduction of AI in the medical imaging field is quite promising. The detection and diagnosis of many illnesses show ingenious improvement.
AI-based medical imaging software can give the healthcare sector viable solutions. The digital data generated during scans can be handled effectively, and the best results are obtained.
This blog shows how AI can help develop advanced software for medical imaging. Let’s dive in.
Where does Artificial Intelligence Will Help in Medical Imaging?
In the next few years, artificial intelligence will play a significant role. AI will neither diagnose patients nor replace doctors; it will increase their capacity to discover the essential facts they need to look after their patients and deliver them in a clear, easy-to-digest style. When a radiologist calls up a chest-computed (CT) scan, the AI will review the image and identify likely results and the history of patients associated with individual scanned anatomy. If the exam order is for chest pain, the AI system will call up:
- All the relevant data and prior exams specific to former cardiac history.
- Pharmacy information regarding drugs specific to COPD, heart failure, and coronary disease.
- Previous imaging exams from any modality of the chest may aid in diagnosis.
- Preliminary reports for that imaging.
- Prior thoracic or cardiac procedures.
- Recent lab results.
- Any pathology reports that relate to specimens collected from the thorax.
Patient history from prior reports or the EMR that may be relevant to potential causes of chest pain will also be collected by the AI and displayed in brief with links to the complete information (such as the history of aortic aneurism, high blood pressure, coronary blockages, history of smoking, prior pulmonary embolism, cancer, implantable devices or deep vein thrombosis).
To know about the role of AI in healthcare, read our blog How AI is Changing the Future of the Healthcare Industry
|· AI and ML have the potential to transform the way health care is conducted, with AI and ML solutions complementing the work of physicians to enable the development of new treatment paradigms.|
|· Blockchain is the area with the fastest growth. It is worth watching because the secure distributed storage of medical data outside medical institutions is necessary for enabling patients to access and control their data.|
|· 3-D visualization technologies allow physicians to “see” the patient in new ways, with greater precision that supports more effective treatments and better clinical outcomes.|
3 Key Benefits of Using AI in Enterprise Imaging:
With AI as a driver, provider organizations can realize three key benefits:
1. Faster, Better Patient Outcomes
Time for treatment is a crucial determinant of therapeutic effects in many time-sensitive situations, such as acute ischemic strokes. Before treatment, CT scans should be started in Emergency Radiology within 25 minutes of the patient’s arrival. In 45 minutes, the interpretation of scanning must be accomplished.
For particular clinical results, such as examination of CT scans for intracranial hemorrhages related abnormalities, AI technology may carry out extensive picture analysis. When AI discovered acute anomaly is alerted to radiologists, they may react significantly faster to life-threatening cases.
2. Automated Study Prioritization
Prioritization is essential in any work environment, but even more so in a fast-paced clinical setting. AI-enabled systems drive efficient worklist prioritization, as they continually communicate the results of image analyses. AI can also help ensure that such studies are automatically assigned to the most appropriate available physician.
3. Measured and Improved Performance
The consolidation in one unified list of tasks, like quality, communication, and interpretation, can help measure and boost productivity by providing an AI-driven intelligence solution that drives efficient and accurate imaging and proves the general value of enterprise imaging in the entire health system.
In addition, an integrated workflow solution may offer a large variety, including contact with Radiology-EDs, mammography imaging, technologist QA, and even an anonymous peer review to guarantee that quality is always at the forefront.
AI is undoubtedly an exciting crossroads for medical imaging. AI increases the capacity to process a large number of medical images and has a promising future. Besides the excitement, improvements are still needed before it gets stronger. In medical imaging, however, AI can play an important role. The way individuals handle the vast quantity of images improves patient care and reduces scanning times may be changed.
About AI capabilities, we are still scratching the surface. With increasing consumer confidence in AI-based healthcare solutions, the medical imaging landscape accelerates. All you need to do is keep developing robust software that can enable radiologists with improved diagnostic accuracy.
Build AI-enabled Medical Imaging Software with Dash:
Despite the many challenges, it is exciting to bring medical imaging AI solutions to the market. Radiology AI will grow in leaps and bounds. So, if you are looking at developing robust medical imaging software, talk to us.
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