Analyzing a Current Healthcare Problem or Issue/Annotated Bibliography and Kaltura Video

The complexity of the healthcare sector is due to the increase in chronic diseases, and the speedy changes that technology has brought in the field make the use of artificial intelligence technology in the healthcare domain necessary to take into consideration when researching population health. Artificial Intelligence technologies are already being used in healthcare to give healthcare professionals an opportunity to diagnose diseases, offer treatments, and a way to manage patients. Although the use of AI technologies in this manner has significant benefits, there are several inaccuracies and ethical concerns regarding the accuracy of the outcomes delivered, clinicians’ acceptance of outcomes delivered, and potential biases that can arise in making decisions (Olawade et al., 2024). The misuse of AI tools and misunderstanding of the information generated by clinicians can have adverse implications for patients’ health statuses.

AI in Clinical Decision-Making: A Growing Healthcare Priority

With the increasing disease burden of chronic conditions, limited workforce in the healthcare industry, and complex health systems, artificial intelligence for clinical decision-making is emerging as a major hurdle in today’s healthcare landscape. AI technology is used to assist health practitioners in diagnosis, identification of health risks, health recommendations, and improving healthcare processes. In recent times, studies indicate that the AI healthcare market has crossed the $20 billion mark and is set to grow at an accelerated pace owing to increased reliance on digital tools by healthcare facilities (Ali et al., 2023). Furthermore, studies suggest that machine learning models have the potential to improve diagnostic precision in certain medical fields, and in testing procedures, some devices have successfully been tested with a success rate exceeding 90 percent ( Zhang et al., 2026). Still, issues such as the reliability of suggestions provided by machines, the misuse of technology, lack of acceptance by health practitioners, and discrimination against certain groups of patients are big challenges. Inadequate AI system design and oversight can lead to inaccuracies, biased treatment decisions, and compromised patient safety.

Manipulation of synthetic intelligence technologies in healthcare facilities has a huge influence on the financial and operational features of healthcare organizations. The problem of medical errors and their economic implications is still topical today. Medical errors are costly, billions of dollars a year, and so are ways to improve patient care safety and reduce unnecessary expenses. AI technologies can address this issue by enabling healthcare practitioners to identify diseases earlier, make better decisions, and predict any issues before they arise. With the advent of EHRs, there is sufficient data to be able to use algorithms in hospitals. Although AI applications are effective at facilitating the workflow and reducing expenses for hospital management, their wrong usage can have negative outcomes such as incorrect predictions and treatment recommendations (Huang et al., 2025). Therefore, the use of AI should be closely monitored by health professionals who will assess the usefulness and suitability of the predictions made by the algorithm. It is crucial to note that AI tools should be used as a tool and never as a replacement for the practitioner’s experience. The regular evaluation of the performance of algorithms is needed to ensure fairness.

There is a need for healthcare professionals, such as nurses, to be responsible in the implementation and application of AI technology. Due to direct interaction with patients, nurses should be aware of what impact AI has on healthcare decision-making and on the patient. Communication is crucial for preserving the level of trust between the patient and the nurse when using such tools as AI technology. While the benefits of using AI technologies in healthcare settings are clear, challenges in the transparency, responsibility, and accountability of AI tool usage in healthcare facilities remain (El-Banna et al., 2025). Thus, it is crucial to educate clinicians to learn how to use AI technologies.

Process for Selecting Peer-Reviewed Articles

To ensure that the information retrieved and used in the paper is relevant to the problem under study, which is to make clinical decisions using artificial intelligence, scholarly peer-reviewed articles were searched using credible databases, such as PubMed, ScienceDirect, and CINAHL. Some specific keywords were employed while conducting a thorough literature search, such as artificial intelligence and health care, clinical decision support, machine learning in medicine, algorithmic bias, ethical problems in AI, provider trust, and digital health technologies. In order to make sure that the literature reviewed is contemporary and relevant to the advancements in the field, peer-reviewed sources no older than five years were chosen.

The selection of the sources was based on the following criteria: types of literature including articles that focused on the AI technology related to diagnosis and treatment, use of AI technology by health care providers, ethical issues related to the AI technology, and approaches that could help in deploying the AI technology in health care service delivery (Olawade et al., 2024). In this study, review articles, research articles without any kind of empirical data, research articles that are completely irrelevant to the topic of clinical decision-making, and computer science research articles that focus on technological development and not on clinical decision-making were excluded. Lastly, three academic research articles were chosen that were relevant to the topic, including the ethical issues, algorithmic biases, impact of AI on clinical decisions, and health care providers’ experience with AI technology.

Evaluation of Selected Sources on AI in Clinical Decision-Making

The literature review sources selected are scholarly articles found in peer-reviewed academic journals specializing in healthcare technology, informatics, ethics, and practice. The credibility and accuracy of the results presented in the selected articles have been obtained through the use of such methods as cohort studies, cross-sectional studies, and qualitative studies with healthcare professionals who use artificial intelligence technologies in their professional practice. These methods have been employed in the medical field to examine various issues associated with the use of AI technologies, such as their impact on patient health outcomes, ethical considerations, algorithmic bias, and healthcare providers’ trust in AI technologies (Olawade et al., 2024). The need for the use of relatively recent research arose because the literature review would have to provide the current trends that go with AI application in healthcare practice.

Some articles were those that involved only the healthcare organizations and were opinion-based and experience-based, but the methodology was presented well, data were analyzed well, and the results obtained were felt to be valid and important for practice. The papers each yielded different insights, but they were all related to general concerns of the use of AI in clinical decision-making. In this regard, one paper focused on transparency and clinicians’ trust as a driver of secure and successful use of AI tools for decision-making solutions. The responses indicated that AI adoption is more secure and beneficial when healthcare professionals are aware of its capabilities and adopt AI suggestions (Olawade et al., 2024). The following article discussed the ethical concerns about the use of machine learning algorithms, some of which may be biased. According to the author, continuous supervision and control of AI usage are necessary to minimize inequality in healthcare and promote ethical decisions in clinical practice (Amiri et al., 2024). Last but not least, one paper talked about healthcare professionals’ experience in using AI for diagnosis and treatment planning. In summary, the results of these studies can inform the development of intervention strategies to encourage responsible use of AI technologies in healthcare to support patients, patient safety, and ethical practice in healthcare.

Annotated Bibliography

Amiri, H., Peiravi, S., Sara, Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Anar, M. A. (2024). Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: A systematic review and meta-analysis. BioMed Central (BMC) Medical Education,24(1), 45–80. https://doi.org/10.1186/s12909-024-05406-1

The findings from the various studies indicate that the integration and implementation of AI into clinical practice rely on the skills, competence, and confidence of health practitioners, who apply AI within the context of their work. Health practitioners need sufficient education and training so that they can fully understand the pros and cons of AI solutions and be able to understand the AI recommendation and make a safe decision about the clinical case. The evidence suggests that skilled health practitioners are safe, correct, and effective in their use of AI technologies, selecting the appropriate intervention and preventing errors. This means that for AI to thrive in the healthcare sector, collaboration between IT experts generating the AI solutions and health professionals and organizational managers aiming to implement these technologies efficiently in clinical practice will be crucial. Cooperation will enable the improvement of the effectiveness of the clinical workflow, reduce decision-making errors, and enable the safe and proper use of technologies within the healthcare environment. In the meantime, issues of ethics, such as the removal of algorithmic bias, equality in access to AI-aided care, accountability and transparency in the decision-making process should be addressed.

Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., Gillan, C., Haghzare, S., Jackson, E., Lalani, N., Mattson, J., Peteanu, W., Tripp, T., Waldorf, J., Williams, S., & Tavares, W. (2021). Artificial intelligence education programs for health care professionals: Scoping review. JMIR Medical Education, 7(4), e31043. https://doi.org/10.2196/31043

Another one of the chosen studies focused on healthcare professionals’ attitudes regarding the integration of artificial intelligence into their clinical decision-making processes. Specifically, the article highlighted challenges faced by medical practitioners in interpreting and implementing AI recommendations within the scope of their work. According to the study’s results, healthcare providers often struggled to interpret or implement recommendations provided by artificial intelligence due to vague, confusing, or unworkable suggestions. Researchers identified various aspects that affect the acceptance of AI recommendations among clinicians, including their technological background, attitude towards technology in general, confidence in decision-making, and trust in health organizations. It is important to note that interdisciplinary communication is vital for improving medical practitioners’ understanding and implementation of AI tools. Furthermore, AI suggestions are more likely to be accepted by medical professionals if they are transparent, clear, and meet the needs of clinicians. This research underscores the necessity to introduce appropriate educational programs aimed at teaching medical professionals, such as nurses, about artificial intelligence.

El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025). AI literacy and competency in nursing education: Preparing students and faculty members for an AI-enabled future-a systematic review and meta-analysis. Frontiers in Medicine, 12(8), 67–90. https://doi.org/10.3389/fmed.2025.1681784

One of the selected studies was about the perceptions of healthcare professionals toward the use of artificial intelligence in clinical decision-making. In particular, the article points to difficulties in understanding and applying AI recommendations in the context of healthcare practitioners’ duties. The study results showed that healthcare providers faced challenges in interpreting and implementing the recommendations given by AI because they received vague, confusing, and unworkable suggestions. The researchers found several elements that influence uptake of AI recommendations by clinicians, including their technology background, general attitude to technology, confidence in making decisions, and trust in health organizations. To emphasize, interdisciplinary communication plays a crucial role in enhancing medical practitioners’ knowledge and utilization of AI tools. Also, AI recommendations will be more likely to be embraced by healthcare professionals, provided they are clear, understandable, and relevant to the needs of the healthcare provider. The research implied that healthcare professionals should be active participants in the decision-making process to promote ethical application of technologies and prevent overreliance on the computer. The research review of the above-mentioned research helped to build my academic abilities and develop the ability to integrate the research results into practice and future academic endeavors, including the MSN Capstone project. Moreover, the literature review revealed several research gaps that were identified in the existing body of knowledge, such as the long-term impact of AI-based decision support systems, strategies aimed at the reduction of algorithmic bias, AI training for physicians, and consequences of using artificial intelligence in health disparities and patient safety issues.

Conclusion

Artificial intelligence has become more commonly used in making medical decisions to assist health workers in making diagnoses for patients and choosing suitable treatment plans for them. While AI has numerous benefits, there are some challenges to be overcome in the transition to AI tools in the clinical workflow. To embrace AI in healthcare effectively, it is essential to dedicate resources to training and educating stakeholders about how AI functions. There would also be a need for inter-sectoral collaboration amongst different health sectors.