# data[].attributes.body
I chose the healthcare industry because it directly affects people’s lives, and I am interested in health and helping others stay overall healthy. Healthcare includes hospitals, clinics, public health programs, medical research, and companies that make medicines and medical devices. It is always changing because of new technology, growing populations, and the need for faster/more accurate care. I am personally interested in healthcare because of my studies as i major in health science and my second hand experience coaching youth sports, where I see and hear how health, wellness, and injury prevention affect performance and daily life.

Artificial intelligence is becoming very important in healthcare. AI can help doctors and nurses make better decisions by looking at large amounts of patient data, predicting health risks, reading medical scans, and suggesting treatment plans. AI can also help hospitals run more smoothly by improving scheduling, managing supplies, and supporting services that allow patients to get care from home. This is important to me because AI shows how technology can work with humans to make care faster, safer, and more effective.

On my Padlet wall, I will share examples of AI in patient care, AI in hospital operations, how AI predicts public health risks, and the ethical issues around privacy and fairness. I will also include my thoughts on how AI could help student-athletes stay healthy and prevent injuries. Learning about AI in healthcare matters to me because it shows a future where care is smarter, easier to access, and better for everyone.

# data[].attributes.headline
Healthcare- AI assisted medical imaging for detecting diseases

# data[].attributes.subject
Healthcare- AI assisted medical imaging for detecting diseases

# data[].attributes.body
One specific example of AI in healthcare is AI assisted medical imaging for detecting diseases like cancer. This technology uses machine learning and computer vision to analyze X-rays, CT scans, and MRIs, identifying abnormalities such as tumors or fractures that might be missed by human eyes. By providing potential problem areas, AI helps radiologists make faster and more accurate diagnoses, leading to earlier treatment and better patient outcomes.

The benefits include improved diagnostic accuracy, reduced workload for doctors, and faster results for patients. However, challenges remain, such as the need for large, high-quality datasets to train AI models, potential biases in data, and reliance on technology that may sometimes produce false positives or negatives.

I find this example fascinating because it combines technology with direct patient care. Its potential impact is huge, as earlier and more accurate diagnoses can save lives. I believe AI will continue to transform how healthcare professionals detect and treat diseases.

<iframe width="560" height="315" src="https://www.youtube.com/embed/76LqIY7uL2w?si=y1HE6lhUZyCferaB" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

# data[].attributes.wish_content.attachment_props.alternative_text
AI in healthcare is AI-assisted medical imaging for detecting diseases

# data[].attributes.body
A third example of AI in healthcare is AI-driven robotic surgery. This technology uses machine learning, computer vision, and precise robotic instruments to assist surgeons during complex procedures. The AI system can analyze a patient’s anatomy in real time, guide surgical instruments, and even suggest optimal movements to improve accuracy and reduce human error.

The benefits of AI in robotic surgery include smaller incisions, less blood loss, faster recovery times, and improved precision in delicate procedures. It allows surgeons to perform complex operations with higher confidence and consistency. However, challenges include high costs, the need for specialized training, and reliance on technology that may fail or require human backup in emergencies.

I find AI-driven surgery fascinating because it combines cutting-edge technology with hands-on medicine. Its potential impact is huge, as it could make surgeries safer and more efficient. I believe this technology represents a major step toward the future of precision healthcare.

<iframe width="560" height="315" src="https://www.youtube.com/embed/Ois-SyXeTAU?si=Eq4Ej2oJqDjxUAtq" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

# data[].attributes.body
AI is changing not just healthcare, but society as a whole. In healthcare, AI affects jobs, privacy, fairness, and access to care. Some healthcare jobs, like diagnostics, may be automated, which can reduce the need for human labor. At the same time, AI can create new roles, such as AI system managers, data analysts, and technology specialists, shifting the type of skills needed in healthcare.

Privacy is a big concern because AI systems rely on large amounts of patient data. Sensitive information, like medical history and genetic information, must be protected to prevent misuse or data breaches. Equity is also an issue. If AI is trained on limited or biased data, it may provide better care for some populations while disadvantaging others. Access to AI tools may also be unequal, with wealthy hospitals or urban areas benefiting more than rural or underserved communities.

Personally, I see AI in healthcare as very promising because it can make care faster, more accurate, and more personalized. However, I am concerned about the ethical and societal impacts. It is important to address privacy, fairness, and access to ensure that AI benefits everyone. These societal issues make me believe that healthcare AI should be carefully managed with strong rules, education, and oversight, so it improves healthcare without creating new problems.

# data[].attributes.headline
Healthcare- predictive analytics for patient risk management

# data[].attributes.subject
Healthcare- predictive analytics for patient risk management

# data[].attributes.body
A second example of AI in healthcare is predictive analytics for patient risk management. This application uses machine learning and data analytics to examine large amounts of patient data, including medical history, lab results, lifestyle habits, and genetic information. AI models can predict which patients are at higher risk for conditions such as heart disease, diabetes, or hospital readmission.

The benefits of this technology include early intervention, personalized care plans, and improved patient outcomes. Hospitals can focus resources on high-risk patients, reducing complications and healthcare costs. However, challenges include the need for accurate and complete data, concerns about patient privacy, and the risk of bias if the training data is not diverse.

I find predictive analytics particularly interesting because it can prevent serious health problems before they occur. Its potential impact is significant, as it shifts healthcare from reactive treatment to proactive care. I believe AI in this area can save lives and make healthcare more efficient and personalized.

<iframe width="560" height="315" src="https://www.youtube.com/embed/-e1HQQWH0yM?si=PMxbin_lqmB4eoVM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

# data[].attributes.wish_content.attachment_props.alternative_text
Healthcare- predictive analytics for patient risk management

# data[].attributes.headline
Future Trends and Ethical Considerations.

# data[].attributes.subject
Future Trends and Ethical Considerations.

# data[].attributes.body
The future of AI in healthcare is very exciting and has the potential to change how patients are treated. AI could help create personalized medicine, where treatments and prevention plans are designed specifically for each patient based on their health history, lifestyle, and genetics. AI can also improve telehealth and remote monitoring, allowing patients to get care at home while doctors track their health in real time. Another area is drug development, where AI can analyze large amounts of data to find new treatments faster and more efficiently.

However, there are important ethical concerns. AI relies on sensitive patient data, so privacy and security are very important. There is also the risk of bias in AI systems if they are trained on limited or unrepresentative data, which could lead to unfair treatment for some patients. It is also important for AI decisions to be transparent and accountable, so patients and doctors can trust the results.

I find AI in healthcare interesting because it can make care faster, safer, and more personalized. At the same time, I worry about privacy and bias issues. I think strong ethical rules, careful oversight, and diverse data will be key to making sure AI helps everyone fairly and safely, while improving healthcare for the future.

# data[].attributes.body
Researching the role of AI in healthcare was both enlightening and challenging. I explored examples such as AI-assisted medical imaging, predictive analytics for patient risk, and AI-driven robotic surgery, analyzing their benefits, limitations, and ethical considerations. One challenge I faced was navigating the vast amount of technical information and ensuring I understood complex AI processes while keeping explanations clear and accessible. I overcame this by consulting multiple reliable sources and summarizing key points in my own words.

Through this research, I learned how AI can significantly improve diagnostic accuracy, efficiency, and patient outcomes while also raising important ethical questions about privacy, bias, and equitable access. Personally, this research deepened my understanding of the potential of AI to complement human expertise rather than replace it. It also reinforced the importance of balancing technological innovation with ethical responsibility to ensure that AI benefits everyone fairly.

# data[].attributes.body
Khalifa, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 266, 107170. This article provides how AI enhances diagnostic imaging by improving accuracy, efficiency, and early detection in medical imaging practices.

Wah, J. N. K. (2025). The rise of robotics and AI-assisted surgery in modern healthcare. Journal of Robotic Surgery, 19(1), 311. This article provides the impact of AI and robotic-assisted surgery on surgical accuracy, workflow efficiency, and overall patient care improvements.

MRII. (2024, May 17). The limits of AI: Why in-person research matters more than ever. Market Research Institute International. This article provides why in-person research remains crucial, highlighting AI limitations and emphasizing human insights for accurate and effective data collection.

Czaban, T. (2025, March 5). Predictive analytics in healthcare: How AI is transforming patient care. GoodData. This article provides how AI-based predictive analytics transforms healthcare by enabling early intervention, improving patient outcomes, and supporting smarter clinical decision-making.

Jay. (2025, August 29). AI in healthcare: Ethics, regulations, and future trends. Medium. This article provides AI in healthcare, focusing on ethical considerations, regulatory challenges, and emerging trends shaping future medical practices.