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Innovative Informatics Tools and Applications To Clinical Practice

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Innovative Informatics Tools and Applications to Clinical Practice

AI, Machine Learning, Genomics, Precision Health, And Robotics, Applications, Benefits,

And Potential Challenges of The Innovations

Artificial Intelligence (AI) is the subfield of computer science concerned with creating

algorithms and computer systems that can perform tasks that normally require human intellect,

including speech recognition, natural language processing, and decision-making (Bini, 2018). AI

has many potential advantages, including enhancing the efficacy of complex processes

and improving the precision and accuracy of decision-making. Regrettably, AI is also affiliated

with various challenges. The potential for AI to automate tasks and replace human workers,

resulting in joblessness and economic disruption, is a significant concern. 

Machine learning, a branch of artificial intelligence, creates algorithms and models that

can automatically learn and improve from new information (Bini, 2018). Potential uses of

machine learning include consumer behavior prediction, medical data pattern recognition, and

business process optimization. Nevertheless, machine learning isn't without its own set of

problems. The potential for discriminating or unjust results due to biases in the data used to train

machine learning algorithms is a key cause for worry. 

"Genomics" refers to the study of an organism's or a population's whole collection of

genes and DNA sequences (Bustamante et al., 2011). Improvements in medicine and a deeper

knowledge of human biology might result from breakthroughs in genomics. Genomics can

enhance illness diagnosis, medication development, and individual medical care. Genomics

could present a few challenges as time goes on. The possibility of discrimination or

stigmatization due to genetic data collection and analysis is a serious ethical and privacy issue. 
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Precision health is a medical practice that aims to personalize therapy for each patient by

considering their specific biochemical, environmental, and behavioral characteristics (Fawaz,

2021). Possible advantages of precision health include better diagnosis, more efficient treatment,

and lower healthcare costs. Yet, precision health may potentially face certain challenges. The

high price tag and limited availability of certain precision health technology are key causes for

worry. 

Robotics encompasses designing, developing, and operating autonomous or remote-

operated devices (Kroff, 2019). There are numerous prospective applications for robotics,

including manufacturing, healthcare, and logistics. The possible advantages of robotics include

increased productivity, decreased expenses, and enhanced security. Nevertheless, robotics also

presents several potential challenges. The ability of robots to eliminate human workers and

worsen income inequality is a significant cause for concern. 

Potential To Improve Healthcare Practice And Related Outcomes.

AI can enhance healthcare outcomes by facilitating precise and expedient disease

diagnosis and treatment. AI can be used, for instance, for analyzing medical images and

detecting abnormalities that may be difficult for human specialists to detect.

Large datasets, like medical records and genomic data, may be utilized

in developing more tailored therapies with the assistance of machine learning. Additionally,

machine learning can help healthcare providers make more accurate diagnoses and predict

patient outcomes, resulting in better outcomes and lower healthcare costs.

Genomics can enhance healthcare outcomes by facilitating more precise diagnoses and

individualized treatments. For instance, genomic tests may detect high-risk patients for specific

diseases, allowing for early intervention and disease prevention (Ginsburg & Willard, 2009).
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Precision health can enhance healthcare outcomes by adapting treatments and

interventions to each patient's unique environmental, genetic, and lifestyle traits. Precision health

can also assist in identifying patients at high risk for specific diseases, allowing for early

intervention and disease prevention. 

Robotics can enhance healthcare outcomes by enhancing the efficiency and precision of

medical procedures. For instance, robotic surgery allows surgeons to execute complicated

procedures with greater accuracy and fewer complications. 

Whether the Technologies Integrate Big Data

AI, ML, genomics, Precision Health, and robotics applications might benefit from using

Big Data. However, the extent to which these applications use Big Data varies widely between

use cases and software.

Examples of where AI and ML are being used include the analysis of medical records

and other huge datasets to make more informed diagnoses and treatment decisions. As the

quality and amount of the data used to train the AI or Machine Learning algorithm determine the

system's accuracy and usefulness, Big Data plays a crucial role in this application (Bini, 2018).

To find genetic markers and other elements that might influence individualized treatment

recommendations, Genomics and Precision Health also depend on big data, including genomic

data and patient health records. 

However, the importance of Big Data integration in Robotics applications may be lower

due to the potential narrowness of the data utilized by Robotics systems. A robotic surgical

system, for instance, would not need massive volumes of historical data to function properly but

instead depend on real-time sensor data to direct its motions and avoid obstructions.

Difference Between AI, Machine Learning, Data Mining And

Deep Learning

The article by Bini (2018) describes the differences between AI, ML, DM, and DL as

follows: AI refers to the creation of intelligent devices that can carry out duties that normally

require human intellect, like speech recognition, natural language processing, and decision-

making. AI systems can be founded on rules or learning. 

Machine Learning is a subset of artificial intelligence that entails the creation of models

and algorithms that can learn and improve from data without even being explicitly programmed

(Bini, 2018). Data Mining is the process of identifying patterns and tendencies within large

datasets. Data Mining employs statistical methods and algorithms to examine and extract insights

from data. Data Mining serves various purposes, including identifying customer behavior

patterns, detecting fraud, and enhancing healthcare outcomes. Deep learning is a subset of

Machine Learning that entails the creation of artificial neural networks that can learn and

progress based on data. Deep Learning networks typically consist of multiple layers of

interconnected nodes, allowing them to learn more complex data patterns and relationships.

Why The Differences Matter

Artificial intelligence (AI), machine learning (ML), data mining (DM), and deep learning

(DL) all reflect different ways of processing and interpreting data and hence have fundamental

differences between them. Big Data, which requires complex processing and analysis methods to

extract useful insights, makes it more important to have a firm grasp on these differences.

Artificial intelligence and machine learning may be used to create predictive models that

can mine massive data sets for hidden patterns and trends upon which to base reliable forecasts.

By applying Data Mining, previously unseen connections and patterns in data may be uncovered.
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Large datasets may be processed using Deep Learning to reveal intricate patterns and

correlations that would otherwise be invisible to human analysts.

Several industries, from medicine and finance to sales and marketing, are beginning to

place a premium on the capacity to handle and understand Big Data. Organizations may better

analyze their data and derive useful insights by having a firm grasp of the distinctions among

artificial intelligence (AI), machine learning (ML), data mining (DM), and deep learning (DL).

This may result in smarter choices, more productivity, and higher returns.
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References

Bini, S. A. (2018). Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive

Computing: What Do These Terms Mean and How Will They Impact Health Care? The

Journal of Arthroplasty, 33(8), 2358–2361. https://doi.org/10.1016/j.arth.2018.02.067

Bustamante, C. D., De La Vega, F. M., & Burchard, E. G. (2011). Genomics for the

world. Nature, 475(7355), 163-165. https://www.nature.com/articles/475163a

Fawaz, M. (2021). Role of nurses in precision health. Nursing Outlook.

https://doi.org/10.1016/j.outlook.2021.01.016

Kroff, J. (2019). Modern robotics: designs, systems, and control. Willford Press.

Ginsburg, G. S., & Willard, H. F. (2009). Genomic and personalized medicine: foundations and

applications. Translational Research, 154(6), 277–287.

https://doi.org/10.1016/j.trsl.2009.09.005