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I chose to focus on healthcare as I plan to pursue a career within this industry. I am particularly interested in learning more about how artificial intelligence (AI) can be used to improve healthcare outcomes and how that may influence my future career. As AI continues to evolve, its integration with healthcare brings new and exciting advancements that have the potential to improve patient care, provide more accurate diagnoses, and enhance workflow.

In this Padlet wall, I will explore three specific examples of AI applications present in healthcare: OpenEvidence, a tool used to assist healthcare workers in making clinical decisions; Qure.ai, a tool used to interpret medical imaging and help provide diagnoses; and Medtronic’s Hugo™ RAS system, a robotic-assisted surgery tool that uses AI to improve precision in surgical procedures. Furthermore, I will analyze the benefits and limitations associated with each tool and how that affects their overall impact on healthcare. In addition, I will examine the future trends and potential developments of AI in healthcare, focusing on ethical concerns such as data privacy and embedded biases. I will also explore the societal impacts of utilizing AI in healthcare, highlighting the impact on employment and equity within the industry.

As someone going into the healthcare industry it is important for me to understand how AI is influencing and transforming the field. These technologies are likely examples of what I may encounter in the future, so I need to understand how they work and how to use them effectively.

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OpenEvidence- AI in Clinical Decision Support

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OpenEvidence- AI in Clinical Decision Support

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Description:

This AI platform is designed to assist healthcare professionals with point-of-care questions by taking information from trusted and peer-reviewed sources⁸. It primarily uses large language models to interpret clinical information and recognize critical insights. Additionally, it combines machine learning and natural language processing to analyze medical literature and patient data. It is highly accurate and quick to answer questions, saving time and uncovering patterns or key details that may otherwise be missed⁹. However, many healthcare workers are hesitant to fully trust this without human oversight as it has the potential to relay false information which can lead to a misdiagnosis⁴.

OpenEvidence Image

Personal Insights:

I find this tool to be very interesting as it can cut the time needed to pinpoint a problem or answer a question in half. Additionally, it provides highly accurate answers as seen in a case where the tool delivered the proper treatment advice for a patient with hypertension⁴. While I find this to be a fascinating tool, it is counterintuitive in healthcare. The healthcare professionals that this is targeted to already contain the knowledge and skills needed to make these decisions, so there is no need for them to rely on AI for answers they already know. Furthermore, I believe it would be harmful to allow medical students access to this tool as they may become overly dependent on AI for advice when they start to practice in the healthcare industry.

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Why Doctors Say OpenEvidence Is A ‘Game Changer’

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Qure.ai- AI in Medical Imaging and Diagnostics

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Qure.ai- AI in Medical Imaging and Diagnostics

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Description:

Qure.ai is an AI tool that possesses an advanced set of algorithms that can automatically interpret medical imaging such as X-rays, CT scans, and MRIs¹. It utilizes the technology of machine learning, deep learning, and natural language processing to achieve this¹². Additionally, this tool allows for faster patient diagnoses and enables healthcare professionals to treat higher priority patients more effectively. Furthermore, it is very versatile and quick to adapt, meaning it can be applied to any healthcare setting with minor difficulties¹. There is fear that the tool may have bias embedded in the algorithm which influences its diagnostic process¹².

Qure ai Image

Personal Insights:

I find this tool to be extremely interesting as it can quickly decipher medical imaging which takes doctors years of training and practice to achieve. Furthermore, it does so with high precision and accuracy. I believe that this tool has amazing potential in the healthcare industry as it improves workflow and overall patient care. It can be especially impactful in underdeveloped countries that lack an abundance of well trained healthcare professionals as it can help fill in any gaps in diagnostic services and ensure all patients are well taken care of.

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Meet the Startup: Qure - Transforming Healthcare Pathways With AI

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Hugo RAS System- AI in Robotic-Assisted Surgery

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Hugo RAS System- AI in Robotic-Assisted Surgery

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Description:

This AI tool is a modular system used to assist healthcare professionals in minimally invasive surgeries. It builds off of the Da Vinci platform by integrating AI technology, including machine learning and deep learning to analyze key surgical procedures and provide real-time feedback. This system provides greater precision and control in surgery, allowing for more accurate procedures and reduced risk of errors⁵. Additionally, it promotes surgical training by providing performance analytics. However, there are many challenges with this system as it is still undergoing clinical trials in multiple countries such as the United States, which limits its accessibility within healthcare⁷.

Hugo RAS System Image

Personal Insights:

I think this AI technology is very fascinating as it can understand the complexities of surgery through algorithms which takes doctors years of school to understand and even more years of practice to master. I think it has the potential to significantly help surgeons as it is very precise with its movements and can even provide real time feedback which leaves less room for error. However, I think this tool needs extensive testing before it is released for clinical use as there are many risks involved when such tools are in direct contact with patients.

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First-ever Clinical Operation with the Hugo™️ RAS system in APAC | Robotic- Assisted Surgery

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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Currently, AI is being utilized to automate tasks that are time consuming and repetitive, in order to lighten the load of healthcare professionals. However, in the next 5-10 years it has the potential to become even more involved with patient care, acting as a copilot to the healthcare professionals. AI could assist with analyzing complex medical data, predicting patient outcomes, and suggesting personalized treatment plans based on such information. In addition, AI can improve upon the research in the healthcare industry particularly in the areas of disease and drug development². Although there are components of AI that can significantly advance the healthcare field, there are many ethical concerns that could limit its potential. For example, there are embedded biases within the algorithms of the AI tools which could lead to the misdiagnosis and treatment of a patient, this is especially prominent with patients of color. Additionally, many are concerned with the privacy of their data, as they don’t feel comfortable trusting AI with their highly sensitive personal information. Furthermore, there are issues with transparency as it is not understood how AI is able to arrive at its decisions, this is known as the “black box” problem¹⁰ . Personally, I hesitate to fully trust AI when it comes to making critical medical decisions as it can miss or falsely report important details that could cost an individual their life. I believe AI needs to be refined and tested more before it can be deemed safe to use in the healthcare industry.

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Although AI has amazing potential to transform the healthcare industry, it also comes with societal consequences. One of the primary concerns of AI being integrated with healthcare is the impact on employment. AI is able to automate the same routine tasks of healthcare professionals and is able to do so quicker and with the same accuracy. This raises concern for job displacement in the healthcare industry as AI can perform at the same level as human healthcare professionals, but does not require the same years of training as them¹¹. Additionally, there are privacy concerns associated with AI in healthcare as patients are not aware to the extent of which AI has access to their medical history and other personal information. Furthermore, this data is vulnerable to leaks through breaches or other internal and external violations⁶. There are also worries regarding the equity of AI in healthcare as the algorithms involved in AI are primarily trained based on the data of white male patients. Therefore, female patients of color are at a disadvantage when receiving care from AI as there is more room for error and improper treatment³. Personally, I believe that AI is a great aid, but it should not fully replace humans within the healthcare industry. They lack the empathy and interpersonal skills needed as a healthcare worker. Furthermore, before AI can even be introduced as a tool for the healthcare industry, its embedded biases must be researched and addressed to allow for equal treatment for all populations.

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From my research process I have a lot learned about the impact of AI on the healthcare industry and how beneficial it can be. Throughout my research I struggled the most with identifying the technology that each example utilizes as I am not well versed in technology or AI. I believe that AI has the potential to be significantly impactful in healthcare, however there are still many flaws that need to be addressed before it can be implemented into the healthcare industry. Therefore, it is necessary to further test AI algorithms in these next couple of years before complete integration.

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1. American Thoracic Society. (2023, May 2). qure.ai: AI For Healthcare. https://conference.thoracic.org/about/press/resources/industry-press-kits/qure.ai-media-kIt-revised_02_may_2023_compressed-21.pdf

1. This article provides a comprehensive overview of what qure.ai is and its benefits and limitations.

2. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future healthcare journal, 8(2), 188–194. https://doi.org/10.7861/fhj.2021-0095

1. This article highlights the future of AI in healthcare and the potential advancements in the industry.

3. Harvard Medical School. (2025, April 7). AI Implications for Health Equity: Shaping the Future of Health Care Quality and Safety. https://learn.hms.harvard.edu/insights/all-insights/ai-implications-health-equity-shaping-future-health-care-quality-and-safety

1. This article provides insight into the equity of AI in the healthcare industry.

4. Hurt, R. T., Stephenson, C. R., Gilman, E. A., Aakre, C. A., Croghan, I. T., Mundi, M. S., Ghosh, K., & Edakkanambeth Varayil, J. (2025). The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care Physicians. Journal of primary care & community health, 16, 21501319251332215. https://doi.org/10.1177/21501319251332215.

1. This article provides insight into the benefits and limitations of OpenEvidence.

5. Marino F, Moretto S, Rossi F et al. (2024). Robot-assisted radical prostatectomy with the Hugo RAS and da Vinci surgical robotic systems: a systematic review and meta-analysis of comparative studies. European Urology Focus, 11(2): 312-322. https://doi.org/10.1016/j.euf.2024.10.005 .

1. This article provides a comprehensive overview of the Hugo RAS system.

6. Moldt, J. A., Festl-Wietek, T., Fuhl, W., Zabel, S., Claassen, M., Wagner, S., Nieselt, K., & Herrmann-Werner, A. (2025). Exploring the social dimensions of AI integration in healthcare: a qualitative study of stakeholder views on challenges and opportunities. BMJ open, 15(6), e096208. https://doi.org/10.1136/bmjopen-2024-096208

1. This article highlights the privacy concerns associated with AI in healthcare.

7. Prata, F., Ragusa, A., Tempesta, C., Iannuzzi, A., Tedesco, F., Cacciatore, L., Raso, G., Civitella, A., Tuzzolo, P., Callè, P., Pira, M., Pino, M., Ricci, M., Fantozzi, M., Prata, S. M., Anceschi, U., Simone, G., Scarpa, R. M., & Papalia, R. (2023). State of the Art in Robotic Surgery with Hugo RAS System: Feasibility, Safety and Clinical Applications. Journal of personalized medicine, 13(8), 1233. https://doi.org/10.3390/jpm13081233.

1. This article discusses the functions and progress of the Hugo RAS system.

8. Sermo Team. (2025, September 25). How OpenEvidence AI is transforming clinical decision-making. Sermo. https://www.sermo.com/resources/openevidence-ai/

1. This article provides a comprehensive overview of OpenEvidence.

9. Shen, A. (2025, October 2). OpenEvidence. Contrary Research. https://research.contrary.com/company/openevidence

1. This article introduces the AI technology involved in OpenEvidence.

10. Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS digital health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810.

1. This article provides a comprehensive overview involving the ethics of AI within the healthcare industry.

11. Yakusheva, O., Bouvier, M.J., and Hagopian, C. (2025). How Artificial Intelligence is altering the nursing workforce. Nursing Outlook, 73(1): 103200. https://doi.org/10.1016/j.outlook.2024.102300.

1. This article highlights the impact of AI on employment within the healthcare industry.

12. Zavaleta-Monestel, E., Quesada-Villaseñor, R., Arguedas-Chacón, S., García-Montero, J., Barrantes-López, M., Salas-Segura, J., Anchía-Alfaro, A., Nieto-Bernal, D., & Diaz-Juan, D. E. (2024). Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment. Cureus, 16(6), e61585. https://doi.org/10.7759/cureus.61585.

1. This article discusses the AI technology and uses of qure.ai.