Significance of Artificial Intelligence in the Health-care Industry

Due to the increased number of heart-related diseases in the American populace, it has become pertinent that we seek a holistic and tech-phased approach to the ever-growing number of concerns associated with heart-related diseases. The cardiology medical practice is one of the most sensitive parts of medical health practice due to the delicacy involved and the methodological intricacy. This has created the ever-important need for the usage of AI in the biomedical world. This data-oriented solution mechanism has now become an all-important medical subject and the last resort for very delicate and complicated medical situations. From my vast knowledge and wealth of experience, I understand the very concerns that the public might be having but it’s not to say that this concern isn’t warranted as AI is still a very new topic to some part of the global community. 

AI in short term is a product and combination of a sophisticated statistical and mathematical model in creating complex value-driven models capable of emulating super-human intelligence with insights from a defined dataset. AI is set to create a paradigm shift in the cardiovascular field as baseless assumptions and low performance diagnostic and prognostic analysis will be discarded. This can provide room for significant therapeutical improvement and a well-rounded modus operandi in cardiovascular disease treatment (Rajpurkar & Irvin, 2019). 

To improve our current diagnostic modalities and leverage AI in sound clinical decision-making, there is the capability of creating a computer-based algorithm to help filter what therapy to use and which to avoid (Brown & Cam, 2018). Recent advances and increased tech-aligned personalization in the medical field have spurred numerous debates on AI capability and its potential value to the cardiological field. It’s an opportunity to allow doctors to be more of a doctor by helping to make their work easier. This helps to save a lot of cognitive load and time on the part of the doctor, and also create room to attend to all patients. 

AI has the capacity to shake away all the standard excuses we make as to why we can’t screen people for these conditions, and also help in the early detection of treatable and sudden death-associated syndromes. By mining EHR (Electronic health records), we can get a comprehensive health record system, understand these data and use them in improving health care quality. AI algorithms have a feedback loop and they are constantly updating their theory, hence, it’s always accurate and rich in insights. EHR data continues to grow exponentially and on the other hand, biomedical knowledge is active and dynamic in such a way that the human brain is not able to process and manage to make it difficult for humans to keep up-to-date with new findings and data. 

This panorama expatiates on the need for a reorganized and much productive health structure with the capacity to handle new challenges and perspectives. And here comes AI and all its superficial benefits. 

There is a huge demand for medical health practitioners and this will result in a higher cost of resources to facilitate treatment and to maintain the required health standard. One way that AI can help in revenue maximization is through the use of IoMT (Internet of Medical Things). The use of the IoMT in consumer healthcare applications is already helping people to stay healthy. This encourages healthier behavior and lifestyle with proactive management of lifestyle and puts users in control of health and well-being while assisting health care practitioners to better understands day-to-day patterns and needs of people. This is to provide guidance, feedback, and support. With this in place, the cost of personnel management is reduced as only a small number of staff will be required to manage clients. 

In addition, AI can also help in the early detection of diseases through wearable devices and other high-end technology (Zhang & Zhu, 2018). This will help in enabling faster review and reduce the need for hospitalization. This will enable caregivers to better monitor and detect life-threatening diseases at earlier stages making them more treatable, thereby reducing costs for all stakeholders. 

Furthermore, treatments are made easier using high-tech prognosis and diagnosis equipment. AI can help practitioners in a comprehensive solution-based approach in disease management, patient management, and patient rehabilitation. Robots are also now being deployed to carry out repetitive tasks and physical therapy, thereby saving cost and time for medical practitioners. 

AI will be very much important in medical research as many phases, clinical trials and testing can be simulated to ease decision making and results from invalidation. This will help in reducing the much abominable cost it takes to develop a drug from lab to patient Advances in AI will also help to streamline drug discovery and aid repurposing thereby reducing cost and maximizing resources which will dovetail into larger revenue. 

More also, AI can help in the training of medical practitioners as it has the capacity to draw on a large database of events and also simulate real-life scenarios for better understanding. This will save the huge cost involved in conducting several practicals. In summary, this will help to improve patient care, better user experience, and brand image. 

The use of the Tensorflow library and its analysis can help in the classification, perception, understanding, discovery, prediction, and creation of models (Hinton & Salakhut, 2016). Applications include sentiment analysis, smart reply, speech recognition, image and video recognition, time series, self-driving cars, text summarization, and sentiment analysis. It is an open-source app that uses an algorithm known as a neural network to process information (Bogunovic & Waldstein, 2017). It enables computers to identify data and learn patterns. It also allows the creation of a large-scale neural network with many layers used for the analysis of data points. Trained models such as BERT, R-CNN, TFlite, and Deep face are typical examples of Tensorflow models used in the analysis. 

The BERT model can be used in the pre-training of deep bidirectional transformers for language understanding. Its also used for sound detection and text comprehension. Bidirectional Encoder Representations from Transformers model can use its independent noise detection capacity in cardiology and will be applicable in sound detection (heart rate, pulse rate, and heart-beat) to give the specific signal. R-CNN is used in object detection and segmentation. This is used in pattern recognition and objects detection and can be very helpful in detecting outliers and anomalies from images gotten in medical health practices for disease identification. TFlite assists in running ML models on wearable devices with low latency and performs functions such as classification, regression, etc without necessarily contact with the server. This is very helpful in IoMT.  The deep face is a hybrid model for facial recognition by helping to identify human faces in digital images. This model is also applicable in the health system for bio-detection and distinct identification. It helps in classification based on pre-identified image and recognition. This is also helpful in the early detection of life-threatening diseases.  

Image recognition is also another key part of AI and has applications in various industries. AI models are used to identify objects, people, places, and images in general. With the aid of convolutional neural networks (CNN) which works like the human visual cortex. Data is divided into pieces and images are analyzed in bits. It is engineered to efficiently process images, correlate images and comprehend vital information from them. AI also has wide applicability as image classification and object detection is gotten at full scale. Cardiologists use various images of the heart in their diagnosis and rather than carrying out cost-intensive tests, images can be used for swift diagnosis and examination of patients by running the image through a trained algorithm on disease detection.  

ANN’s ability to learn gives birth to the glamorous potentials as applicable in disease detection and diagnosis in cardiological healthcare. Sequentially, trained neurons pick images and segment them, then do the feature analysis and the classify image to pick insights and validate authenticity, similarity, or differences. These basics are what in full extrapolation details image recognition and pattern matching. 

In the cardiovascular segment, AI will be very much helpful in image analysis and real-time monitoring in conjunction with EHRs. They will also help in moving closer to patients and providing personalized health care. They can help extract information from images with ease at a faster processing power. Echocardiography is a timely and cost-effective evaluator of cardiac structure and function using its imaging modalities.

Algorithms in AI are already being used in image processing used to perform automatic single-photon emission, reconstruction, computed tomography, tomographic oblique reorientation, myocardial perfusion imaging, motion correction, high-level result analysis, and quantification. From these studies, an improvement in the prediction of obstructive CAD was birthed. Furthermore, ML algorithms and systems embedded in the EHR can help identify risk information and create accurate predictions in regions with unique HF risk factors, modify itself if HF risk features change, and incorporate wearable devices, AI-enabled ECG or image analysis, and other data in the HF risk prediction algorithm. 

Investigations also show that trained neural networks have a higher performance than expert physicians and are perfect in identifying which coronary arteries are much likely to have stenotic lesions for any specific hypo-perfused distribution (Ghosh & Das, 2019). 

More also, AI approaches are used in the prediction of revascularization in patients with suspected CAD from various studies with self-correlating invasive angiography within 90 days of the first MPI scan. Predictive modeling will be used in this case by ensuring that past incidents and problems attributed to the tire pressure monitoring system and their data for the past five to ten years are used in modeling and simulating that situation and how those problems can be prevented. 

I understand that numbers will provide satisfactory clarifications on this, hence, a little mathematical explanation will do. With the implementation of AI, the number of patients treated per day will move from the present 60 to 200 due to telemedicine and virtual nursing practice. . Isn’t that magnificent? New efficiency in treatment, patient care, and administration will move from 81 percent to 93.8 percent as AI treatment system, virtual nursing, echocardiography, electrocardiogram, CT scans, and AI-powered drug development will be in play. The cost of hospital administration reduce by 46% which is great as AI diagnostic and treatment as well as telemedicine will be in play. Operating profit will surge up to 39% will increase resources for development and other beneficial projects. Furthermore, with the implementation of AI, the return on investment of $20 million will be 39.6% for a year and it will take just 223 days for us to break even on the funds spent.

In the cardiovascular segment, AI will be very much helpful in image analysis and real-time monitoring in conjunction with EHRs. They will also help in moving closer to patients and providing personalized health care. They can help extract information from images with ease at a faster processing power. Echocardiography is a timely and cost-effective evaluator of cardiac structure and function using its imaging modalities. 

Despite this availability for diagnostic and point-of-care applications, there are still many concerns about the accessibility, diagnostic, and qualitative utility of this system. The usage of echocardiograms is overly dependent on its user’s experience, hence creating an opportunity for standardization and augmentation through AI due to the available voluminous clinical data creating ample opportunity for the creation of AI-dependent echocardiographic platforms.  This will help to improve workflow standardization, interpretation, and automation of pathological functions (valve disease, regional wall motion abnormalities, and cardiomyopathies detection and monitoring), and usage of data at the point of care on a need-basis. Its strength lies in the swift detection of unrecognized or non-easily detected subclinical diseases and in-patient prognosis. 

Algorithms in AI are already being used in image processing used to perform automatic single-photon emission, reconstruction, computed tomography, tomographic oblique reorientation, myocardial perfusion imaging, motion correction, high-level result analysis, and quantification. From these studies, an improvement in the prediction of obstructive CAD was birthed. Furthermore, ML algorithms and systems embedded in the EHR can help identify risk information and create accurate predictions in regions with unique HF risk factors, modify itself if HF risk features change, and incorporate wearable devices, AI-enabled ECG or image analysis, and other data in the HF risk prediction algorithm. Investigations also show that trained neural networks have a higher performance than expert physicians and are perfect in identifying which coronary arteries are much likely to have stenotic lesions for any specific hypo-perfused distribution (Rajpurkar & Irvin, 2019). 

More also, AI approaches are used in the prediction of revascularization in patients with suspected CAD from various studies with self-correlating invasive angiography within 90 days of the first MPI scan. This will create a significant impact in this field dovetailing into improved health outcomes and a better-personalized approach to create an efficient healthcare system whilst creating a personalized system. AI can prove to be a tremendous and fundamental tool in cardiovascular healthcare as it can be a robust system with a well-detailed approach to solving health complications and conundrums.

Of course, there is the possibility of issues and complications arising as a result of the new implementation and projects. Issues such as cultural affiliation and change can be humdrum. Hence, Lewis’s model for cultural change will be implemented during the transition process. We will ensure that we are very close to the system and assist all the stakeholders during the process by enlightening and positive re-education. With this, the issue of mistrust that can stem from this will be also avoided.

Proper documentation of all processes and activity will be done to ensure a harmonious co-existence and cooperation from all stakeholders. This and proper communication of all activities will always be duly communicated to all stakeholders. More also, smart and practical goals will always be in the implementation and will always be communicated to all stakeholders. This is to forestall undue pressure and unrealistic management expectation. 

In the implementation, our legacy soft wares, systems, and devices will all be updated and upgraded where applicable. Replacement where necessary will be carried out to compatibility complications. Data will be cleaned and labeled properly to ensure seamless execution and access. The data ecosystem has to be in proper shape. 

Techniques to be used will include tensor flow. The use of the Tensorflow library and its analysis can help in the classification, perception, understanding, discovery, prediction, and creation of models. Applications include sentiment analysis, smart reply, speech recognition, image and video recognition, time series, self-driving cars, text summarization, and sentiment analysis. It is an open-source app that uses an algorithm known as a neural network to process information. It enables computers to identify data and learn patterns. It also allows the creation of a large-scale neural network with many layers used for the analysis of data points. Trained models such as BERT, R-CNN, TFlite, and Deep face are typical examples of Tensorflow models used in the analysis. The BERT model can be used in the pre-training of deep bidirectional transformers for language understanding. Its also used for sound detection and text comprehension. 

Bidirectional Encoder Representations from Transformers model can use its independent noise detection capacity in cardiology and will be applicable in sound detection (heart rate, pulse rate, and heart-beat) to give the specific signal. R-CNN is used in object detection and segmentation. This is used in pattern recognition and object detection and can be very helpful in detecting outliers and anomalies from images gotten in medical health practices for disease identification. TFlite assists in running ML models on wearable devices with low latency and performs functions such as classification, regression, etc without necessarily contact with the server. This is very helpful in IoMT.  The deep face is a hybrid model for facial recognition by helping to identify human faces in digital images. This model is also applicable in the health system for bio-detection and distinct identification. It helps in classification based on pre-identified image and recognition. This is also helpful in the early detection of life-threatening diseases.  Others include Pytorch, Keras, CNTK, Caffe, Scikit-learn, and Theano. Techniques to use in the AI projects are basically regression, classification, support vector mechanism, clustering, machine vision, heuristic, time series, Markov decision process, optimization, natural language, deep learning, and anomaly detection

Feedback is expected and will be used for improvement, development, and further projects. In summary, this doesn’t mean that the role of physicians will be jeopardized but will aid them to perform their functions effectively with evidently high accuracy alongside patient-centric healthcare management.  

References

Bogunovic, H., & Waldstein, S. (2017). Prediction of anti-VEGF treatment requirements in Neovascular AMD using a machine learning approach. Invest Ophthalmol, 58 https//doi:10.1167/iovs.16-21053.

Brown, J.M., & Cam, A.L. (2018). Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol, 136 https//doi:10.1001/jamaophthalmol.2018.1934.

Ghosh, S., & Das, N. (2019). Understanding deep learning techniques for image segmentation. https//arxiv.org/abs/1907.06119. 

Hinton, G.E., & Salakhut, R.R.(2016). Reducing the dimensionality of data with neural networks. Science, 313(112). https//doi:10.1126/science1127647

Rajpurkar, P., & Irvin, J. (2019). Radiologist-level pneumonia detection on chest xrays with deep learning. https://arxiv.org/abs/1711.05225. 

Zhang, Q., & Zhu, S. (2018). Visual interpretability for deep learning: A survey. https://arxiv.org/abs/1802.00614.