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Detecting and Collecting: The Ethics of Artificial Intelligence and Wearable Technology for Preventive Heart Health

Updated: Aug 8

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According to the American Heart Association, 121.5 million people in the U.S. suffer from heart disease. To address this and prevent heart disease, leveraging the power of artificial intelligence (AI) and rising technologies can help us to imagine a pathway for the future of the US healthcare system.  If this technology is being increasingly integrated into our daily lives, why shouldn’t healthcare be included? In this paper, I examine AI working with wearables as being integrated in tandem with the user’s healthcare provider and acting as a helping hand. Wearable technology, as I will reference them in my paper, or Digital Health Trackers (DHT) working with AI detection, offer a promising outlook on the future of patient cardiovascular care by looking at the patient’s lifestyle continuously and as a whole.


However, though this picture seems bright, the drawbacks of this path must be acknowledged as well. I will be exploring the ethical question: as AI and wearables are rising in healthcare, does this increase in preventative care come at the cost of lost privacy and accountability? I will be examining this by explaining the background of these rising devices and algorithms and how they will be applied to promote preventative heart disease health. I will then follow into my ethical analysis of the benefits and costs through a consequentialist framework, looking at the consequences and results of AI and wearable integration. My ethical benefit analysis will be based on the values of accessibility and autonomy, and the ethical costs based on privacy and accountability. 


Table of Contents

  • Abstract

  • Introduction

  • Background of Cardiovascular Health and Wearable Technology

  • Background on Integration of AI

  • Stakeholders

  • Ethical Benefits

    • Accessibility: Improving Care for Vulnerable Populations

    • Autonomy: Patient Empowerment and Advocacy

  • Ethical Costs

    • Privacy: Who Has Access To Your Data? 

    • Accountability: Who Should Be Held Accountable- the Doctor or the AI?

  • Conclusion


Introduction

“You think you are invincible.” These words ring true for almost everybody- until they find themselves confined in a hospital bed, abruptly facing their new reality. Scott M. believed he was invincible: working fifty hours a week, having late-night dinners, traveling constantly, and ignoring his exercise. On the week of his 37th wedding anniversary, his unhealthy lifestyle caught up to him. He was rushed to the ER, diagnosed with heart disease, and then had a pacemaker implanted into his heart. Many Americans have a future that resembles Scott’s. Their unhealthy lifestyle and ignoring their well-being accumulate over time into some version of heart disease or issues. Unfortunately, many Americans are unaware of how their everyday decisions play a role in the big picture of their health, leading them to ignore that bike ride they planned, grab a quick meal at McDonald’s, and spend a few extra minutes online before they go to sleep. These little choices repeat and turn into habits. Once these decisions amass into a heart disease symptom, it will likely be too late. 


Often referenced as one of the four horsemen of chronic disease, cardiovascular diseases are widely known to be the deadliest. Heart disease is the leading cause of death in America, responsible for 1 in 5 deaths. Moreover, the United States spends 216 billion dollars on heart disease per year- encompassing treatments, resource allocation, and labor loss. However, it is imperative to understand that 80% of these deaths are preventable (“Heart Disease Facts”). Currently, the U.S. healthcare system is built on interventional medicine that pinpoints the disease once it has already manifested and progressed in a patient. This brings the foundation of the US healthcare system into question and prompts the discussion of what the future of healthcare should look like. 


The rise of AI with wearable technology offers a solution to being proactive with one’s health and well-being, shining a light that may help prevent heart disease in the U.S. However, there is a prevailing discussion on how this AI integration with wearable technology will affect healthcare overall. In this paper,  I will be exploring the ethical question: as AI and wearables are rising in healthcare, does this increase in preventative care come at the cost of lost privacy and accountability? I will be focusing on the ethical benefits and costs of integrating this wearable technology and AI for preventative healthcare, using the values of accessibility, autonomy, privacy, and accountability 


Background of Cardiovascular Health and Wearable Technology

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Many individuals have experienced the scare of a heart attack through a family member, a friend, or even by experiencing it themselves. Often patients become aware of their cardiac issues once an extreme medical event has already occurred. Therefore, it is crucial to acknowledge and understand that cardiovascular health is an umbrella term. Under this umbrella is heart disease, which most notably affects your heart and blood vessels. Preventative heart health is focused on lowering a patient’s risk for heart disease, thus the risk of having a heart attack or fatal event.  


Many patients believe the notion that their probability of a heart issue is pre-determined, so no change to their health can delay the inevitable. However, although it is important to be aware of one’s family history, it is also crucial to be cognizant of one’s lifestyle choices as well. According to UT Southwestern Medical Center, there are two types of risk factors for heart disease: controllable and uncontrollable. Uncontrollable risk factors include gender (men are at higher risk for cardiovascular disease than women), age, family history, and possible environmental factors. On the other hand, controllable risk factors include high blood pressure, obesity, smoking, an unhealthy diet, and a sedentary lifestyle. These factors potentially could give way to various heart issues: sudden heart attacks, heart disease, coronary artery disease (CAD), or arrhythmias. This highlights the importance of lifestyle factors and the decisions we make on a daily basis that can be tracked for heart health. 

As mentioned previously, the World Heart Federation has estimated that 80% of heart-related deaths are preventable. However, preventative screenings and primary care physician visits dwindle as many reach their 30s-50s. Additionally, America’s healthcare system overworks its doctors, with 81% feeling overworked and burnt out due to the high volume of patients and shortage of providers (“Physician Compensation, Overwork, and Shortage” ). All around, this leads to a gap in the doctor-patient relationship. As doctors cannot allocate a vast amount of time to each appointment, they are often unable to build a relationship where they can truly understand the patient’s lifestyle and connect it to their health. Healthcare agencies that are able to devote more time to patients are often expensive and inaccessible to a majority of the population. Ultimately, through lack of guidance, Americans are unaware of the impact of their everyday lifestyle decisions, thus allowing the effects of their choices to accumulate into a fatal event. 


The shift in realizing how one’s lifestyle decisions connect with their health can be achieved partly through the use of wearable technologies. Wearable technologies, such as smartwatches or activity trackers, are devices worn on the body to monitor for health or well-being purposes. After COVID-19, the global market saw a shift in attention to lifestyle habits through tracking; most notably, wrist wearable devices grew from 66.5 million units in 2019 to 105.3 million in 2023 (Beckman). By tracking sleep, heart rate, oxygen levels, and other metrics, many users were able to view the impact of their decisions on their health. The ability of wearable technologies to track these metrics and notify the user of their potential health risks makes a strong argument for a preventative care approach by the monitoring of continuous data. This would tie health metrics to lifestyle decisions (such as hours of sleep or physical activity) and will give greater insight into the status of one’s health.  It will also allow patients to assume an active role in their physical well-being. 


In this paper, the wearable devices that I reference are consumer-grade devices, not to be confused with medical-grade devices. According to the Life Science Academy, a consumer-grade is available on the market as advertised for the majority of the population and can have the intended uses for well-being and fitness. On the other hand, medical-grade devices are often bought through the recommendation or prescription of a healthcare provider. These devices tend to be specifically for patients who have been diagnosed with a disease or who have already been evaluated to be at high risk for one. The specific consumer-grade wearable devices I will be referring to are the Apple Watch, Fitbit, and the Oura Ring. 


Firstly, the Apple Watch is a leading product in wearable technology and has announced it is developing an emphasis on health, “[o]ur goal is to empower people to take charge of their own health journey. With these innovative new features, we’re expanding the comprehensive range of health and wellness tools that we offer our users,” said Sumbul Desai, M.D., Apple’s vice president of Apple Health. The Apple Watch Series has an expansive list of capabilities for tracking important and comprehensive health metrics. Particularly, the watch measures heart rate, monitoring and checking unusually high or low heart rates. For instance, according to Apple, if a patient’s heart rate is above 120 bpm (beats per minute) or below 40 bpm while they appear to have been inactive for 10 minutes, the user will receive a notification. Users can also adjust the bpm thresholds to fit what is regular or irregular for them, as well as turn notifications off. Additionally, the watch tracks irregular rhythms that may suggest atrial fibrillation (AFib) through an ECG (electrocardiogram). The watch also prompts the user to enter any symptoms that they may be experiencing, such as fatigue or a heavy chest. Finally, the watch can also track exercise, blood oxygen, and sleep duration- contributing to the user having a holistic overview of their health. All of this information is recorded on the Apple Watch, which is connected to the Apple Health App on the iPhone. This data can be viewed by the user and shared with their healthcare provider from the app. 


Next is Google’s Fitbit Watch. This device is similar to the Apple Watch as the features center on heart rate monitoring and exercise, however, it is not as complex and comprehensive in its tracking measures. Fitbit is widely known for having all the foundational health tracking abilities at a reasonably accessible price, the lowest type of watch being $99 in comparison to Apple’s price of $249; this does not include data plans, which I will speak more on in my ethical analysis. In addition, Fitbit has a corresponding app for users to access their data on their phones. 


Finally, the Oura ring takes a slightly different approach as it measures heart rate and heart rate variability based on the fact that the ring is sitting on top of an artery, on the finger. In addition, it has been increasingly popular as it can measure stress levels, sleep scores, and temperature tracking. A key purpose of the Oura Ring is to provide individual users with their unique baselines for their activity levels, sleep, or additional suggestions. It is also important to note the “look” of the Oura Ring. The Oura Ring has a distinct style with the lowest type of ring costing $299. 


In summary, two major aspects that allow this type of technology to contribute to preventive heart health are its continuous monitoring and ability to personalize one’s healthcare. Originally, going to the doctor’s office once every five months showed one-time stamp data and did not reflect a patient’s ongoing health. However, with a wearable device and AI, this continuous monitoring allows for greater trend analysis and application in one’s life. An individual’s healthcare can also be more personalized and they can make informed decisions based on comprehensive data from their overall health. To define the personalization of healthcare, these devices will offer the opportunity to tailor treatment plans to each individual’s needs, thus leading to more effective suggestions and strategies (Abdul-Yun). This goal is achieved by the collection of data via these devices as it is unique to each individual’s health.  It also paints a picture with concrete evidence of how the user’s daily life activities impact their well-being as a whole. As a result, this moves away from the one-size-fits-all approach in healthcare and offers an opportunity for users to be engaged with their ongoing health status.

“Our goal is to empower people to take charge of their own health journey. With these innovative new features, we’re expanding the comprehensive range of health and wellness tools that we offer our users,” said Sumbul Desai, M.D., Apple’s vice president of Apple Health. 

Background and Integration of AI

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While these wearable devices track the data, AI will detect and synthesize a report based on this information. Artificial intelligence or AI is defined as “the ability of a computer or machine to perform tasks that are commonly associated with the intellectual processes and characteristics of humans, such as logic and reasoning,” (Copeland). Currently, the term “AI” is widely known, however, the majority of the time it is being used incorrectly and without a solid understanding of its components. 


Machine learning (ML) is the most referenced part of AI that improves its computer agents’ perception, knowledge, and actions based on experience and data. Deep Learning (DL) is a type of machine learning approach: it aims to compute the continuous data from the watch and scale big data. Contributing to big data refers to the machine’s immense and diverse collections of information. Big data is simply the growing information and data set that the AI is referencing from. Through defining AI and its subsets, it can be logically connected to how the AI is trained to recognize patterns to make timely predictions and analyses without being explicitly programmed (Seitz). Now, I will connect AI’s characteristics and capabilities to its healthcare integration with a wearable device. 


The pathway of AI is illustrated as follows: detection, review, analysis, integration, and results. Beginning with data processing, data collected from a wearable device, for instance, an Apple Watch, is raw data and needs to be fully processed to ensure consistency and accurate analysis. This involves combing through or filtering- pinpointing the most relevant and key data points, such as heart rate variability, activity, and sleep. From this data, the AI adapts and compares it to historical, past, and present charts based on the user’s initial input on their health. Next is analysis, once the AI has validated the data and filtered through it, in real and efficient timing, it will analyze the focused data points and use it to make predictions about the risk of cardiac issues. From this, an analysis of a synthesized prediction as well as recommendations is generated for the user. This could include the user’s potential issues and suggestions the AI has based on different categories of data points, such as lifestyle changes or a recommendation to seek medical attention. 


 AI is increasingly becoming more and more integrated into our daily lives through the devices that we use, including our phones, computers, and wearable devices. Currently, there is a plethora of developing AI healthcare platforms that will have the data uploaded from the user’s device. There is also the emergence of more powerful and capable AI algorithms being incorporated into wearable devices, for example, the Apple Watch Series 9 and the Samsung Galaxy Watch Pro. AI detection will synthesize and predict from user data to form an analysis of a patient’s health status. This will be automatically shared on its corresponding health app that can be accessed by doctors and healthcare providers. This information will be a valuable tool for customizing a plan for individualized preventative healthcare. 

"AI is the ability of a computer or machine to perform tasks that are commonly associated with the intellectual processes and characteristics of humans, such as logic and reasoning,” B.J. Copeland, Britannica.

Stakeholders 

When analyzing the ethics of AI and wearable technology for heart health, I will begin by establishing the main stakeholders involved: patients, doctors, companies, the government, and society as a whole. Foremost, patients who are more susceptible to a heart issue or condition are more likely to be users of this technology. Specifically, this includes men past the age of 45 and women past menopause as the highest category of people at risk ("ABCs of Knowing Your Heart Risk."). They will be the ones receiving the personalized medicine and have their health tied to their lifestyle choices as the wearable data tracking will not just include heat rate, but also other factors such as sleep, exercise, and stress levels. The patients will be the ones having their data monitored continuously, which these large corporations, such as Google or Apple, will have access to. 


Next is the doctor. In practice, the doctor or healthcare provider may decide how patient data from the wearable is used in a professional healthcare setting and what the role of AI in detecting the data will be (working alongside the doctor, central diagnoser, or as a suggestion). Throughout this paper, I am assuming that the doctor will be working in tandem with the AI as a “helping hand”. Additionally, the doctor will have access to the patient’s data through corresponding health apps and reports with the AI integrated throughout. There are various regulations that the doctor must abide by in regards to patient privacy, the most well-known being the Health Insurance Portability and Accountability Act (HIPAA). I will address this later in my ethical costs analysis. There is also the issue of  AI  being integrated and implemented into healthcare facilities. The cost of this effort is difficult to estimate as it differs among individual hospitals and hospital groups. Currently, AI is an area in which hospitals have been projected to spend $2 billion annually within four years of actively implementing it into healthcare practices (Miyashita). Lastly, AI will increase the efficiency of a doctor’s work and shorten wait times, therefore contributing to a change in doctor-patient relationships. 


The leading companies in the current AI market are Google, NVIDIA, Microsoft Healthcare, Amazon, and Apple. Currently, the AI market is projected to grow by $1.3 trillion in the next eight years. Additionally, AI in healthcare is one of the leading categories of growth, just below consumer AI (“AI to Become a $1.3 Trillion Market by 2032”). This results in these companies having a great amount of control over the future of healthcare as technology is being increasingly used and developed to fit healthcare’s needs, adding to the Medical Industrial Complex (a term used to describe how U.S. healthcare is increasingly more profit and business-oriented). These companies also have access to patient data and analytics, presenting issues on privacy that I will expand upon in my ethical costs analysis. By buying a wearable, are we actively contributing to companies honing greater power over healthcare, thus contributing even more to the Medical-Industrial Complex? 


The Federal Drug Administration (FDA) and the government play a crucial role in determining how AI will be implemented in a professional setting and work as a diagnoser. Currently, there are few clear standards for AI regulation and how it should be implemented in healthcare. In my ethical analysis, I will reference a potential outline that the FDA is proposing, focusing on regulating the use of AI. 


My final stakeholder is society as a whole. This technology will continue to advance our society in terms of innovation, but it is also important to keep in mind the social norms that may arise from this. For instance, disparities between the cost of different types of wearables and if seeing a doctor or therapist in person will be deemed a “luxury”.


Additionally, though this technology has the potential for Americans to be more proactive and efficient with their health, the widespread implementation of change in their lifestyle decisions may not be realistic. 


Ethical Benefits

Accessibility: Improving Care for Vulnerable Populations

Autonomy: Patient Empowerment and Advocacy


First, I will review how these technologies enhance and benefit the preventative care one receives through the values of accessibility and autonomy.


The ethical value of accessibility is defined as making goods, services, and information attainable and usable for all people. It is often mentioned that poverty can take a toll on one’s health. The increased risks for heart disease and other chronic conditions are often associated with certain lifestyle factors, such as inaccessible healthy food options. Many people of a lower socioeconomic class may suffer from food insecurity and only have access to the cheaper items in a store which are most often unhealthy fats like chips, cookies, or frozen meals. The doctor visits can also be costly and unattainable for lower-income populations, resulting in reduced access to preventative health screenings, checkups, and medication. Additionally, transportation is a major issue when it comes to doctor’s visits in rural communities. It is evident those living in rural areas have to travel 2 to 3 times farther than those in sub/urban areas. Furthermore, people who already live with varying chronic conditions may not be able to leave their houses  (National Institute of Health).


Wearables with AI can open the door to providing accessible preventative care for lower-income populations. Without having an in-person doctor’s visit, patients can access recommendations for a healthcare plan through the use of their wearable device and AI detection. This is particularly helpful in those metrics that highlight lifestyle choices that may increase the potential risk of heart disease. By using AI, each user receives a personalized synthesis of their data. This access allows patients to play an active role in their preventative health approach even if their in-person doctor’s visits are only once every year. However, it is important to assess the extent to which all of this information is understood by the patient and whether or not they choose to initiate change based on it.  I will talk more about this under the value of autonomy. In a recent case study from Southeast England, patients fitted with a Wi-Fi-enabled armband had their vital signs remotely monitored. This included their respiratory rate, oxygen levels, pulse, blood pressure, and body temperature. A National Health Service (NHS) pilot program used AI to analyze all this patient data in real-time. The conclusion of  the study was that through early detection ER visits were reduced, and the need for home visits dropped by 22%. This study sheds light on the possible value of this technology reducing the need for in-person visits, thus being accessible and of quality usage for vulnerable populations. 


Next, a key issue with this technology is affordability for those of a lower socio-economic status. A model that reflects well upon this is having insurance companies provide wearables for patients as a prerequisite to a specific insurance plan or if they should be provided as a public expense. There is also the question of cellular data costs as many of these well-known devices, like the Apple Watch and Fitbit, use a significant amount.

While acknowledging the potential of integrating wearable devices and AI to improve access to care, it is imperative to address the societal stratification that may stem from this. Would this possibly lead to in-person doctor’s visits seen as  “luxury”? If seen as a “luxury”, time with a human physician may be significantly more valuable and, therefore only attainable by those of a higher socioeconomic status. Moreover, there is also the argument that wearable products are a social divider to show off wealth. For instance, a user wearing an Apple or Samsung watch is seen vs. a less-known branded device that may be insurance-backed or have a type of payment model for lower-income access. Though this technology, in my description above, may provide care for all, it would differentiate the quality of care everyone receives. It is then crucial to ask, is some care better than no care at all? 


Additionally, autonomy is defined as the right to self-determination and respects the individual’s right to informed decision-making. Wearables and AI will empower patients to take charge of their own health since the device continuously tracks data and metrics. This data, the synthesized analysis, and the prediction done by the AI are all at the user’s fingertips. As they have access to all of this information, users will be more informed, allowing them the ability to promptly advocate for what they have noticed with their primary care provider or seek urgent medical attention. As mentioned in a study focusing on wearable devices and AI used in a cohort of patients, a 27-year-old interviewee mentioned, “Precise data will complement what the patient is saying …. It will replace questionnaires and box-ticking,” (Tran-Riveros-Ravaud). This encompasses improving the doctor-patient relationship as both the informed patient and the doctor can work with the data to improve health plans to address the patient’s ongoing health. The patient's decision-making capabilities will improve, allowing them to make properly informed decisions that best fit them. 


By users having real-time feedback on their health metrics, there is the opportunity for them to become more engaged and focused on their health based on what the AI-integrated algorithm suggests. Users may become aware of how their daily lifestyle choices, such as exercising, affect their health by seeing the data that supports it from their device. As a 30-year-old interviewee states, “[c]onnected applications and tools will help patients in monitoring their symptoms by guiding their observations and informing them. This will reassure them and help them to better know themselves and their diseases,” (Tran-Riveros-Ravaud). Using this technology will guide the user to understand not only what their health looks like, but what their “normal” looks like and how to maintain that (in regards to controllable risk factors, ie. diet).  However, ultimately,  it comes down to the patient being proactive and making the decision themselves. Even if they do have real-time, constant metrics in front of them that display their ongoing health status, who is to say that they will actually implement it? 


While ideally, users who have access to all this information should be actively using it and applying it to their lives, this is not always the case. This presents the idea of information overload: although users have the information to make informed decisions there is an issue of enacting and applying it to one’s health. 


In short, the patient may lack motivation or understanding of what to do with this abundance of information and disregard it. Additionally, upon viewing their data, a patient may feel overwhelmed and in a constant state of fear- this is known as patient anxiety. From the beginning, they may think they feel a “flutter” or a “skipped beat’, waiting for that one abnormal data point to appear on their watch. Let’s now use the scenario that Patient A uses an Apple Watch and can view their report on their iPhone. Once the AI synthesizes and gives a summary of their data on the iPhone, the individual could go into a spiral and rely on “Dr. Google” to explain what each inference or prediction means, never consulting a human practitioner. Patient A then becomes in a frozen state; always watching their data in fear, but never acting on their health needs.


In summary, the benefits of wearables working with AI are numerous as it shifts how we approach healthcare overall. These devices allow vulnerable populations to have access to a preventive approach to their own health and have greater insight into their risk factors based on their unique data trends. This combats the cost of in-person doctor visits, and with the preventative aspect, reduces the potential of a user from entering a cycle of ongoing treatments. Wearables with AI also give patients greater autonomy by empowering them to take greater control by being constantly engaged with their metrics. Having access to this data increases their decision-making capabilities when making lifestyle choices or going to the doctor's office. On the other hand, this also presents the possibility of patient anxiety due to being overloaded with information. 


Ethical Costs 

Privacy: Who Has Access To Your Data? 

Accountability: Who Should Be Held Accountable- the Doctor or the AI? 


Next, I will review how these technologies present a major concern in regards to the ethical costs of privacy and accountability. 


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Foremost, privacy is defined as the right of individuals to control access to their personal information and be protected from unwarranted access by others. As the prevalence of these technologies rises, so does the issue of privacy. These wearable devices track immense amounts of data and all of it must be stored in a digital cloud. Each cloud, no matter if it is Apple or Google, is never truly secure. In the Digital Age and as the lines between life online and life in reality blur, it is getting increasingly easier to have users click the “I accept” button to a terms and conditions policy statement without the user even reading it. This presents a major privacy risk for patient data as it is unknown who can see your data and if they can share it; major companies often allow third-party companies to access cloud information. For instance, Fitbit, a fitness tracking device, faced a class-action lawsuit in 2011 for “allegedly selling personal health data to third-party advertisers without user consent,” (Peres da Silva). This leads to potential instances of ad-targeting for a specific product, or service, including medicine based on the individual’s demographic. This could specifically be harmful to vulnerable populations, such as the elderly. The elderly are less versed in navigating life online and, therefore may believe each pop-up advertisement or alert on their device is real. However, in truth, it is just third-party companies targeting these individuals with ads or other compelling alerts. This leads to the question: to what extent do we value our health data? And should we value it over our social data? 


With the risk of health data, it is important to bring up the legal perspective. First, HIPAA’s (Health Insurance Portability and Accountability Act) privacy rule “requires appropriate safeguards to protect the privacy of protected health information and sets limits and conditions on the uses and disclosures that may be made of such information without an individual’s authorization” (“HIPPA Compliance). The issue with this is that one, it does not account for the updating technologies of today, and two, it fails to work around the fact that the majority of these companies get users to hand over their data by presenting a screen that does not let one pass without accepting. 


On the other hand, data tracking online has become a norm. It is widely acceptable to hit “accept” on all the websites that ask for “cookies”. The “cookies”, like a cookie trail, allow websites to identify an individual and track their browsing. While this seems like a violation of privacy, it has become normal and somewhat contributes to having a personalized experience online as users can see posts that are relevant and interesting to them. Based on this, perhaps data tracking one’s health may increase the emphasis on personalization. 

Accountability is taking responsibility for one’s actions, including any errors made. The main question posed with accountability is who should be held accountable, the doctor or the AI?

A faulty issue with AI is its hallucinations and capability to make a misdiagnosis. An AI hallucination is an output error made by the AI algorithm due to biased or unrepresentative data. The lines between truth and fallacy can get easily blurred, making it difficult for both the patient and the doctor to navigate and understand the accuracy of the AI. 


If there is an issue that occurs when the AI is working in tandem with the doctor, currently there is a lack of standards to combat this. According to the AI Accountability Policy by the National Telecommunications and Information Administration,  “relevant actors must be able to assure others that AI systems they are developing or deploying are worthy of trust and face consequences when they are not.” In addition, it includes that they must be valid and reliable, while accountable and transparent. Although the Biden-Harris Administration has worked to address the AI policy, there are no clearly outlined standards as to what AI integration looks like, especially as different AI algorithms are being created exponentially.  

 The lack of transparency, both within the algorithms and the standards, accounts for the discrepancy when it comes to holding someone accountable when a mistake is made.

Based on the developing current US policies, it does not specify AI in healthcare, only the user’s interaction with it. These policies hold the manufacturer accountable instead of the doctor or the algorithm. In essence, this would be related to product liability as a legal solution. However, another approach to this would be looking at physicians, manufacturers, and hospitals as a common enterprise. This would shift away from “individualistic notions of responsibility, embodied by negligence and product liability, and toward a more distributed conception,” (Chan). The idea of a common enterprise would create a more mindful approach to how the standards of AI are approached in smaller environments, such as its implementation in hospitals. 


 Moreover, another major issue when navigating AI’s accountability is its inherent structure. As previously mentioned, AI learns through its subset of ML (Machine Learning) based on the data sets provided and its experience. If its data set is biased, for instance only collecting data from 7 coastal states out of all 50 states, or collecting information from a higher socioeconomic demographic, there is the potential for significant bias. The FDA has outlined four general principles as a potential approach when addressing the question of AI in healthcare and its adaptive learning. I will be focusing on the first one: Clear expectations for quality systems and good machine-learning practices. The FDA expects SaMD (Software as a Medical Device) developers to “have an established system to ensure that their device meets the relevant quality standards and conforms to regulations,” (“How The FDA Regulates AI”). However, again, there is a discrepancy as this potential policy only outlines SaMD developers, which are devices intended for medical use. Wearables, such as the Apple watch, will not have to go through these specific regulations, thus calling for another approach. 


In summary, the ethical costs of wearables and AI in healthcare are seen through the values of privacy and accountability. Privacy issues align with specific data collection and involve the issue of the role of companies in patient privacy. Furthermore, if there is an error or misdiagnosis, there are inadequate guidelines made by the FDA. There are no clear standards on how AI should be implemented or regulated, presenting an issue as AI algorithms can possibly hallucinate, or present inaccurate information. 


After assessing the ethical benefits and costs, I believe that implementation of these technologies is a double-edged sword, with the benefits of implementation greatly important, but how it is regulated needs to be decided before it becomes of widespread use. Constantly being aware of metrics will encourage users to be more aware of lifestyle choices, which is a major factor when it comes to heart health as well as having access to quality care. Knowing that heart disease is one of the leading causes of death in America, I believe that these technologies have immense opportunities to revolutionize American healthcare and perhaps even the world. However, while acknowledging the benefits of this technology,  I do believe that health privacy takes priority.  Health privacy encompasses patient information and records, which can be used to target vulnerable populations with scams and possibly exploit people. Each of these companies can sell patient data to third parties or involve them in health studies that they may not even be aware of. In order to combat this, there needs to be strict regulations by the U.S. government on both AI implementation as well as the use of company-developed AI. Government agencies, specifically the FDA, must clearly outline to companies the standards of AI algorithms. Without this, AI detection can become biased and inaccurate as the information that they output is representative of the data that the company inputs. It is imperative to have clear consequences to ensure proper standards of use and development of these technologies as these software companies increase their weight on the U.S. healthcare system. 


Conclusion

In exploring the ethical implications of integrated AI and wearable technology for preventative cardiovascular health, this paper first identified the ethical benefits through the values of accessibility and autonomy.  This section focused on how vulnerable populations, such as individuals living in rural areas or of lower socioeconomic status, will have access to a preventative approach with less need to spend on in-person doctor visits. The synthesized reports generated by the technology increase the patient’s understanding of their health status and promote informed decisions when making lifestyle choices. On the other hand, the integration of these technologies presents the ethical costs of privacy and accountability. Privacy issues arise with specific data collection and involve the question of the role of leading companies in AI in patient privacy. Moreover, if there is an error or misdiagnosis, there are inadequate existing guidelines made by the FDA to determine who is accountable. 


As I have discussed how AI and wearables will be integrated into preventative care for cardiovascular health and the surrounding ethical benefits and costs, I believe that harnessing the benefits from these technologies can only be achieved through implementing standards of use. In order to address these concerns about how wearables and AI should be regulated, I will outline current government regulations and my ideas for the future.


Although, as I mentioned in my ethical analysis, there are some potential FDA standards, these are merely proposals that have yet to get approved. The FDA’s approval process will not be sufficient as this technology is increasing exponentially. Moreover, the Biden-Harris Administration has established the National AI Advisory Committee. This committee’s main purpose is to provide advice and make the public more knowledgeable about AI. Both the FDA and the AI Advisory Committee will experience difficulties enacting AI standards of use in health. This is why I believe there should be a government sub-association, similar to the FDA, that is solely dedicated to the use and implementation of AI. This group would create codes of conduct and regulations to ensure standards of use when AI is being applied in a professional healthcare setting. 


Overall, heart disease is the biggest killer in America and instead of looking at fixing the problem, we as a country should be looking at how to prevent it. AI and wearable technologies offer us a tool for taking a preventative approach by leading a healthy lifestyle. Today, we can see the evolution of the “bigger” Apple Watch, to the small and thin Oura Ring. As this technology is becoming more discreetly associated with our bodies, its ease of use and incorporation into our daily lives will only become more natural. So, as we look toward the future, is this like opening Pandora’s box? How much of this data is informative, versus how much is controlling- taking away from the spontaneity of life?


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