Healthcare holds a significant role for every individual as it affects our opportunity to pursue life goals, reduces our pain and suffering, helps prevent premature loss of life, and provides information needed to plan for our lives. There is a distinguished difference regarding how healthcare worked from the past compared to today in the United States. Previously, healthcare was delivered by “physicians” who are simply vendors without any background knowledge regarding medicine. Since there weren't any innovative advancements, the healthcare provided before was not an ideal way to get treated as it was unsanitary and unreliable with charlatans operating the almshouses for the sick. Over the years, the United States healthcare system began to be continuously developed by scholarly researchers and innovators of their respective fields technologically and scientifically. Consequently, the current healthcare is more aseptic with its clinical procedures, organized work with trusted medical staff of their corresponding area of expertise, and are much more advanced with the implementation of artificial intelligence within the sectors of the healthcare industry.
Implementation of AI-based Augmented Care in Healthcare: A Scholarly Researched Argument Essay
Healthcare holds a significant role for every individual as “...it affects our opportunity to pursue life goals, reduces our pain and suffering, helps prevent premature loss of life, and provides information needed to plan for our lives” (American Medical Association). There is a distinguished difference regarding how healthcare worked from the past compared to today in the United States. Previously, healthcare was delivered by “physicians” who are simply vendors without any background knowledge regarding medicine. Since there weren't any innovative advancements, the healthcare provided before was not an ideal way to get treated as it was unsanitary and unreliable with charlatans operating the almshouses for the sick (ACHE 17). Over the years, the United States healthcare system began to be continuously developed by scholarly researchers and innovators of their respective fields technologically and scientifically. Consequently, the current healthcare is more aseptic with its clinical procedures, organized work with trusted medical staff of their corresponding area of expertise, and are much more advanced with the implementation of artificial intelligence within the sectors of the healthcare industry.
Artificial intelligence, otherwise known as AI, has been popularized for the past decades resulting in a surge in job prospects such as machine learning engineer, data scientist, software engineer, and many more (Gupta). Generally, when an individual thinks of AI, they refer to it as “the building of intelligent machines that can reason, learn, and even mimic some human behaviors.” (Kulz 6). This suggests the ability to turn artificial intelligence into humans as much as possible by thinking and incorporating everything that’s around it to be able to copy how humans act and behave. It’s a smart machine— a robot in which most say, but in a slightly more specific, a computer algorithm (Hulick 6). It’s a machinery that has been developed intricately with a multitude of programming languages embedded in its algorithm with the intention of being able to do the tasks of humans. This application progressed as more experts worked on evolving AI to make the future of the world easier and more efficient. First, it was implemented in computer work-based applications; however, AI can now be found in other fields such as the healthcare industry within its armamentarium and services.
There are many uses of AI in healthcare, but substantially “AI sorts through medical data and helps doctors make diagnoses” (Smibert 7). On a yearly basis, hospitals generate 50 petabytes of data (Greene) and to compare, “a petabyte could hold about 256 million photos” (Komprise). With AI applied in devices, it could organize each data file to categories and subcategories based on each patients’ financial outcomes, sickness, and others that could easily be found and examined by medical staff during diagnosis or during a “check out”. Having AI in armamentarium aids physicians to create diagnosis based on patients’ results during examinations, symptoms, and concerns spoken out by the patient, essentially creating a plan to approach the ailment by prescribing an accurate medication, supporting the patients to achieve a good result to their health. Therefore, artificial intelligence implemented in healthcare armamentarium “... has the potential to enhance the practice of physicians by facilitating improved efficiency and organization, thus improving patient care and outcomes” (Hirani et. al 1). The advancement helped reshape the flow of clinical practices, delivering accurate, timely care. However, the integration of AI in armamentarium is not a new concept as it has been a part of healthcare since the earliest implementation of medical technologies.
Admittedly, the introduction of AI in healthcare started to come around the early 1970s. An AI program called MYCIN, a “computer-based consultation system” (van Melle), introduced the broad term of artificial intelligence to the healthcare industry with a purpose to treat and identify blood infections. It was then that the development of AI in healthcare was researched by specialists from across the nation, eventually forming the American Association for Artificial Intelligence in 1979 (Xsolis). This advancement created a major shift in clinical practice as it gave a clear potential for the future of healthcare. The organizational society, American Association for Artificial Intelligence, researched ways in which to develop MYCIN to be more used in other parts of the healthcare industry as it showed a “clear potential to take some of the onus of clinical diagnosis from healthcare providers and provided a mechanism for physicians to cross-check their differential diagnoses.” (Hirani et. al 2). Between 1980s to 1990s, the systems contained improved data sets, producing “faster data collection and processing”, MYCIN’s work has expanded to assist in “more precise surgical procedures” rather than only identifying blood infections, added the ability to store medical records electronically using Electronic Health Record (EHR), and others that would further advance the hospital industry (Xsolis). These advancements led to a more streamlined healthcare structure within, enabling faster communication regarding patients’ health, greater care provided for patients and visitors alike, and the lessening of medical errors to almost none. The Electronic Health Record, however, improves the overall keeping of data with additional protection given AI’s ability to encrypt. Thus, these innovations will lead to the modernization of healthcare because it’s becoming data-driven. And so, foreshadows the coming evolution of AI to expand beyond its original design, applied to various sectors of healthcare.
MYCIN’s purpose to treat blood infections progressed to become more versatile and have other uses in healthcare. Xsolis, a technological company aimed to create AI and machine learning technologies for healthcare firms states:
“AI technology and machine learning have evolved to influence how healthcare is delivered profoundly. This advanced technology has evolved beyond biological sciences, where it began and now applies to medical specialties including radiology, screening, psychiatry, primary care, disease diagnosis, [and] telemedicine”.
Artificial intelligence is also implemented in the financial and mathematical sectors of healthcare. This indicates the benefits of having AI implemented in armamentarium giving an advantage to all sectors, medical wise, financially, and more. The complexity developed by researchers adds to the credibility of the advantages of having AI throughout healthcare. This application created opportunities to construct knowledge and ideas of how AI can be used and evolved. As of right now, AI has been worked on to the point that “... [the] inputs no longer needed to be symptoms and outputs could be more complex than purely clinical diagnosis” (Hirani et al. 2 & 3). Just as MYCIN, a new AI system named Pharmbot was implemented back in 2015 to aid patients to understand and learn how to do or take certain medications whether it is pills or fluids for their treatment (3). The ability to help patients and clinicians alike adds on to how artificial intelligence in armamentarium significantly supports the further development of it in healthcare.
Although the implementation of AI in healthcare exhibits the progressive technology that the nation is able to put forth, contradictory views of its implementation have transpired. The most known influence of AI-based augmented care was its capability to enhance the accuracy of patient diagnosis. Adam Bohr, Ph.D in pharmaceutical technology, noted, “AI-based algorithms can be implemented to assist and guide clinical experts in their decision-making process” (153). To prove this point, a study regarding “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network” (Rahman et al. 5), showed an outcome stating that AI is more accurate than physicians in diagnosing with “AI involved in the diagnosis of arrhythmias had an average F1 score of 0.84 compared to the average cardiologist score of 0.78” (3). With AI-based augmentation assisting providers, enables a more efficient, precise identification of any medical problems which significantly help patients with their complications by interfering with its progression, or overall cease it. This results in a positive reaction from patients, building that credibility of having AI in healthcare as well as the reputation of the facility. To give another example from another sector of medicine, An AI model showcased a 93.8% diagnostic accuracy used in diagnosing a development of tumor from a gene mutation known as V600E (3). With an approximate 6.2% error, this suggests that the artificial intelligence used during these diagnostics is well grounded, backed up with technical work that experts have compiled to make the accuracy well above 50%. With this advancement embedded in the equipment allows a domino effect to partake with high accuracy diagnosis resulting in efficient treatments, faster workflow, and more, beneficial for patients’ health and clinicians’ work.
Correspondingly, emerging challenges arose in modern day healthcare as it becomes difficult to “[...] choose the right drug combination for the right patient as the drugs become more specific to molecular targets” (Horgan et al. 147). AI-based care allows personalized treatments for patients, crediting that “Personalising care means more effective care, less waste of time and resources, greater patient satisfaction” (146). For example, a patient of Parkinson’s Disease which “is one of the most frequent neurodegenerative disorders that presents various life-altering symptoms, such as upper-limb trembling” (Wu et al. 2) is able to maintain “[...] independent motor function and walking capability” (8) by muscle strengthening, shortening of flexor muscles, and improving a balanced posture through personalized therapy. As such, tailored care for individuals results in a higher success rate of fighting the complications enabling a faster hospitalization; therefore, satisfying patients’ desire for convalescence and allowing a greater space to admit more patients in hospitals. Using ML-based (machine learning) algorithms, hospitals can configure a custom treatment based on patients’ complications along with their specific genotype. In some instances, patients are unable to stay in hospitals for long periods of time. Having personalized care with AI allows them to stay at their homes with simple equipment embedded with artificial intelligence guiding them to their needs. AI can aid physicians to “[..] tailor treatment plans to individual patients by analyzing patient data, medical records, and other relevant information” (Stafie et al. 22) in which could be taken home. The ability to make specific diagnosis and predictions which leads to next-steps treatment plans facilitates each needs of the patient, creating a greater shift for their well-being, proving that AI-based augmented care’s outcome of specialized care and treatment the demands of evolving healthcare and the necessity of individuals.
Given that precise diagnosis and custom treatment plans are some of the advantages of implementing AI-based augmented care into the healthcare system, it also leads to a lucrative revenue stream for the health industry. A book titled Economic and Social Development: Book of Proceedings highlights the task of artificial intelligence in the economic sector of healthcare. How it enhances the profit, productivity, and management of those. Biljana Markovic, economics professor at University of North Croatia and Ante Roncevic, the head of the economics department stated that integration of artificial intelligence into healthcare systems allows an “[...] optimizing revenue and reducing costs to enhancing resource utilization and financial solvency, AI offers promising avenues for driving sustainable healthcare systems” (176). Artificial intelligence analyzes patients’ data in a fast manner, creating a quick, yet precise diagnosis. This leads to a lower expenditure having been able to intervene with the progression of their medical condition swiftly. The lower cost significantly helps the patients in the United States of the 21st century because of the growing inflation and mixed healthcare system of the US (meaning that it is not a part of the universal healthcare system, rather having both insurance and personal funds to apply when paying). The algorithm can notice patterns in imported medical goods and equipment to recognize a strong, reliable candidate for supplying, reducing unnecessary costs from purchasing in insubstantial suppliers. When AI is embedded in the structure of healthcare, it can also track data and make predictions of costs in order to have a positive reimbursement; therefore, able to utilize financial solvency. Using a predictive analytics to project what AI could offer in the future, “We find that AI adoption within the next five years using today’s technologies could result in savings of 5 to 10 percent of health care spending, or $200 billion to $360 billion annually in 2019 dollars” (Sahni et al. 50). This states that AI implementation will significantly help in the long run regarding financial stability. It creates lower costs for patients since they are able to get a quick diagnosis, enabling them to not need to stay in hospitals for an unnecessarily long time. This shift causes a great increase in access to care for others given that the expenditures will be much lower. Furthermore, hospitals are able to decrease the unnecessary spending that goes towards administrative work such as data encryption, managing finances, staff, and more. These effects result in increasing revenue for hospitals.
The advantages of AI-based augmented care focuses on the technological development and how that positively affects the healthcare system; however, it is also crucial to inspect its effects on the perspective of those receiving the development, patients and clinicians alike. Highlighting the scientific breakthrough to emphasize its advantages undermines negative outcomes in reality with the well-being of others at stake, physically and mentally. Even though there are developing applications of AI used in sectors of healthcare, the ethical and regulatory concerns challenges the implementation with the critics emphasizing that AI-based augmented care should not be implemented in armamentarium because it violates patients’ data privacy, can cause misinterpreted diagnosis with its ML and rule-based algorithms, and it also creates the lack of trust crucial between the patient and their provider.
Concerns arose from the moment that implementation of AI in healthcare was becoming normalized, stating that these algorithms infringed the hospitals’ data privacy regulations although a public law known as the Health Insurance Portability and Accountability Act established on August 21, 1996 to ensure the protection of patients' data information. Concerns arose from the moment that implementation of AI in healthcare was becoming normalized, stating that these algorithms infringed the hospitals’ data privacy regulations. A collection of data from the National Library of Medicine, the world’s largest biomedical research and library for health professionals, showed the occurrence of data breaches from 2015 to 2019 in various industries. In their data was the medical sector which exhibited 1587 breaches, otherwise 76.59% compared to other sectors such as the businesses-financial which had 194 breaches meaning 9.36%. The vast difference in the statistics will continue to increase as industries rely on technology. Furthermore, the analysis placed the healthcare industry in a vulnerable position, with the hackers accessing the data unauthorizedly and the escalating concerns coming from the patients. Not only will it tarnish the reputation they’ve developed, hospitals will also face significant costs when a data breach occurs. This cost overall includes the compensation, to fix the systems, and reinforce protections. On average, the cost can reach up to 6.45 million in the United States (Seh et. al). On the other hand, the law “...requires appropriate safeguards to protect the privacy of protected health information, sets limits and conditions on the uses”
(HHS). However, the appropriate safeguards do not ensure the protection of the data. Since there are an increasing number of learners in the field of cybersecurity that can be studied at college or online through search engines, there is no guarantee that all of the students will follow through with ethical hacking. Knowing how an algorithm operates allows someone to find a way to break into the systems without being noticed.
That leads to the second argument which was the inability to detect data breaches due to the advancements of AI learning and studies. With AI’s job to gather data and store all information within its rule-based and ML-based algorithms, results in the high possibility of data theft and seizing over all platforms (Arunkumar and Theral 339). Just as mentioned previously with the amount of data stolen illegally within the healthcare sector with over 50% breaches, majority of this number was due to the complex cyber attacks, making it a challenge for the establishments to detect them. Some of the most used attacks that create a significant effect to the hospitals and to the patients are called zero-day vulnerabilities in which “ exploited by hackers before the software or system's developers have had any opportunity ("zero days") to create and release a fix or patch.” (Peremore). Using this tactic allows hackers to divert the developer’s attention and quickly steal sensitive information in bulk from patients. Since it was newly developed, its unknown software capabilities make it hard to detect. Another example would be supply chains which infiltrate 3rd-party suppliers, disrupting essential services or equipment needed for patient care (Hargreaves). This attack has a major effect on the procedures happening within healthcare as well as data stored. Disrupting the supplies causes delayed procedures resulting in worsening of medical outcomes. All cyber-attacks, however, receive harsh consequences from laws established. Under Section 1177 of Health Insurance Portability and
Accountability Act overview the possible consequences of having health data stolen or released accidentally or intentionally, stating that:
[...] obtains individually identifiable health information relating to an individual; or ‘‘(3) discloses individually identifiable health information to another person, shall be punished [...] be fined not more than $50,000, imprisoned not more than 1 year, or both; ‘‘(2) if the offense is committed under false pretenses, be fined not more than $100,000, imprisoned not more than 5 years, or both; and ‘‘(3) if the offense is committed with intent to sell, transfer, or use individually identifiable health information for commercial advantage, personal gain, or malicious harm, be fined not more than $250,000, imprisoned not more than 10 years, or both. (95)
Although there are consequences for breaches, it would be completely futile to have as more data breaches become undetectable. However, presenting this law to the patients in order to create a safe environment and trust, does not speak of how hospitals will use all of a patient's data to create a diagnosis through AI-based augmented care, rather only showcased in terms of the use of data illegally by hackers.
The lack of transparency towards patients' data usage creates problems in implementation of AI. It raises ethical and legal regulatory challenges concerning the patient's privacy and compliance as health institutes do not give the direct information about the data usage, also allows questioning of who owns the data and the applicable access to their information. Although it is a problem that ensued, it shouldn’t have happen under Section 264 of Health Insurance and Portability Act:
(1) The rights that an individual who is a subject of individually identifiable health information should have. (2) The procedures that should be established for the exercise of such rights. (3) The uses and disclosures of such information that should be authorized or required. (99)
But since the use of AI-based augmented care is generalized throughout, it is confounding to tell how it should be addressed to the patient since their information is crucial to make diagnosis. This becoming more of a well known problem stemming from the increasing amount of data breaches, a survey result concluded that approximately 80% of patients want to have the option of sharing their health data whether all or some of it (American Medical Association). This result displays the rising concerns of patients, pushing hospitals further into vulnerability. To make a decision whether to please the patients and give them the choice or use of their data. The implementation of AI-based augmented care does not condemn the opinions of those affected; therefore, it should not be continued.
Machine learning based algorithm, otherwise known as ML-based algorithm incorporates current knowledge of a situation and uses probability theory and statistics to make a diagnosis (Mennella 3). This type of algorithm is highly popular and embedded in the majority of AI applications such as healthcare and other industries. Another one of a prevalent algorithm is called a rule-based algorithm which essentially “...start[s] with a collection of facts called a knowledge base... use[s] logical rules to make inferences using those facts.” (Hulick 18 & 19). Despite the advance work that it is able to do, these algorithms can still create misinterpretation in diagnosis of complex medical conditions with little-known information about it, not applicable to create effective treatments as said by the proponents of the implementation. Having minimal freedom to use critical thinking like physicians to create a solution to intervene in a medical condition. A lot are concerned about the privacy of their medical record with their personal information, billing, and others now that AI is fixed within computers to store information. This installation provokes the possibilities of identity theft, easily having to steal data if compiled together and the ethical regulations that have been established as a public law.
Making a diagnosis means that an algorithm must use data sets in order to have an accurate outcome; however, critics argue that each individual has a varying genetic heterogeneity from each other. By definition, it’s the different gene mutations from various people causing the same medical condition (National Cancer Institute). The difference in symptoms, complications, and needs varies from each and every patient causing a misinterpreted diagnosis not suitable for that patient; therefore, unable to satisfy their needs and the betterment of their health effectively. Mennella et al. published multitudes of research papers regarding artificial intelligence under the Institute of High-Performance Computing and Networking (ICAR) and a study from their highly credited institute “underscored inaccuracies in cohort identification when utilizing vocabulary mappings within a common data model during the data process [107]. These mappings, integral to data, may suffer from inaccuracies stemming from programming bugs and errors that escape detection during quality assurance stages” (Mennella et al. 16). This study concludes that AI is not developed thoroughly and completely as it still stems from program bugs and errors, causing inaccurate diagnosis. According to the book Can We Trust AI?, a limitation within the algorithm also causes errors within diagnosis like following a set of rules- defining rule-based algorithms. People have different genetic heterogeneity from each and everyone. If artificial intelligence does not have a certain percentage of people’s common genotype dataset embedded within its coding, the effects of diagnosis is inaccurate and ineffective as it does not fit seemingly with their unique code for their group (Chellappa 35) Misinterpreted diagnosis from unique genetic information that one could not control is not something that should raise concern, but it still does.
AI-based augmented care is not developmentally appropriate in medicine as the chances of inaccurate diagnosis are apparent.
Since an algorithm compiles all of the knowledge acquired from a patients’ data and applies a strict rule-following set to make a conclusion (Hulick 18 & 19), it causes the inability to adapt in changing symptoms or characteristics of diseases and other illnesses. Medical conditions evolve over time, frequently discovering new symptoms and changes either for the better or worse due to the habits that a patient has done consistently. As stated by Bennett Lorber, American medical educator and a professor emeritus, “Implicated causes for these changes include alterations in lifestyle such as sexual behaviour, leisure activity, and dietary trends, along with the impact of immigration and the effects of medical progress” (Lorber). Having illnesses change and develop due to an unrecognizable doings is something that would create a misinterpreted diagnosis as it will not be consistent for the artificial intelligence to be able to maintain its “accurate” diagnosis. Furthermore, using rule-based and ML-based algorithms does not guarantee the reliability of AI-based augmented care given the changing environment of the medical sector.
Misinterpreted diagnosis is also caused by the racially biased data sets embedded in algorithms. In order for an algorithm to create an output, it must have related data from others to create a collection to use for processing. However, a compiled study by extinguished researchers of Pew Research Center showed that people of color raised questions regarding racial bias embedded in the AI’s algorithm with statistics showing that approximately 4 in 10 Hispanics (42%) and English-speaking Asian adults (39%) believed that patient’s race and ethnicity is a major problem in health and medicine (Tyson et al. 11). Given that the study counted Hispanics and Asians majorly, many have wondered about the percentage including other people of color.
A psychiatrist from Chennai, India answered in an interview by M Thenral and Annamalai Arunkumar for the academic journal, “Challenges of Building, Deploying, and Using AI-Enabled Telepsychiatry Platforms for Clinical Practice Among Urban Indians: A Qualitative Study” the lack of minority data within AI algorithms that would satisfy a minority groups’ needs:
The data related to mental health is scarce in India...in our country where diverse minority groups constitute the majority of the population, we need specific data on such population to account for various sociodemographic differences...or else, we will end up trying to build a model with one size fits all approach” (340)
Not only is the racial bias algorithms problem prominent in the United States healthcare, it can also be found in other parts of the world. This indicates that an issue such as this creates the hesitancy of patients in an AI-based augmented care as it produces the possibilities of having their health information stolen or released illegally and create the incorrect diagnosis due to the racial bias within algorithm codes.
The developing hesitancy creates the lack of trust between the patient and the provider as the reliance on the AI algorithm overrides human interaction. When the transition to AI-based augmented care becomes much more apparent, the interactions during diagnosis will mostly be with an algorithm, relying on its “accurate” work and expeditious process. Although it may smoothen the healthcare system, a data gathered from the respondents compiled by Pew Research Center concluded that 57% of adults across the nation believed that the relationship between patients and providers will worsen due to the fact that the healthcare industry is transitioning to AI-based augmented care (Tyson et al. 10). This highlights the perspective of those receiving the care, noticing the dependence towards AI’s outputs. It also concludes that the dependency makes someone use the slightest critical thinking and thorough judgement regarding a diagnosis, accepting AI’s output as “right” and “correct” since it is artificial intelligence embedded with compilation of rules and complexity that could “solve” anything complex within the medical field. This mentality roots into the idea of “AI [ability to] identify patterns and anomalies in patient data that might be challenging for human professionals to detect. AI enables the tailoring of treatment plans to individual patients” (Mennella et al. 5). Since AI is implemented in healthcare, there are assumptions in which it is “better than real people” or “it’s more accurate”. However, the relationship between a patient and provider is significantly crucial as they are the ones who can understand the patients’ needs that artificial intelligence lacks as it is an algorithm, made by a set of rules to directly follow.
Artificial intelligence, becoming much stronger, undermines the provider’s validity of expertise. Students spend an average of 11-13 years in school to become physicians (Kenny), excelling in different branches of science and math in order to help others in their field. But introducing AI may decrease the job prospects in healthcare, starting from small like medical coders, to technicians in the future. The exponential progression created a feel of uncertainty from others with a research report concluding that 75% of US adults felt that healthcare progresses potentially with the usage of AI-based care without fully comprehending the risks that comes with it (Tyson et al. 6). More focused on the idea of being the most technologically advanced care and country, undermines human intelligence because the reliance towards AI-based augmentation becomes evident in some sectors of healthcare such as radiology and financial services.
Embedded in medical devices, it is an important piece to connect with the patients. Despite having experienced specialists develop these algorithms to ensure a professional transaction between patients and AI just as one would carry a meeting with a physician, some felt unsure to tell everything to an Al-based armamentarium concerned with the possibility of confidential information getting revealed, connecting back to the issues of data privacy. A patient elucidated their thoughts whilst encountered with an Al-based care during a medical check up, “[...] I hesitate to tell everything as I would otherwise tell my doctor in person...” (Arunkumar and Thenral 339). AI in armamentarium is simply an algorithm acting and behaving the way it is supposed to be, coded that way to do its task, “AI systems have no goodwill towards us, nor any motivation to act in our interests” (Hatherley 480). Compared to human physicians, they show emotions and a built-in trusted nature due to their long educational background specifically of their specialty which creates a comforting, safe space to tell everything and “[...] have the confident expectation that, when the need arises, the one trusted will be directly and favourably moved by the thought that you are counting on her” (480). Artificial intelligence does not have that authentic characteristic that takes in patients comfortably, rather a robotic aspect that is seemingly cold and comfortless. Furthermore, a research report conducted by several researchers from Pew Research Center concluded that 75% of US adults felt that healthcare progresses exponentially with the usage of AI-based care without fully comprehending the risks that comes with it (Tyson et al. 6). This demonstrates the lack of trust between patients and AI is visible despite the benefits that come with it in terms of the technological aspects. The perspective of those provided is just as important to address since they are the ones needing the help.
AI-based augmented care applications are rapidly growing, especially in the health and medical industry. Even though the industry is becoming technologically advanced, “[The] lack of empirical data validating the effectiveness of AI...” (Khan et. al) as well as other social and ethical concerns; therefore, AI-based augmented care should not be implemented in armamentarium because it violates patients’ data privacy, can cause misinterpreted diagnosis with its ML and rule-based algorithms, and it also creates the lack of trust crucial between the patient and their provider. Proposing a plan for the future of AI in healthcare, the systems must receive regulatory approval, attain standardized protocols to guarantee its functionally, have medical staff trained and know the use within the algorithms, and undergo iterative supervision for data breaches and programming bugs and updates (Mennella et. al 17). A way to make this proposal work is to have a trial and error process with artificial intelligence and ensure that the complexity within the encoded program secures the data and creates flexible diagnosis according to each and every patients’ needs. Furthermore, to make certain that the healthcare industry is not heavily reliant with AI armamentarium, “The future governance of AI must prioritize human expertise and experience in overseeing these technologies, ensuring that the ultimate decisions, even when contrary to AI recommendations, remain in the hands of healthcare professionals.” (17).
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