Artificial intelligence in radiology decision support systems, Medical Imaging and Healthcare

AI software algorithms help access vast quantities of patient data and images, machine learning software can ingest medical textbooks and care guidelines and review examples of clinical cases, AI has the advantage of reviewing hundreds of rare studies from archives to become proficient at reading them and identify a proper diagnosis, unlike the human mind, it always remains fresh in the computer’s mind.

Artificial intelligence in healthcare

AI helps quickly sift through massive amounts of big data or offers immediate clinical decision support for appropriate use criteria, the best test or imaging to make a diagnosis or offer differential diagnoses, Artificial intelligence will diagnose patients and replace doctors, it will find the key, relevant data they need to care for a patient and present it in a concise, easily digestible format.

Artificial intelligence (AI) helps clinicians, It is also called deep learning, machine learning or artificial neural networks, Machine learning software serves as a very experienced clinical assistant that improves the patient’s care and makes workflow more efficient.

AI Systems select appropriate empiric antimicrobial regimens more frequently than physicians, They can decrease the consumption of broad-spectrum antibacterial agents in the ICU and with a statistical increase in antimicrobial susceptibility among Pseudomonas, Acinetobacter spp., and Enterobacteriaceae isolates.

Electronic medical records (EMRs) can capture all sorts of data about the patient, This includes imaging data, exam and procedure reports, lab values, pathology reports, waveforms, data automatically downloaded from implantable electrophysiology devices, data transferred from the imaging and diagnostics systems themselves, as well as the information entered in the EMR, admission, discharge and transfer (ADT), hospital information system (HIS) and billing software.

The patients can upload their data and images to their EMRs, This will include images shot with their phones of things like wound site healing to reduce the need for in-person follow-up office visits, It also will include medication compliance tracking, blood pressure and weight loss, blood sugar, anticoagulant INR and other home monitoring test results, and activity tracking from apps, wearables and the evolving Internet of things (IoT) to aid in keeping patients healthy.

AI in radiology advantages

Artificial intelligence in radiology can help physicians make decisions about their patients’ care, Computer-based systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses, Artificial intelligence is valuable for radiologists and pathologists looking to accelerate their productivity and improve their accuracy.

Medical imaging data is one of the richest sources of information about patients, With megapixel upon megapixel of data packed into the results from X-rays, CAT scans, MRIs, and other testing modalities, combing through extremely high-resolution images can be challenging for the most experienced clinical professional.

AI tools can perform better than human clinicians at identifying features in images quickly and precisely, Measuring various structures of the heart can reveal an individual’s risk for cardiovascular diseases or identify problems that may need to be addressed through surgery or pharmacological management, Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors.

Using artificial intelligence to identify left atrial enlargement from chest x-rays could rule out other cardiac or pulmonary problems and help providers target appropriate treatments for patients, Similar AI tools could be used to automate other measurement tasks, such as aortic valve analysis, carina angle measurement, and pulmonary artery diameter.

Applying AI to imaging data may help to identify thickening of certain muscle structures, such as the left ventricle wall, or monitor changes in blood flow through the heart and associated arteries, Algorithms could automatically populate reports, saving time for human clinicians, and identify measurements or values that qualify as abnormal.

The fracture type is difficult to detect on standard images, but AI tools may be more likely to see subtle variations in the image that could indicate an instability that requires surgery, Allowing unbiased algorithms to review images in trauma patients may help to ensure that all injuries are accounted for and receive the care required to secure a positive outcome.

An AI algorithm could assess x-rays and other images for evidence of opacities that indicate pneumonia, then alert providers to the potential diagnoses to allow for speedier treatment, AI can help to identify high-risk patients when the pneumothorax is suspected, especially when radiologists are not present.

Artificial intelligence may be able to help prioritize the type and severity of pneumothoraces, which may change the urgency of treatment, AI may also be able to help providers monitor patients over time, Medical imaging is used in routine, preventive screenings for cancers, such as breast cancer and colon cancer.

For patients with established cancers, AI could support the detection of malignancies that have spread, Extranodal extension (ECE) of cancers is associated with poor prognosis, and is often only discovered at the time of surgery, AI could be useful for head and neck cancers, prostate cancer, colorectal cancers, and cervical cancer.

AI algorithms can read medical images similar to radiologists, by identifying patterns, AI systems are trained using vast numbers of exams to determine what normal anatomy looks like on scans from CT, magnetic resonance imaging (MRI), ultrasound or nuclear imaging, Then abnormal cases are used to train the eye of the AI system to identify anomalies, similar to computer-aided detection software (CAD).

AI system will call up all the relevant data specific to prior cardiac history, Pharmacy information regarding drugs specific to COPD, heart failure, coronary disease and anticoagulants, Prior thoracic or cardiac procedures, Recent lab results, and any pathology reports that relate to specimens collected from the thorax.

AI system will call up 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 full information (such as history of high blood pressure, coronary blockages, history of smoking, prior pulmonary embolism, cancer, implantable devices or deep vein thrombosis).

AI is used in prior chest imaging studies, Cardiology report information, Medications the patient is currently taking, Patient history relevant to them having COPD and a history of smoking that might relate to their current exam, Recent lab reports, Oncology patient encounters including chemotherapy, and Radiation therapy treatments.

AI enhances radiology reading, not replace radiologists, AI requires big data, massive computing power, powerful algorithms, broad investments and a lot of translation & integration from a programming standpoint before it can be commercialized, Radiologists employ artificial intelligence (AI) techniques that allow computers to diagnose diseases.

Benefits of CDSSs (Computerized decision support system)

CDSS  is used in making wise antimicrobial treatment decisions and to assist the antimicrobial stewardship program with the identification of patients for potential intervention, Decision support (DS) systems are based on artificial intelligence (AI), they can improve the diagnostic performance of radiologists.

Decision support systems can use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning, AI tools can aid decision-making in diagnostic radiology, As these systems become a part of routine clinical care, radiologists must understand the strengths and weaknesses of each type of decision support tool to improve diagnostic accuracy.

Electronic decision support systems improve patient safety through reduced medication errors and adverse events and improved medication and test ordering, CDSSs improve the quality of care by increasing clinicians’ available time for direct patient care.

CDSSs improve efficiency in health care delivery by reducing costs through faster order processing, reductions in test duplication, decreased adverse events and changed patterns of drug prescribing cheaper but equally effective generic brands.

Computerized decision support systems (CDSS) improve the quality of health care delivery, The rapid expansion of the use of electronic medical records and automated surveillance systems offer an opportunity for the use of CDSS in antimicrobial stewardship programs, however, the capabilities of currently available systems are quite variable.

Electronic decision support systems offer the automatic provision of relevant, personalized expert advice, expertise and recommendations sourced from up-to-date, best practice knowledge, they reduce variation in the quality of care, they can support medical education and training, they can help overcome problems of inefficient coding of data.

Electronic decision support systems can be cost-effective after initial capital costs and update and maintenance costs, they can offer immediate feedback to patients, If integrated with an EMR, they can help streamline workflow and encourage more efficient data gathering, They can offer an audit trail and support research, they can maintain and improve consistency of care, they can supply clinical information anytime, anywhere it’s needed.

Artificial intelligence and machine learning have captivated the healthcare industry as these innovative analytics strategies become more accurate and applicable to a variety of tasks, AI is increasingly helping to uncover hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets.

Drawbacks of CDSSs

Electronic decision support systems have the potential ‘deskilling’ effect, they can be perceived as a threat to clinical judgment, they can be considered too inflexible, Promote over-reliance on software, and limit clinicians’ freedom to think.

Electronic decision support systems are difficult to evaluate – lack of accepted evaluation standards, they can be time-consuming to use, possibly lead to longer clinical encounters and create extra work, Uncertain and untested ethical and legal status, Costs of maintenance, support and training are required after the initial outlay, A clinician’s experience and imagination can’t be duplicated in a computer application.

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