AI in healthcare sector simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost.
Infrastructure and regulations are created by governments and authorities all over the world to provide a better medical environment for patients. The current healthcare system has some inefficiencies, like uneven distribution of healthcare experts, a high rate of primary clinician misinterpretation, a lengthy training period for practitioners, a shortage of clinicians in underdeveloped areas, and untraceable medical costs.
However, as Machine learning in healthcare technology has advanced over the last few years, AI in the medical field has progressed from a theoretical concept to a practical application. It is becoming clear that artificial intelligence has numerous applications in medicine.
Applications of AI in Healthcare Sector
Furthermore, The future of AI in healthcare Sector has the potential to impact the development of the healthcare sector and raise the caliber of medical practitioners in below mentioned areas :
1) AI in Radiology
Radiology is a medical specialty that uses imaging technology to diagnose and treat diseases. AI in health care can play an important role in the various fields that :
Radiomics: feature extraction from diagnostic images. A radiomics analysis can extract over 400 features from a region of interest in a CT, MRI or PET study and correlate these features with each other and with other data far beyond the ability of the human eye or brain to detect. Such features can be used to predict prognosis and response to treatment. AI in medical field can support radionics feature analysis and help correlate radionics with other data (proteomics, genomics, liquid biopsy, etc.) by creating signatures of patients.
Imaging biobanks: The fast storage capacity of computers allows the storage of huge amounts of data. Quantitative imaging can generate interferometric biomarkers from multiple sources, which can then be stored and organized in large imaging biobanks and used to predict disease risk in large population studies and response to treatment. AI can be used to visualize disease progression and evolution.
Dose optimization: radiation dose and standard of care vary both within and between institutions to promote and develop a comprehensive approach to medical radiation protection. In this situation, artificial intelligence technologies (AI) can be an optimization tool to help technologists and radiologists select an individualized patient protocol, track patient dose parameters, and provide an assessment of risks related to cumulative dose and patient sensitivity, such as age and other clinical parameters.
Structured reporting: AI in healthcare projects can support the reporting process, integrate various quantitative data sets, and then indicate the most likely diagnosis. These include radiology case collections, structured image annotations, clinical research case report formats, computerized reporting systems, and clinical radiology reports CDEs can serve as the AI’s lexicon to create a customized structured report for a patient.
Physicians’ workflows could be improved, and we could offer decision support or “augment” our capabilities. For example, AI technologies and techniques could be used to automatically identify poor-quality photos for telemedicine or help triage cases; many of these functions would be performed in collaboration with physicians rather than in their place.
Dose optimization: radiation dose and standard of care vary both within and between institutions to promote and develop a comprehensive approach to medical radiation protection. In this situation, artificial intelligence (AI) can be an optimization tool to help technologists and radiologists select an individualised patient protocol, track patient dose parameters, and provide an assessment of risks related to cumulative dose and patient sensitivity, such as age and other clinical parameters.
Structured reporting: AI in the medical field can support the reporting process, integrate various quantitative data sets, and then indicate the most likely diagnosis. These include radiology case collections, structured image annotations, clinical research case report formats, computerized reporting systems, and clinical radiology reports CDEs can serve as the AI’s lexicon to create a customized structured report for a patient.
Physicians’ workflows could be improved, and we could offer decision support or “augment” our capabilities. For example, AI techniques could be used to automatically identify poor-quality photos for telemedicine or help triage cases; many of these functions would be performed in collaboration with physicians rather than in their place.
2) AI in cardiology
The successfully developed algorithm analyzed the scanned image of the patient’s eye and correctly inferred various types of data, including the patient’s age, blood pressure, and smoking status. As a result, it can predict the likelihood of the patient’s cardiovascular disease. The use of AI in cardiovascular medicine has promising prospects.
In the context of precision medicine, AI can initially be used for remote monitoring, medication reminders, real-time disease counselling, and early warning of symptoms. At the same time, from a clinician’s perspective, AI can help collect voice information (e.g., on medical history), link electronic medical records, and reduce clinician workload. It is highly likely that AI can be used to create a precise medical plan that personalizes healthcare for each patient.
Clinical Predictions :
Using machine learning and Big Data analytics, AI can help clinicians make more accurate predictions for patients, such as health improvement or recovery time.
Cardiac imaging analysis :
With the advent of Deep Learning in recent years, cardiac imaging analysis has shown realistic development prospects. Deep Learning can help in the interpretation of coronary angiography, echocardiography, and electrocardiograms (ECG). In recent decades, cardiac intervention has been the main treatment for cardiovascular disease. In the near future, AI will be able to detect coronary atherosclerotic plaques more accurately than clinicians using Deep Learning. In addition, AI can be used to analyze echocardiographic images, including automatically measuring the size of individual chambers and assessing left ventricular function. It can also be used to evaluate structural diseases, such as valvular heart disease, to aid in disease classification and grading.
Conclusion :
Could artificial intelligence be a better alternative for healthcare workers in the future?
Certainly not. The sole purpose of AI and machine learning is to better assist healthcare professionals in caring for patients, not to replace humans. We at AIACME Provide Cutting edge AI solutions for Your Health Care Operations.