Artificial Intelligence in Healthcare

Subject: Tech & Engineering
Pages: 7
Words: 1394
Reading time:
7 min
Study level: Master

Artificial Intelligence (AI) is believed to be a subfield of computer science devoted to constructing intellectual machines capable of simulating human mental processes, is a rapidly expanding subject of study. Two limited instances of artificial intelligence are speech recognition and facial recognition. In other words, the use of computers to replicate human abilities is characterized as follows: the gadgets are capable of detecting detectable signs of human intellect (Liu, 2020). Both machine and deep learning algorithms have significantly enhanced performance in a variety of domains, including image cataloging, text examination, speech, and also facial recognition, as well as broad range of potential programs, including independent vehicles, regular language processing, and medication (Liu, 2020). AI is being used in a wide variety of applications worldwide, comprising proficient systems, regular language dispensation, speech recognition, as well as machine vision.

Artificial Intelligence (AI) will play a rising role in healthcare as computer power, machine-learning techniques, and huge datasets derived from patient medical records and other equipment such as wearable health monitors continue to improve. This is possible due to the extensive obtainability of graphics processor components, and this accelerates parallel dispensation, and the apparently infinite computer resources obtainable on-demand in the cloud (Liu, 2020). It is feasible to store massive amounts of data on the cloud indefinitely. Analyzing training data and using machine-learning algorithms to diagnose and treat patients can provide new insight into these subjects. Health care data has resulted in the expansion of artificial intelligence technologies that guarantees the increase of the effectiveness and value of medical services through analysis of data (Liu, 2020). Electronic medical records (EMRs) as well as wearable monitors in the healthcare facilities can exploit big data in healthcare.

Patients will receive better care, and radiologists will have better tools for diagnosis and treatment AI is used in an era of big data. AI can handle these things to help reduce human error, making it a valuable tool in the fight against human error. According to some estimates, artificial intelligence (AI) can improve patient outcomes by 20%-30% while lowering treatment costs by up to 45% (Liu, 2020). Despite the growing benefits of artificial intelligence, there is growing fear of healthcare professionals adopting these changes. Most of them view artificial Intelligence as hectic to adopt in the health care industry. For instance, there is an argument that using artificial intelligence in health care will likely eliminate more jobs than it will create.

Additional concerns include the possibility of difficulty in training machines that have artificial intelligence (AI) built into them. Data sets that have been carefully curated are necessary for AI technology to work as intended. It may be difficult to gather some of the data AI learning requires due to privacy concerns. According to some, the adoption of artificial technology is challenging, just like any other industry-wide shift. No matter what industry you’re in or how big your company is, it’s challenging to make a change. The medical community requires proof that AI works and a plan to convince investors that AI is worth the investment. Working with AI requires a thorough understanding of how technology can improve a person’s daily routine.

How Artificial Intelligence is Used to Make Diagnosis

With the advancements in technology, artificial intelligence can be used to diagnose. The diagnostic function of Artificial intelligence is more common in radiology and pathology. It is becoming increasingly important in radiology and pathology to apply AI and machine learning to examine massive datasets and derive actionable insights. Because MRI machines, X-rays, as well as CT scanners, and include vast quantities of multifaceted information, it would be challenging and also consume too much time for physicians to examine these images. Soon, radiology practice will be profoundly impacted by AI/DL technologies in routine clinical imaging. Radiologists can benefit from the use of artificial intelligence (AI).

Analysis and selection of medical imaging features, as well as generation of novel feature extractions, may be assisted by DL algorithm implementation (Hosny et al., 2018). The use of DL algorithms in image interpretation can aid radiologists in identifying and classifying disease patterns in images, which can then be used to suggest treatment options to patients in conjunction with other physicians involved in their care. The incorporation of artificial intelligence can also be helpful in pathology (Paul et al., 2021). Clinicians might be better equipped to make treatment choices that are not solely based on the characteristics of a tiny part of cancer by succeeding in this quest with artificial Intelligence. Virtual biopsies and radionics, a new field that uses image-based algorithms to characterize tumors’ phenotypes and genetic properties, are being aided by artificial Intelligence (Elemento et al., 2020). Providers might also be able to classify cancers’ aggressiveness to better target treatment accurately.

Data Ethics Issues

Data may be utilized to make decisions and have a large influence. However, this valuable resource is not without its drawbacks. Protecting clients’ autonomy through sufficient permission, maintaining equity, as well as honoring participants’ confidentiality are all essential ethical considerations (Gerke Minssen and Cohen, 2020). In some cases, artificial Intelligence has been demonstrated to interfere with data confidentiality. Privacy is a concern not only in the field of Artificial intelligence but in any data-related field in general. The conversation will center on the importance of controlling one’s data and making decisions based on that data. The protection of their personal information has always been the number one concern of our clients. On the other hand, some businesses are utilizing Big Data Analytics to carry out operations that may compromise the privacy of their consumers’ information (Gerke Minssen and Cohen, 2020). Clients may find themselves in an unpleasant situation, even in front of their family and friends, due to this breach of personal information. When it comes to artificial intelligence, data privacy is not guaranteed. Some companies promote artificial Intelligence to patients using patient testimonials.

In AI, data privacy is at risk due to vulnerability to adversarial attacks. A damaging attack on an AI system can render it useless. Image recognition systems, for example, are subject to attacks. A precisely positioned pixel can fundamentally alter an image, causing an AI system to misperceive it, even if it has been taught on thousands of photos (Gerke, Minssen, and Cohen, 2020). This could have significant implications for real-world applications involving person identification. Privacy can only be ensured by educating patients about using their personal information and fostering an open dialogue to build trust. Preventing patients from suffering negative repercussions on their health insurance premiums, employment opportunities, or even personal relationships is more vital than simply making sure the correct data is collected. Artificial Intelligence (AI) health apps provide additional challenges; including sharing patient data with family members and friends requiring anti-discrimination laws similar to those currently in place for genetic privacy.

Theoretical and Legal Principles Related to the Data Issues Identified

Ethical concerns arise when businesses begin monetizing their data for purposes other than those for which it was originally obtained using big data analytics, particularly Artificial Intelligence in health care. The scale and ease with which today’s analytics can be carried out fundamentally alter their ethical context. The legal and theoretical principles associated with patient data include data protection, patient information as confidential, maintaining patient data privacy, not interfering with human will, and not unfair institutional biases. A person’s data should not be shared with third parties without their consent, including other businesses or individuals who may use the information for their purposes. However, privacy does not imply complete secrecy, as private data may be subject to legal audits.

It is important to keep confidential information that has been shared with others private. Third-party firms must specify boundaries on how and when sensitive data, such as medical, financial, or geographic information, can be shared. As long as users can see and manage how their data is being utilized and sold, companies can preserve the confidentiality of their customers in their services (Gerke, Minssen, and Cohen, 2020). It is possible to monitor and even determine our own company’s actions using big data analytics in artificial Intelligence. Companies must carefully consider predictions and inferences about the future. Discrimination based on race or gender should not be encouraged through the use of artificial intelligence or big data (Gerke, Minssen, and Cohen, 2020). Machine learning algorithms can absorb and amplify a population’s unconscious biases using training data.

Report Preparation

As part of the report preparation process, I started by deciding on the scope of references. My initial step was to determine the need for and purpose of the document. It usually is crucial to provide these terms of concern since it allows the reader to judge the relevancy of the information before reading the complete page in full. After that, I did extensive research on the report’s subject, and then I created the outlines, which included the names and subtitles for each chapter. Afterward, I wrote the first draft to ensure that all of the material was correct. My final draft of the report was completed later on in my life.

During drafting the report, I discovered that I had reviewed all of the required questions to guarantee that I did not write any incorrect data. The section of the study dealt with data ethics issues that stuck out to me as particularly difficult to read. When I looked into how data privacy is compromised in artificial Intelligence, I could not locate adequate information. Initially, the information was disorganized, but I was able to compile it into a comprehensive paper on data ethics challenges in artificial intelligence later on.

Recommended Code of Conduct

Adherence to the specified ethical principles is the proposed rule of behavior for health care professionals who utilize artificial Intelligence. Health care professionals must guarantee that patients’ privacy is protected and that informed consent is obtained. Additionally, they should ensure the justice and impartiality of the algorithm. Real-world examples demonstrate how algorithms can be biased in ways that result in the race, ethnic origin, and gender discrimination (Liu, 2020). Additionally, biases can arise based on other factors, such as age or a physical or mental handicap. Healthcare providers must go beyond legal requirements to take full advantage of cutting-edge technology and incorporate artificial Intelligence. Medical records can be processed using natural language processing (NLP) technologies to read and categorize the information. Patients’ unstructured notes can be analyzed by NLP systems, providing a wealth of information about quality, technique improvement, and patient outcomes.

Recommendations for Industry Best Practice

Individuals in the industry must cultivate an interest in utilizing new technology to succeed. When recent changes are introduced, they must react quickly to them. Furthermore, individuals in the industry should be willing to learn about new technological developments to use the latest technology in question. Over time, the incorporation of technology in the medical field has resulted in improved patient diagnosis as well as treatment. Healthcare is undoubtedly the most significant of all the areas that have benefited from technological implementation (Gerke Minssen and Cohen, 2020). As a result, the quality of life has enhanced throughout time, and so many lives have been preserved.

Due to the above developments, the government should establish a long-term working relationship with the technology provider when introducing a new product or service. As a result, the ability to communicate effectively, collaborate effectively with one’s team and ally that benefits both the company and the government is critical (Hosny et al., 2018). In addition, the government should encourage the funding of artificial intelligence research and development. For analysis to be implemented, a significant amount of capital is required. Governments should also establish training centers where artificial Intelligence can be taught to people to increase their overall knowledge.

Reference List

Elemento, O., Leslie, C., Lundin, J., and Taurasi, G. (2021) ‘Artificial intelligence in cancer research, diagnosis and therapy’, Nature Reviews Cancer, 21(12), pp.747-752.

Gerke, S., Minssen, T. and Cohen, G. (2020) ‘Ethical and legal challenges of artificial intelligence-driven healthcare’, In Artificial Intelligence in Healthcare, pp. 295-336.

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H. and Aerts, H.J. (2018) ‘Artificial intelligence in radiology’, Nature Reviews Cancer, 18(8), pp.500-510.

Liu, J. (2020) ‘Artificial intelligence and data analytics applications in healthcare general review and case studies’, In Proceedings of the 2020 Conference on Artificial Intelligence and Healthcare (pp. 49-53).

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K. and Tekade, R.K. (2021) ‘Artificial intelligence in drug discovery and development’, Drug Discovery Today, 26(1), p.80.