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AI has been revolutionary for the healthcare industry in a variety of ways. The provision of healthcare is one of the most, if not the most, critically important pillars of society. It has expanded and improved over centuries, and the advent of AI in healthcare will lead to even better care for everyone. It has the potential to transform many different aspects of medical care, including tools for more accurate predictions of outcomes from drugs and treatments, catching on to early signs of illnesses, and significantly lowering the cost of health screenings.

While this all sounds incredible, it is important to keep in mind that the industry’s adoption of these technologies has been slow in consideration of the litany of risks that come with the usage of AI models. These risks include data privacy concerns, security issues, and bias in models. Before this technology can be truly beneficial for all, developers must undertake the responsibilities of making it fair, transparent, ethical, and secure. However, if there exists possibilities to save more lives with tools that have greater accuracy than the current system, I think it is worth investing the time and research into making tools that are responsible.

In this Padlet, I will delve into three AI applications and technology being researched and tested to showcase just how much potential AI has for saving lives. I will also discuss ethical considerations and societal impact in greater depth, because keeping that in mind is important to understand why industry adoption will not happen right away.

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AI is helping doctors predict if patients with wet age-related macular degeneration (AMD) in one eye will develop it in the other. Using machine learning, researchers analyzed digital eye scans from over 2,500 patients. The AI correctly predicted second-eye wet AMD in 41% of cases, outperforming 5 out of 6 experts who had more information on the patients. This technology enables early diagnosis and intervention significantly faster, preserving vision and improving quality of life. A challenge is integrating AI seamlessly with existing optical devices. I find this exciting because it shows how AI can assist in grouping patients based on their risk for progression of a condition, allowing hospitals to focus resources where they’re most needed and potentially preventing irreversible vision loss.

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AI-enhanced tools like smart stethoscopes use machine learning to detect heart failure earlier and more accurately than current methods. In one study, the AI stethoscope correctly identified 90% of patients with heart failure. It’s a portable, easy-to-use alternative to hospital-based echocardiograms. Another study used AI and blood test data to identify actual heart attacks among A&E patients, improving triage and reducing unnecessary admissions. These tools could revolutionize how GPs diagnose cardiac conditions. I find this impactful because it may be difficult for some patients to get to a hospital for testing, and the smart stethoscope makes it so that your primary care provider can detect it without the significant expenditure. Furthermore, with early detection being so key for heart failure, being able to more frequently get screened for it has the potential to save so many lives. A potential challenge would be to have this smart stethoscope widely available for everyone, everywhere.

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AI is being used to personalize lung cancer treatment by predicting which drug or drug combinations are most effective for a patient based on their tumor’s genetic profile. Using deep learning, the system can analyze biopsy data and provide recommendations within 12 to 48 hours. It outperforms traditional genetic matching and has even identified new potential drug combinations. Although the study is small, it shows that AI could dramatically speed up and improve treatment personalization. I’m fascinated by this approach because it brings hope for faster, more effective care, and potentially even finding new significant drug interactions for new treatment plans. There is still the challenge of having the potential new treatment plans clinically approved, though.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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AI’s role in healthcare is expanding rapidly, with future trends focusing on precision medicine, real-time diagnostics, and integrated decision support systems. Technologies that predict disease progression, like those used in wet AMD, or customize cancer treatments based on tumor genetics, will become increasingly common. AI tools will also likely become more accessible at the point of care, such as smart stethoscopes being used by GPs to detect heart failure, minimizing the need for hospital-based diagnostics.

However, these promising trends also raise important ethical considerations. As AI systems are trained on historical and clinical data, there's a risk of bias. For example, if datasets lack diversity, predictions may be less accurate for underrepresented populations. In personalized medicine, it's crucial to ensure transparency in how AI recommends treatments, so that patients and doctors can trust and understand its guidance. Data privacy is another major issue because AI models rely heavily on sensitive medical data. Ensuring secure storage, informed consent, and responsible use of this data is essential to protect patients’ rights.

Personally, I believe AI will bring healthcare improved speed, accuracy, and personalization for every patient. But this future must be built with trust, fairness, and human oversight. It's exciting to think that tools already helping in early detection of heart disease or vision loss could soon become everyday instruments in clinics. Still, we must ensure AI complements, not replaces, human care, and that ethical safeguards keep pace with innovation. AI tools should exist to help inform and supplement a provider’s decision, not take it over.

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The societal impact of AI in healthcare is already becoming visible through technologies like AI-powered stethoscopes and risk prediction tools for wet AMD that have the potential to reduce strain on healthcare systems, improve early diagnoses, and increase access to care in under-resourced or rural areas. This can lead to better health outcomes and lower long-term costs for healthcare providers by allowing them to allocate resources more effectively and stratify risk for patients. However, as with any technological advancement, AI introduces new ethical challenges. Employment in diagnostic and administrative roles may be affected as AI automates certain tasks. While this may allow providers to focus more on complex care, it could displace some jobs if support systems are not in place. Moreover, privacy and consent issues are critical, especially when dealing with genetic data in personalized cancer treatment. There is also a risk of unequal access, where advanced AI tools may be available in wealthier hospitals or countries but inaccessible elsewhere, potentially widening health disparities.

From my perspective, these societal implications make it clear that AI in healthcare is more than just a technical issue. Its success depends on inclusive design, equitable access, and public trust. I am interested in how AI can assist, not replace, healthcare professionals, offering tools that can prevent vision loss, detect heart failure early, or choose life-saving cancer drugs faster as has been showcased on this wall. These innovations hold incredible promise, but we must guide them with empathy, ethics, and equity at the core.

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Overall, conducting this research on how AI is impacting the healthcare industry motivates me to continue in my studies of AI to push the boundaries of improving outcomes for the average person. Most of the time, the news of what I hear about AI being used maliciously for surveillance or having other harmful consequences makes me question why I wanted to go into the field, but this makes me remember that it can and should be used for good and it is needed to have voices that speak on ethics in the field. A challenge I had when doing this assignment was understanding the medical terms in the research papers I drew from, but having the summary articles made it easier.

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Bachtiger, P., Petri, C. F., Scott, F. E., Ri Park, S., Kelshiker, M. A., Sahemey, H. K., Dumea, B., Alquero, R., Padam, P. S., Hatrick, I. R., Ali, A., Ribeiro, M., Cheung, W.-S., Bual, N., Rana, B., Shun-Shin, M., Kramer, D. B., Fragoyannis, A., Keene, D., … Peters, N. S. (2022). Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled Stethoscope Examination in London, UK: A prospective, observational, multicentre study. The Lancet Digital Health, 4(2). https://doi.org/10.1016/s2589-7500(21)00256-9 [Rationale: This research paper details the smart stethoscope that is capable of screening patients for heart failure.]

Coker, E. A., Stewart, A., Ozer, B., Minchom, A., Pickard, L., Ruddle, R., Carreira, S., Popat, S., O’Brien, M., Raynaud, F., de Bono, J., Al-Lazikani, B., & Banerji, U. (2022). Individualized prediction of drug response and rational combination therapy in NSCLC USING Artificial Intelligence–enabled studies of acute phosphoproteomic changes. Molecular Cancer Therapeutics, 21(6), 1020–1029. https://doi.org/10.1158/1535-7163.mct-21-0442 [Rationale: This research paper details the usage of AI to create customized treatment plans for patients with lung cancer.]

Kwint, J. (2023). Artificial Intelligence: 10 promising interventions for Healthcare. NIHR Evidence. https://doi.org/10.3310/nihrevidence_59502 [Rationale: This research article summarizes many different ways that AI has been used to revolutionize healthcare.]

North, M. (2025, March 14). 6 Ways AI is Transforming Healthcare. World Economic Forum. https://www.weforum.org/stories/2025/03/ai-transforming-global-health/ [Rationale: This article talks about how AI is being used for healthcare, as well as ethical considerations for its use and the impact that it might have on the industry and jobs.]

Ripart, M., Spitzer, H., Williams, L. Z., Walger, L., Chen, A., Napolitano, A., Rossi-Espagnet, C., Foldes, S. T., Hu, W., Mo, J., Likeman, M., Rüber, T., Caligiuri, M. E., Gambardella, A., Guttler, C., Tietze, A., Lenge, M., Guerrini, R., Cohen, N. T., … Whitaker, K. (2025). Detection of epileptogenic focal cortical dysplasia using graph neural networks. JAMA Neurology, 82(4), 397. https://doi.org/10.1001/jamaneurol.2024.5406 [Rationale: This research paper details how computer vision was used on MRI scans to predict progression of wet AMD in patients.]