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AI's role in the healthcare industry is by enhancing the ways in which medical professionals diagnose, treat, and manage diseases. AI technologies can process medical data, learn from patterns, and offer insights in a way that way surpasses the capabilities of any human. From improving diagnostic accuracy to personalizing treatment plans, AI as of late is becoming an integral part of healthcare systems around the world.

One of the most important contributions of AI in healthcare is its ability to assist in medical diagnostics. Machine learning algorithms are now capable of analyzing medical images, such as X-rays, MRIs, and CT scans, with remarkable accuracy. These systems can detect early signs of diseases such as cancer; they often identify conditions before they become visible to the human eye. This can lead to earlier interventions and better patient outcomes.

Additionally, AI is also making strides in predicting patient health outcomes. By analyzing a wide ranges of data from a patient’s medical history to lifestyle choices. For example, AI can forecast the likelihood of developing chronic conditions like heart disease or diabetes from a person's genetic history or from their dieting choices. These predictive capabilities help doctors provide more proactive care and tailor treatment plans to individual patients, which ultimately improves patient quality of life.

Another prominent role of AI in healthcare is that it has the potential to revolutionize the way new treatments are developed. For example, have AI to model complex biological interactions, researchers can more efficiently identify promising drug candidates, significantly reducing the time and cost of bringing new medications to market and patients.

I chose AI in the industry of healthcare because of its already showing potential to revolutionize the industry. AI can streamline processes, improve diagnoses, and personalize treatment, making healthcare more efficient and effective. My interest in this topic comes from the opportunity to contribute to innovations that can improve patient outcomes and make healthcare more accessible to all classes. The idea of using AI to solve real world health challenges moves me hence why I chose it.

In this padlet wall, I will explore three examples of AI in healthcare: diagnostic imaging, personalized medicine, and patient monitoring. I will then discuss future trends and ethical considerations, and then social impact of AI in healthcare. Finally, I will provide an overview video summarizing these topics, followed by a reflection and references.

The healthcare industry and AI applications within it are significant to me because of the effect it has on improving people's lives. Healthcare has a direct impact on people's well-being, and the opportunity to contribute to AI that enhances patient care is significant. AI, in particular, excites me because it has the power to transform diagnostics, personalize treatments, and optimize healthcare systems, making them more efficient and accessible. The ability of AI to analyze data and uncover patterns that humans might miss can lead to milestones. In the creation of this padlet, I learned about the changes AI has and will have in healthcare.

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AI in diagnostic imaging utilizes technologies like machine learning, computer vision, and deep learning to analyze medical images such as X-rays, MRIs, and CT scans. The technologies used such as machine learning models are trained to detect anomalies like tumors, often with greater accuracy and speed than human radiologists. Benefits of AI includes improved diagnostic accuracy, faster image analysis, and better patient outcomes. AI can help prioritize urgent cases and reduce human error. However, challenges and limitations include the need for large, high-quality datasets for training, the potential for biases, and concerns over data privacy and the integration of AI into existing healthcare systems.

AI in diagnostic imaging is a interesting and impactful field because of its potential to revolutionize healthcare. Its potential is to use machine learning and computer vision in medical imaging to uncover details in images that may go unnoticed by humans, especially in the initial stages of diseases like cancer. This capability could significantly improve the speed and accuracy of diagnoses, leading to earlier treatments and better patient outcomes. By streamlining the diagnostic process, AI has the potential to enhance healthcare efficiency and ensure more timely interventions, ultimately benefiting both patients and healthcare providers. While AI holds incredible potential, it should be used as a tool to add to human expertise, not as a substitute.

Here's a link to a video about AI in Diagnostic Imaging---https://youtu.be/76LqIY7uL2w?feature=shared

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AI in personalized medicine uses technologies like machine learning and natural language processing to tailor treatments to patients based on factors such as their genetic makeup, lifestyle, and medical history. Machine learning algorithms analyze datasets to predict the most effective treatment options, optimizing care for each person. Benefits include more precise treatments, reduced side effects, and improved outcomes by considering each patient's unique characteristics. However, challenges will always include data privacy concerns, the need for large and diverse datasets, and the difficulty of integrating AI-driven insights into traditional healthcare practices. Additionally, there is the risk of algorithmic bias affecting treatment reccomendation.

AI in personalized medicine is interesting because it promises a reality where treatments can be tailored to each patient, rather than the current a one-size-fits-all approach. I find it particularly fascinating how machine learning can analyze complex datasets, including genetic information, to predict the best treatment strategies. This has the potential to significantly improve treatment outcomes while minimizing side effects. The impact could lead to more effective and individualized healthcare. Overall, I believe this technology could revolutionize healthcare, but it will require careful regulation and collaboration between AI developers and medical professionals to build trust.

Here's a link to a video about AI in personalized medicine--https://youtu.be/TfkHrvct1hg?feature=shared

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AI in patient monitoring involves the use of technology to track patient health in real-time, identifying critical changes before they become severe. Edge computing such as bedside monitors, processes data directly at the source, reducing latency and enabling quicker responses to health changes. Natural language processing helps interpret health professionals notes for comprehensive care insights, while computer vision can monitor patient movements. AI enhances early detection, personalized care, and efficiency, leading to better patient health. On the other hand, AI will continue to raise privacy concerns, especially as it evolves and integrates into healthcare. The other worry of AI in healthcare, and the previous example would be the costly investment.

AI in patient monitoring is interesting and shows great potential since it uses technology such as wearable devices using edge computing allow real-time data processing, this in turn can be the difference that saves a life. While AI offers significant benefits, the concerns about security need to be dealt with before its implementation.

Here's a link to a video about AI in patient montiering--https://youtu.be/IfbhEwUK-iE?feature=shared

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The future trends and potential developments of AI in healthcare is the use of predictive analytics, where AI will increasingly help in early diagnosis and risk prediction. By analyzing patient data, AI could predict health complications, enabling timely interventions, especially in chronic disease management. For example, the growth of AI in personalized medicine, analyzes genetic and environmental factors, that will further revolutionize healthcare by offering individualized treatment plans tailored to specific patient needs. There's AI-powered diagnostic tools, which uses technologies like computer vision and natural language processing. They will become more accurate and efficient, detecting diseases earlier and more precisely in the future. This will significantly enhance diagnostic accuracy in areas such as radiology, dermatology, and cardiology. Additionally, AI's ability to process and interpret unstructured clinical data will improve decision-making and care coordination.

However, as AI advances in healthcare, ethical considerations must be carefully addressed. Bias in algorithms is a major concern; if AI systems are trained on biased data, they could prolong health disparities and lead to unequal care. As previously stated, data privacy is another critical issue, as healthcare data is sensitive and vulnerable to breaches. Ensuring transparency in AI’s decision-making process is also essential for maintaining trust in healthcare systems.

Personally, I find AI’s potential to transform healthcare interesting, but I'm also concerned about these ethical issues. Ensuring AI systems are fair, transparent, and secure will be crucial to achieving positive outcomes without reinforcing existing disparities or compromising patient privacy. Ethical frameworks must evolve alongside technological advancements.

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The broader societal impact of AI in healthcare is profound, touching on areas such as employment, privacy, equity, and access to care. On the side of employment, while AI has the potential to improve efficiency, it also raises concerns about job displacement. Roles in areas like radiology, diagnostics, and administrative work may be automated, leading to the need for reskilling and adaptation in this workforce.

Privacy is another critical issue. AI systems in healthcare rely on large amounts of patient data, which increases the risk of hacking or misuse. Protecting sensitive health information through robust encryption and strict regulations is essential to maintaining public trust. Additionally, transparency in how AI systems use and share data is crucial.

AI also has the potential to impact healthcare equity. While AI could enhance access to high quality care in underserved areas, there’s a risk that the technology could worsen existing disparities if it's not implemented thoughtfully. For example, AI models may perform poorly for underrepresented groups if the data used to train them is biased. Ensuring diversity in both data and the development process is essential to erase these risks and ensure AI benefits everyone equally.

Personally, these societal influence my perception as AI improves healthcare outcomes. On the other hand, these societal issues weigh heavily on my perception as well. Balancing innovation with ethical considerations is crucial to ensuring that AI serves society fairly, inclusively, and transparently. Addressing these challenges will ultimately determine whether AI in healthcare can be achieved to its full potential.

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“Revolutionizing healthcare: the role of artificial intelligence in clinical practice - BMC Medical Education.” BMC Medical Education, 22 September 2023, https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-023-04698-z. Accessed 12 March 2025.

Author links open overlay panelMohamed Khalifa a b c, et al. “AI in Diagnostic Imaging: Revolutionising Accuracy and Efficiency.” Computer Methods and Programs in Biomedicine Update, Elsevier, 5 Mar. 2024, www.sciencedirect.com/science/article/pii/S2666990024000132.

Johnson, Kevin B, et al. “Precision Medicine, AI, and the Future of Personalized Health Care.” Clinical and Translational Science, U.S. National Library of Medicine, Jan. 2021, pmc.ncbi.nlm.nih.gov/articles/PMC7877825/#:~:text=The%20convergence%20of%20artificial%20intelligence,decision%20making%20through%20augmented%20intelligence.

“AI in Remote Patient Monitoring: The Top 4 Use Cases in 2024.” HealthSnap, Inc., 11 Mar. 2024, healthsnap.io/ai-in-remote-patient-monitoring-the-top-4-use-cases-in-2024/.

“How Ai Is Transforming the Future of Healthcare.” How AI Is Transforming The Future Of Healthcare, www.internationalsos.com/magazine/how-ai-is-transforming-the-future-of-healthcare. Accessed 13 Mar. 2025.

Hitrust. “The Ethics of AI in Healthcare.” Hitrust, HITRUST, 11 July 2024, hitrustalliance.net/blog/the-ethics-of-ai-in-healthcare.

Dane.schultz. “The Impact of AI on the Healthcare Workforce: Balancing Opportunities and Challenges.” HIMSS, HIMSS, 8 May 2024, gkc.himss.org/resources/impact-ai-healthcare-workforce-balancing-opportunities-and-challenges#:~:text=Further%2C%20issues%20to%20be%20considered,of%20AI%20into%20healthcare%20delivery.

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Through my research on AI in healthcare, I’ve learned and gained a deeper understanding of how transformative this AI technology can be in diagnostic imaging, personalized treatments, and patient care. A challenge I faced was navigating the complexities of ethical considerations, particularly around privacy and bias. To overcome this, I focused on finding balanced perspectives from both technical and ethical point of views. The research reinforced my belief that AI has the potential to revolutionize healthcare, but it also highlighted the importance of addressing societal issues such as equity and data security. This has broadened my perspective, making me more cautious about the rapid adoption of AI without proper safeguards.