AI is not always superior to human diagnosis. While AI has an immense capacity for information, it can sometimes learn the data too well and can inadvertently lead to poor outcomes or incorrect diagnosis when new data is presented. This is called “Overfitting” (Chustecki, 2024). 

Basis: AI only knows the information that it is given, so if the information is not well-rounded, prejudice and inequality are risks to data results and analysis due to unrepresented data (Chustecki, 2024). 

Accountability: AI makes mistakes and machines fail, just like humans. But the problem with that comes down to who we hold accountable for the mistake. Do we hold the provider accountable that has no knowledge of how machines and advanced technologies work, or do we hold the developer accountable, who has no clinical experience (Chustecki, 2024)? This topic remains unanswered.  

Priorities and Goals

Improving disease diagnosis, treatment options, data analysis, patient education, personalized mental health support, virtual assistants, and so much more (Alowais et al., 2023). 

Diagnosis Accuracy: example: Breast cancer diagnosis. Mammograms were uploaded into the database, and then human readers input what specific abnormalities and signs they were looking for. The technology then combined all the data and was able to detect breast cancer quicker than a human reading the scans. (Ahn et al., 2023) This kind of technology can help improve accuracy and efficiency, while removing the need for multiple human readers. (Ahn et al., 2023) 

Patient Education: AI Chatbots are being used to help answer patient questions 24/7 at their fingertips (Alowais et al., 2023). One study has shown that the use of ChatGPT helped diabetes patients understand their diagnosis, helped them choose between treatment options, monitor their symptoms, as well as help guide them on lifestyle changes like diet and exercise (Alowais et al., 2023). 

Reach as many patients as possible: The main goals for telehealth and AI are focusing on reaching as many patients as possible, improving patient outcomes and experiences, improving quality of care, and decreasing medical care costs (Haleem et al., 2021).  

Patient Trust: Healthcare professionals are trusted to give advice and treatments to patients based on their training and expertise, but with the integration of AI, many providers are frowning upon their implementation due to the belief that patients are now questioning the providers authority (Steerling et al., 2023).  

Large groups of providers that are well established in their careers are not easily engaged and motivated to integrate AI into their practice verses practitioners who are early on in their careers are more eager to use this kind of technology and innovation in their practice (Svedberg et al., 2022).  

Confidentiality: Health data that is input into many AI systems can be used by other countries to help improve their data as well as patients receiving care from across the world (Yadav et al., 2023). Other countries do not have and follow the same privacy laws that we have in the states such as HIPAA (Yadav et al., 2023). Even with high security firewalls in place, there is always a chance for a data breach to occur. In 2023 there was the second largest data breach in the United States with over 11 million people affected (Alder, 2025). Data breaches are on the upward trend across the country due to vast amount of technology usage in healthcare today (Alder, 2025). 

The benefit to nursing staff of having virtual access to pharmacists is direct education on prescriptions.  According to Rural Health Information Hub in states where virtual pharmacy is available, the pharmacist is required to do direct virtual education with the patient on prescriptions prior to prescription authorization (2025).  This allows nursing staff to have further reinforcement of patient education prior to discharge  

These programs specifically benefit nurses in public health who work with special patient populations.  By providing quicker access to preventative treatment options via telehealth, it assists in the prevention of the spread of sexually transmitted diseases in local communities.  These programs also build seamless transitions to access to care without straining patient volumes on community provider groups.  

Ahn, J. S., Shin, S., Yang, S.-A., Park, E., Ki Hwan Kim, Soo Ick Cho, Ock, C., & Kim, S. (2023). Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. Journal of Breast Cancer, 26(5). https://doi.org/10.4048/jbc.2023.26.e45 

Alder, S. (2025, May 26). Healthcare Data Breach Statistics. The HIPAA Journal. https://www.hipaajournal.com/healthcare-data-breach-statistics/ 

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, A., Harbi, S. A., & Albekairy, A. M. (2023). Revolutionizing healthcare: the Role of Artificial Intelligence in Clinical Practice. BMC Medical Education, 23(1), 1–15. https://doi.org/10.1186/s12909-023-04698-z 

ANA Center for Ethics and Human Rights. (2022). The ethical use of Artificial Intelligence in Nursing Practice. https://www.nursingworld.org/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved-12_20_22.pdf 

Chustecki, M. (2024). Benefits and Risks of AI in Health Care: Narrative Review. Interactive Journal of Medical Research, 13(e53616). https://doi.org/10.2196/53616 

Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2(2), 100–117. https://pmc.ncbi.nlm.nih.gov/articles/PMC8590973/ 

Prep Iowa. (2022). TelePrEP. prepiowa. https://www.prepiowa.org/teleprep 

Rural Health Information Hub. (2025). Rural project summary: TelePrEP - Rural Health Information Hub. Rural Project Summary: TelePrEP - Rural Health Information Hub. https://www.ruralhealthinfo.org/project-examples/1029 

Rural Health Information Hub. (2025a). Telehealth models for increasing access to pharmacy services. RHIhub Toolkit. https://www.ruralhealthinfo.org/toolkits/telehealth/2/care-delivery/pharmacy-services 

Steerling, E., Siira, E., Nilsen, P., Svedberg, P., & Nygren, J. (2023). Implementing AI in healthcare-the Relevance of trust: a Scoping Review. Frontiers in Health Services, 3, 1211150. https://doi.org/10.3389/frhs.2023.1211150 

Stryker, C., & Kavlakoglu, E. (2024). What is artificial intelligence (AI)? IBM. https://www.ibm.com/think/topics/artificial-intelligence 

Svedberg, P., Reed, J., Nilsen, P., Barlow, J., Macrae, C., & Nygren, J. (2022). Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program. JMIR Research Protocols, 11(3), e34920. https://doi.org/10.2196/34920 

Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data Privacy in healthcare: in the Era of Artificial Intelligence. Indian Dermatology Online Journal, 14(6), 788–792. https://doi.org/10.4103/idoj.idoj_543_23