Peer Response – Artificial Intelligence (AI) in Healthcare

Peer Response – Artificial Intelligence (AI) in Healthcare

Responding to Aldo Gonzalez

Hello

This is a great post. Your comprehensive analysis of AI applications like BlueLoop and Nuance Dragon Medical One in healthcare accurately outlines the benefits these technologies offer in managing chronic diseases and improving administrative efficiency. However, paying attention to the ethical issues concerning these innovations is critical. While AI systems successfully process personal health data and make vital decisions, patient consent and decision-making questions become relevant. For instance, patients may not receive information on how data collected about them is being used or the logic behind the decisions made by AI systems and, therefore, may develop a negative perception of the same systems (Sanjana Singareddy et al., 2023).

Moreover, while AI could significantly boost productivity in charting and diagnosis, we need to ensure that the very essence of human processes is not negated by the AI tool. Introducing empathy into the discussion, taking into account the specifics of each patient’s case, is critical because, all too often, decisions in the healthcare field require heart and conscience (Davenport & Kalakota, 2019). Another primary concern is the access and distribution of AI technologies in the delivery of healthcare services. This could increase the existing disparities in health since applying these advancements requires high costs and infrastructure, which can only be afforded by those who are financially able (Mennella et al., 2024). This means that the development of AI implanted in the healthcare system will potentially lead to a two-tiered system where better access to such enhanced care will be granted to people with good financials.

In light of these challenges, integrating AI into healthcare demands a proactive approach to ethical considerations. We must contemplate how these technologies impact patient relationships and patients’ trust in healthcare systems. In light of these concerns, should we advocate for an ethical framework prioritizing patient autonomy, consent, and equitable access in deploying AI technologies in healthcare? Could such a framework help balance the scales, ensuring AI is a tool for enhancement rather than exclusion in patient care? Your insights would be valuable in furthering this discussion.

References

Davenport, T., & Kalakota, R. (2019). The Potential for Artificial Intelligence in Healthcare. Future Healthcare Journal, 6(2), 94–98. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/

Mennella, C., Maniscalco, U., Pietro, G. D., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), e26297–e26297. https://doi.org/10.1016/j.heliyon.2024.e26297

Sanjana Singareddy, Vijay Prabhu SN, Jaramillo, A., Yasir, M., N. Gopalakrishna Iyer, Hussein, S., & Tuheen Sankar Nath. (2023). Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus, 15(9). https://doi.org/10.7759/cureus.46066

Responding to Devon Brush

Hello,

Your post provides a compelling overview of how AI-driven tools like IBM Watson transform diabetes management by leveraging predictive algorithms and decision-support systems. These innovations would undoubtedly improve the effectiveness of healthcare service delivery by leveraging electronic health records and patient-self-generated data (Guan et al., 2023). However, the very use of applications based on artificial intelligence in medicine has its advantages in patient care, with some discreet but still complex problems in terms of data protection.

Integration of large patent datasets into the AI system implies a very strict observation of the security and privacy standards that should be exercised to guarantee data privacy from violations. As a result of the heightened risk of data loss in the health sector, the efficiency of AI programs depends on protecting patients’ information (Seh et al., 2020). This aspect of implementing artificial intelligence is critical since once people realize their data has been compromised, it is easy to lose their trust and stall such technological advances, as seen with patients delaying the adoption of such healthcare technologies.

Also, what you have discussed under the topic ‘Algorithmic Bias’ requires further research and analysis. AI systems are trained on input data, and any sort of biases are incorporated into the model itself, which becomes a carrier of existing biases. This can create inequality in healthcare treatments, especially for minorities (Belenguer, 2022). It is crucial to tackle these biases in order to provide health services fairly and to avoid using AI to worsen existing inequalities.

Based on these points, do you think stricter regulations on AI use in the healthcare sector are useful, given that there is a high probability of leaking patients’ data and containing biases? In addition, there is always the question of how the positive effects of the application of AI tools may be balanced or the measures taken to ensure that the benefits from its application will be made available and accented to all the segments of the population to close the gap in the health care system. I was wondering whether you had more thoughts about the management of technology aspect and the issues with ethics.

References

Belenguer, L. (2022). AI bias: exploring discriminatory algorithmic decision-making models and applying possible machine-centric solutions adapted from the pharmaceutical industry. AI and Ethics, 2(2). https://doi.org/10.1007/s43681-022-00138-8

Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liú, D., Yu, S., Wang, Z., Jia, S., Hou, X., Yang, X., Jia, W., & Sheng, B. (2023). Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine, 4(10), 101213–101213. https://doi.org/10.1016/j.xcrm.2023.101213

Seh, A. H., Zarour, M., Alenezi, M., Sarkar, A. K., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Healthcare data breaches: Insights and implications. Healthcare, 8(2), 133. NCBI. https://doi.org/10.3390/healthcare8020133

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PEER RESPONSE 1

Aldo Gonzalez posted

Artificial intelligence (AI) is becoming an essential tool in healthcare, particularly in managing chronic conditions like diabetes and improving the efficiency of administrative tasks such as charting. One example is BlueLoop, an AI platform that assists in diabetes management by monitoring glucose levels, predicting trends, and suggesting personalized insulin dosages. Beyond its patient-facing capabilities, AI can also support healthcare providers with administrative duties, including charting and documentation.

Peer Response – Artificial Intelligence (AI) in Healthcare

For healthcare providers, AI-powered tools like Suki and Nuance Dragon Medical One assist with clinical documentation by using natural language processing (NLP) to capture and organize patient interactions. These systems transcribe conversations during patient visits, auto-generate clinical notes, and integrate directly with electronic health records (EHRs). In managing chronic illnesses like diabetes, AI charting tools can input critical data such as blood glucose readings and medication changes, reducing the time providers spend on manual documentation. This helps free up more time for patient care and improves workflow efficiency. These tools also reduce the risk of errors in charting, contributing to more accurate and comprehensive patient records.

AI is also making significant strides in radiology. For instance, AI algorithms such as those employed by Aidoc are being used to help radiologists analyze medical images for early detection of conditions like lung cancer or cardiovascular disease. In chronic illness management, AI tools can assist radiologists in identifying subtle changes in imaging that may indicate disease progression or response to treatment. By rapidly analyzing large sets of imaging data, AI tools help reduce the workload on radiologists, improve diagnostic accuracy, and shorten the time to treatment.

However, while AI offers numerous advantages, it also comes with drawbacks. AI charting tools and radiology platforms can be expensive, with ongoing subscription fees or integration costs. Moreover, the accuracy of AI depends on the quality of data, and there’s a risk of misinterpretation if the system lacks sufficient or correct input. Privacy and security are other concerns, as AI systems must comply with regulations such as HIPAA to ensure patient data is protected. Lastly, over-reliance on AI could potentially reduce the critical human oversight needed in both documentation and diagnosis, especially when nuanced clinical judgment is required.

PEER RESPONSE 2:

Devon Brush posted

Artificial intelligence (AI) is transforming healthcare by enhancing the ability to diagnose, monitor, and treat chronic illnesses. One notable application is in managing diabetes mellitus, a chronic condition requiring continuous monitoring and care. AI-based tools, such as predictive algorithms and decision-support systems, can assist healthcare providers in managing diabetes by analyzing patient data, such as glucose levels, diet, and physical activity, to predict and prevent complications. AI applications, like IBM Watson for diabetes management, assist both healthcare providers and patients by integrating multiple data sources, including electronic health records (EHRs) and wearable devices, to offer real-time insights for better decision-making (Alowais et al., 2023).

This AI system is primarily designed for healthcare providers but has features tailored for patients. For example, patients can use AI-driven apps to increase self-efficacy by actively tracking their glucose levels, receiving personalized diet recommendations, and monitoring their daily physical activity (Bajwa et al., 2021). By engaging in these self-management practices, patients are empowered to take a more proactive role in managing their health, thereby improving outcomes and reducing the burden of their condition.

Despite its benefits, there are some risks associated with AI use in chronic illness management. One major drawback is the potential for algorithmic bias, which can arise from data that does not adequately represent diverse patient populations (Bajwa et al., 2021). Additionally, there is concern over the high cost of implementing AI technologies, which may limit access for certain patient groups, leading to disparities in care (Alowais et al., 2023). Therefore, while AI holds promise for improving chronic illness management, careful consideration of these risks and costs is crucial for equitable healthcare delivery.