Robots In Healthcare: Ethics

Abstract

Robots in the healthcare system are becoming more prominent in the 21st century in order to improve patient care and surgical procedures. Robots can effectively mitigate the effects of the shortage of many healthcare occupations and the increasing workload on doctors. However, integrating robots in the healthcare field comes with various ethical concerns. For example, the healthcare system lost $21 billion in 2020 due to cyberattacks and ransomware, highlighting the need for a stronger, more robust cybersecurity enhancement (Witts 1, 2023). In addition, the malfunctioning of code and/or improper maintenance of robots can lead to a pause in the surgical process and risk additional lives. Ian Tucker, a former editor of Observer New Review's science, technology and nature section, Discover, and the deputy editor of the Observer Tech Monthly claims, “ [surgical robots were directly] linked to the deaths of 144 people” (Tucker, 2018). Moreover, robots used in surgery generally command substantial costs that many people cannot afford, thus they can have a detrimental impact. Dr. Bakshi collected data on robotic surgeries from multiple hospitals across the US and claimed that robotic surgery is $1,761 more expensive than regular surgery, and most healthcare insurances don't cover the cost (Bakshi, et.al., 2017). The extreme costs of a robot for treatment, the burden of cyberattacks, AI bias, as well as cold interactions, are new concerns in our evolving , tech-inclined world. Through scrutinizing various lenses, this paper aims to explore the aspect of the ethical benefits and concerns regarding the use of robots in the healthcare system.

Introduction

In 2024, the number of robots used in the healthcare system skyrocketed in the Asia-Pacific region and Europe. The Asia-Pacific region accounted for 80% of the robots purchased, whereas Europe stood at 20%. Robots were used in various fields such as hospitality, agriculture, professional cleaning, medical and healthcare, and lastly, transportation and logistics. It is important to keep in mind that there are currently 6,200 robots in the healthcare system, which saw the largest jump in comparison to other fields (Ito, 2024). Robots in the operating room have been proven to carry out the surgeries smoothly with higher success rates and “without errors” in comparison to traditional surgeries (DelveinSight, 2025). However, as beneficial as robots may be, it is imperative to understand the ethical aspects of integrating robots in the clinic and the operating room. Oftentimes, the ethical concerns of robots in the healthcare system are overshadowed by the great variety of benefits that the robot can provide in a stressful environment. One concern is that the older generations, most of whom did not grow up exposed to the level of automation and technology seen today, view robots as anti social.According to a patient survey conducted by Vallés-Oeris, Barat-Auleda, and Doménech, it was observed that the older generation thought that robots didn’t interact well with the patient, and they valued more towards actual interaction between themselves and the doctors and nurses (Vallés-Oeris, Barat-Auleda, and Doménech, 2021). This is just one example of a situation in which robots raise ethical concerns and might not be suitable for all patient groups. Other studies have proven that ethical concerns, cybersecurity risks, and AI bias can skew treatment plans if enough training data isn’t provided.

Methodology

In this paper, academic studies were utilized along with articles from credible individuals and government resources. Most of the academic studies and articles were acquired through Google Scholar and a simple Google Search. The following steps were used for the initial research and were divided into common themes.

1. Find credible articles from Google Scholar and use keywords such as “robots in healthcare”, “surgical robots”, and “clinical robots.”

2. Find a common theme in the chosen articles and academic studies. The ones utilized particularly for this study are “Cyberattacks and Ransom”, “AI Bias in Robots”, and lastly “Cold Interactions.”

Cold Interactions

Robots are deteriorating meaningful interactions between the healthcare staff and patients. Stahl and Coeckelbergh stated, “It is highly doubtful if robots could ever be empathic” (Stahl and Coeckelbergh, 2016). In the healthcare setting, the staff must show empathy, and with robots as a replacement for the healthcare staff, it might be harder for patients to recover from mental trauma, depression, anxiety, palliative care for terminal illnesses, and more. To add on, in the National Library of Medicine (NIH), a US government website, Dr. Farhud and Zokaei claim that “In Obstetrics and Gynecology, any clinical examination requires a sense of compassion and empathy, which will not be achieved with robotic doctors” (2023). Dr. Farhud is a professor of Medical and Clinical Genetics in Iran, and Shaghayegh Zokaei is a researcher for the KKI-Johns Hopkins Hospital. In the article, they presented many ethical issues with modern-day robots, such as the Tommy nurse robot (monitors and communicated with doctors, measure vital signs, reduces infection risk especially during the COVID-19 pandemic, supports overburdened staff and improves efficiency), along with the Mitra robot (communicates with doctors and nurses, taking vital readings, reminding patients about medications, interacts with patients in a human-like way, takes remote consultations, screening and checking symptoms, and collect data) in India. Robots can’t support patients who are facing emotional problems; although they will be able to diagnose the patient, the treatment of patients can be long and extensive. Without a doctor’s sympathy, patients will feel alone in their treatment process and may face mental health problems later on. This can be prevented if doctors limit the amount of time a patient gets to spend with the robot and have human interactions. Robots can’t support patients who are facing emotional problems; although they will be able to diagnose the patient, the treatment includes a wide variety of factors beyond a simple diagnosis and solution. Without a doctor’s sympathy, patients will feel alone in their treatment process and may face mental health problems later on. In order to mitigate the harmful effects of robots on patient’s mental health, doctors can decrease the exposure between the patients and the robots and have more meaningful interactions.

Cyberattacks and Ransom

Robots used in healthcare facilities can be easily breached, and personal information can be stolen. A University of Washington engineering team ethically hacked a next-generation teleoperated surgical robot, one used only for research purposes, to test how easily a malicious attack could hijack remotely-controlled operations in the future (Langstone, 2015). The students were able to gain complete access to the healthcare robot and could control the movements and bypass the orders from doctors. This underscores the fact that robots remain vulnerable to hacking, raising concerns about their safety and reliability during surgery. With hackers potentially having control over the robots, “[they] may disclose the patient's health information that is very sensitive and confidential, raising the confidentiality issue of the channel” (Jain and Doriya, 2022). Jain is an assistant professor at the Indian Institute of Technology at Bhopal, and Doriya is an assistant professor at the National Institute of Technology, Raipur. In their article, they included various “weak points” in a medical robot. The ease of hacking medical robots, along with the danger of having the robot controls outside of the hospital setting, may be dangerous and impact both the patient and medical staff negatively. With this in mind, hackers may commit financial and medical fraud in order to gain large sums of currency, sell data on the dark web, and use ransomware attacks to prevent healthcare organizations from accessing patient information.

AI Bias in Robots

AI bias in robots can result in poorer diagnoses of patients' illnesses. AI is used in the training algorithm of robots, specifically in order to diagnose and treat patients effectively. However, the training data can also be biased. For example, “if a rehabilitative robot is interacting with a female child, its training data set might indicate that female patients do not like video games and thus remove from consideration the use of such therapy” (Howard & Borenstein, 2019). In this case, even if certain girls would like to play video games, the robot wouldn’t suggest this method to a female patient, potentially preventing the patient from benefitting from that form of therapy. This bias also applies when the training data originates from a different location or country. For example, if the training data is acquired from Japan, where obesity rates are one of the lowest in the entire world, it may be assumed that the same percentage of adults are not overweight in the US; however, this is untrue. The training data used from Japan can create biases in the US and assume that the same percentage of adults in Japan and the US are overweight. To elaborate, Brian Scassellati, a professor at Yale University, claimed that due to AI bias, “in the Netherlands, doctors have refused to adopt AI systems created and tested abroad” (Scassellati, 2021). Instead, if the training data were from the Netherlands, the robot would be able to treat and disguise Dutch patients more accurately. This also happened in the following example: In the US, there is a higher percentage of obesity compared to countries such as Japan. If the robot is trained to “think” that breathlessness is a symptom of obesity, it doesn’t mean that other illnesses don’t have shortness of breath. AI learning of robots cannot be trusted since the information and training may be different for each robot, thus resulting in various outputs. AI learning in robots depends on the materials that are used to train the robot, as well as the place where they are trained. Therefore, as robots “learn” as they treat patients, they may have more bias towards certain illnesses than others. But to mitigate this effect, it is suggested that robots should be trained with data provided by many international hospitals to precisely diagnose and treat patients across various countries.

Discussion

The ethical aspects of robots in the healthcare system are negatively affecting the public as well as the hospital administration. The costs of robots are already staggeringly high, and having ethical issues such as AI learning, cyber-attacks, illegal use of personal data, and meaningful interaction with patients creates an extra burden on hospitals and patients to “pay” for the price. But robots in healthcare are also benefiting many patients since they decrease the recovery time compared to the traditional way of surgery and save lives due to a lower chance of malpractice. Companies that design robots can avoid cyberattacks by creating stronger cryptography systems and detect vulnerabilities servers by spreading awareness, hiring more cybersecurity experts and engineers and having more software updates. Especially as the cost of these robots are high due to the fact of hiring employees such as cybersecurity experts, working on research and design and making sure the robot fits in the rules and regulations, the US government and health insurance could compensate for the expense of robots used in hospitals, and more people could get lifesaving treatments. Though this will take time, doctors should be the primary and ideal practitioners to diagnose, treat, and perform surgeries, while robots should be their assistants. By integrating robots in the field of healthcare, there would be more economic and scientific breakthroughs in the US healthcare system.

This being said, there are ethical aspects that shouldn’t be overlooked by the benefits that robots can provide in the healthcare setting. Research is still ongoing to develop a well-designed robot that can address the ethical concerns raised by patients, doctors, researchers, political leaders, and the public. Robots are useful in various ways and can address the problems that many hospitals face but it is important to recognize the need for a cheaper, safer, and empathetic robot.

Conclusion

As more technological advancements are made, developers are trying to improve upon the ethical concerns that medical and surgical robots bring within the healthcare facility. Due to biases in the robots’ training algorithms, cold and impersonal interactions with patients, and the high risk of hacking and data misuse, there is a need for higher-quality research—especially focused on improving robot safety and enhancing the accuracy of AI systems. In addition, robots should be integrated into treatment along with in-person interactions between patients and healthcare staff, or use different approaches so all patients, regardless of age groups, feel accommodated. With more development and research, medical and surgical robots may be able to combat these ethical concerns and improve the healthcare system drastically.

References

1. Bakshi, Godwin, Teixeira, C. A. J. (2017). Cost Analysis of Robotic Assisted Surgery vs Laparoscopy in General Surgery. SAGES. Retrieved November 13, 2023, from https://www.sages.org/meetings/annual-meeting/abstracts-archive/cost-analysis-of-robotic-assisted-surgery-vs-laparoscopy-in-general-surgery/ 

2. Farhud and Zokaei, D. a. S. (2021, November). Ethical Issues of Artificial Intelligence in Medicine and Healthcare. NCBI. Retrieved November 13, 2023, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826344/ 

3. Howard, A., & Borenstein, J. (2019, January, 1). Trust and Bias in Robots. American Scientist. Retrieved November 13, 2023, from https://www.americanscientist.org/article/trust-and-bias-in-robots 

4. Ito, T. (2024, October 8). Sales of Service Robots up 30% Worldwide. International Federation of Robotics. Retrieved June 24, 2025, from https://ifr.org/ifr-press-releases/news/sales-of-service-robots-up-30-worldwide

5. Jain and Doriya, S. a. R. (2022, May 24). Security framework to healthcare robots for secure sharing of healthcare data from cloud. NCBI. Retrieved November 17, 2023, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128770/ 

6. Langston, J. (2015, May 7). UW researchers hack a teleoperated surgical robot to reveal security flaws. University of Washington. Retrieved November 13, 2023, from https://www.washington.edu/news/2015/05/07/uw-researchers-hack-a-teleoperated-surgical-robot-to-reveal-security-flaws/ 

7. Robots In Healthcare | Benefit, Disadvantages and Future of Medical Robot. (2025, June 23). DelveInsight. Retrieved June 24, 2025, from https://www.delveinsight.com/blog/robotics-in-healthcare

8. Scassellati, B. (2021). Hard choices: AI in health care < Yale School of Medicine. Yale School of Medicine. Retrieved November 17, 2023, from https://medicine.yale.edu/news/yale-medicine-magazine/article/hard-choices-ai-in-health-care/ 

9. Stahl and Coeckelbergh, B. a. M. (2016, August 18). Ethics of healthcare robotics: Towards responsible research and innovation. Science Direct. Retrieved November 13, 2023, from https://www.sciencedirect.com/science/article/pii/S0921889016305292#:~:text=Privacy%20and%20data%20protection%3A%20Robotics,to%20them%2C%20and%20so%20on.&text=Safety%20and%20avoidance%20of%20harm,be%20safe%20to%20work%20with. 

10. Tucker, I. (2018, March 25). Death by robot: the new mechanised danger in our changing world. The Guardian. Retrieved June 22, 2025, from https://www.theguardian.com/technology/2018/mar/25/death-by-robot-mechanised-danger-in-our-changing-world

11. Vallès, N., Barat-Auleda, O., & Domènech, M. (2021, July 28). Robots in Healthcare? What Patients Say. MDPI. Retrieved June 24, 2025, from https://www.mdpi.com/1660-4601/18/18/9933

12. Witts, J. (2023, March 28). Healthcare Cyber Attack Statistics 2022: 25 Alarming Data Breaches You Should Know. Expert Insights. Retrieved November 13, 2023, from https://expertinsights.com/insights/healthcare-cyber-attack-statistics/