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This Grand Rounds presentation is going to cover many topics pertaining to Artificial Intelligence and Telehealth within the healthcare community. Artificial Intelligence is specific technology that can learn, comprehend, problem solve, and make decisions, just like the human brain (Stryker & Kavlakoglu, 2024). AI has been growing for decades, especially in healthcare.

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Challenges

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).

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Disparities and Inequities/Barriers

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).

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Would you trust AI over your health care provider?

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How Generative AI is Transforming Healthcare

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According to the Rural Health Information Hub Telehealth technology has helped rural communities gain access to pharmacy services in areas of the United States that have previously been unable to obtain licensed pharmacists or pharmacy services (2025).  This allows for more availability of pharmacy services and consultations for nursing staff in rural inpatient and outpatient settings that may not have been previously available.

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

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States such as Iowa have now integrated free state-funded telehealth programs for specialized drugs for HIV prophylaxis and treatment to serve at risk populations and rural locations within the state. PrepIowa.org allows telehealth access to readily available pharmacists to help patients gain access to local testing options and treatment prescriptions (2025).

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.

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Iowa TelePrEP Program (Iowa Department of Public Health)

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The American Nurses Association has recently released a position statement titled, “The Ethical Use of Artificial Intelligence in Nursing Practice” to articulate nursing standpoints on how to approach the integration of AI into nursing care while maintaining foundational principles of the nurse-patient relationship.  The American Nurses Association believes nurses involved in the design and development of AI must strive to integrate quality and transparent data (2022). Evidence-based data that is also transparent supports artificial intelligence to perform in a way that allows for traceability, reliability, and accountability.   Through traceability and reliability of data, there will be opportunities for improvement within the technology while maintaining trusting relationships between healthcare provider groups and patients.  This also continues as a reminder to lean on the knowledge and evidence-based practice curated and procured by nursing staff and providers so that patients do not interpret technology as absolute fact.

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Creating a culture that adopts justice, fairness, and equity will be vital to preventing AI technology from creating biases within patient populations, according to the American Nurses Association (2022). Examples of how nurses will create this culture include continuing practices that identify disparities in AI programming.  Nurses will need to engage in work that promotes diversity, equity, and inclusion when incorporating artificial intelligence with various patient populations (2022).

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Protecting patient data will continue to be a challenge as artificial intelligence continues to work with data mining software to lure people to participate in healthcare-related social media and health promotion applications.  Educating patients on what platforms they share patient information on will continue to help combat exposure of patient information and retain privacy.  Finally, incorporating Nursing informaticists to engage in system design that will protect patient information according to the American Nurses Association (2022).

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Have you ever uploaded personal health information to a healthcare-related platform, such as fitness and nutrition application?

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References

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