Journals

August 12th Journal Post: 

My name is Harshetha, and I’m entering my junior year of high school as a first-year EMC student. Some activities I do outside of school are running and reading. I did track for the last two years and plan on continuing it this year. I enjoy reading horror books. I've been reading a lot of horror books this summer. I am currently reading a book called Echo. I want to branch out of horror books and try reading a fantasy book sometime soon. 

Volunteering is another activity I love. I volunteered for Key Club and Garden Club throughout the year. Volunteering is my favorite activity because it is a way to contribute to our community and is an opportunity to connect with new people. In my free time, I also like to go on walks and play badminton. 

A surprising fact about me is that I lived in Singapore for five years, ages one to six, then moved to India for two years, and finally settled here when I was eight years old.

August 19th Journal Post:

I think my relationship with my grades is pretty good, I try my best to get high 90’s. My favorite memory was my first track meet in 9th grade. I remember being terrified right before my run because I did not feel ready. It was raining that day too which only made it worse. I started sweating at the starting line praying for it to be over.  When I started running, I forgot everything. My only thought was to complete the 400m. Before I knew it, I finished my run! It was not nearly as bad as I thought. Looking back, I actually think I enjoyed the run because all my fears left me when I started running.

My motivation in school comes mostly from my friends, grades and teachers. Something I love about school is lunch because it’s when I can connect with friends. Something I hate about school is how early it starts. My normal wake-time the whole summer vacation has been 9 AM, which is way later than when I have to wake up for school. Waking up at 5 AM for school everyday is the worst thing about school. 

I was attracted to EMC because I really like the idea of self-researching a topic I am passionate about. I’ve always wanted to self-research, and the idea of EMC was the perfect way to do so. 

August 26th Journal Post:

This year I would like to research how the future of artificial intellegence may look like. AI is taking over a lot of jobs including some sectors of healthcare like quick diagnosis, treatment plans and patient care. Artificial intellegence is likely to take over customer service, translation and simple financial analysis. Some jobs that would be very unlikely for AI to take over would be healthcare professionals, teachers, lawyers and creative professionals. 

5 Questions:

• What are some regulations that should be in place for AI development?

• What are ways AI will influence in healthcare advancements?

• How can we ensure medical decisions driven by AI are safe?

• Can we really trust AI to make important decisions?

• Are there any new jobs that can be created because of AI?

3 Professionals: 

• Mr. Rice - Computer Science Teacher at GHS

• David Turetsky - Professor of Homeland Security at the University at Albany

• Gopal Jasti - Software Developer

September 16th Journal Post: 

I think that AI has some positive and negative benefits to it. The use of AI can help prescribe medicine to a degree, AI can help diagnose patients faster which helps them get better much quicker. On the other side, there is still a chance that AI could malfunction or something could go wrong and the patient could be prescribed the wrong medication which would lead to much more dangerous issues. Also, humans are not 100% accurate either, but they are more precise and reliable because of their years of experience which a machine does not have. AI will replace a large portion of customer service (this is also happening). 

I know that artificial intelligence will take over automation such as automated customer service, responders, and smart assistance. I know that AI will be taking over data like data processing and analysis. I know that if AI is used appropriately, it can be efficient and help boost productivity in the workplace (Emily Byrne Ai in the workplace: 12 ways workplace AI can improve the Office). I know that 85 million jobs will be lost due to the growth of AI and 97 million new jobs will be created. I know that AI will sometimes have to use sensitive information, which is a major privacy risk (Karl Balancing the pros and cons of AI in the Workplace).

September 23th Journal Post:

1. I don't know how AI first started

2. I do not know what job opportunities opened up with the creation of AI

3. I don't know how AI will be used in warfare

4. I don't know how accessible AI is to people around the world

5. I don't know if AI can help more people than it can harm

6. I don't know if AI can get to a point where it can be identical to a human

7. I don't know if AI can lead to privacy concerns

8. I don't know if AI in medicine will have a positive or negative impact

9. I don't know if the use of AI is helping more students than it hurts

10.  I don't know how much AI can harm one’s creativity

From my list of 10 “don’t know”, I determined that I need to know the privacy concerns of AI. This is important because AI technologies allow personal information to be linked across different data sources, which increases the risk of identity exposure. This is happening because of the spread of misinformation through fake images or videos that AI creates. This allows information to be either damaged or stolen. I may need to find ways to prevent this. There might be software online that can be downloaded to prevent this. 

September 30th Journal Post:

Need to know: In what ways does using AI pose a privacy risk?

One big challenge is to make sure that all users understand that their data is collected by AI systems like ChatGPT. More importantly, many users are unaware of how this data gets used, which brings up huge privacy concerns. The misuse of data can be broken down into three categories: data persistence, data repurposing, and data spillovers. 

Data persistence refers to ongoing availability and storage. AI collects and stores all the information the user shares, this includes sensitive information. This data can even be accessed long after its initial purpose, which makes it a high risk. Sensitive information that gets stored in AI can be easily misused by hackers. Data repurposing is when the data gets used for a purpose other than what it is originally supposed to be for. Data spillover is when unintended exposure of data beyond the audience who should be accessing it, this leads to data breaches.

A major issue to privacy when using AI are data breaches and unauthorised access to sensitive information. So much data gets collected by AI that there is a good chance it may fall into the wrong hands. Hackers use this method to threaten the user into making them pay a certain amount to ensure their information stays safe. 

The two sites I used: 

https://transcend.io/blog/ai-privacy-issues 

https://economictimes.indiatimes.com/news/how-to/ai-and-privacy-the-privacy-concerns-surrounding-ai-its-potential-impact-on-personal-data/articleshow/99738234.cms?from=mdr

October 11th Journal Post:

I think that October SDA assignment did not allow much freedom because the rubric felt stricter compared to other assignments. However, something I really liked about the SDA was using my need-to-know question for the post. I found really interesting information. I think that my need-to-know allowed me to stay on track with my topic. A con would be that oftentimes, I found it difficult to find information. A lot of the information I found was overlapped. I would say I dedicated two to three hours working on this assignment. Through my research, I learned a lot about what it is like to work in cybersecurity. Even though my need-to-know question was more geared towards privacy concerns of AI, a lot of the websites also included information about ways to prevent your privacy being exploited which is what a cybersecurity worker does. I also learned all the ways a hacker can gain access to the user’s personal information. One of the biggest ways is through phishing emails, which is when a hacker sends the user an email like they are a legitimate organization to trick them into sharing their information. The most important thing I learned was to be careful what information you share to the internet because it could fall into the wrong hands very easily. Being aware that the data you share can directly go to  a hacker is crucial to staying safe from cyber criminals. 

I think it is important to break down a problem into basic elements. This helps understand patterns and efficiently solve the problem. Looking back, I am proud of taking the time to find quality information for each of the privacy issues. One area I could have done better is maybe research more about each of the privacy concerns I included to get a better understanding. Doing more quality research on each one of the components of privacy issues would have improved my SDA. 

My mentor, Professor David Turetsky at UAlbany, provided valuable insights that helped me better understand my topic. He guided me to consider all aspects of privacy concerns, which is a crucial component of my research I should consider. My new essential question is: What are the negative effects of AI?

Yes, I definitely feel heard by my mentor. He helps gain a clear understanding of AI and its impacts.

October 23rd Journal Post:

 My need-to-know question is “How is AI impacting healthcare?” I believe this falls into the Knowledge category because the question starters “How is…” starts my need-to-know question as well. Through my research, I can learn more about ways that the use of AI can impact healthcare in both positive and negative ways. 

My sub questions are: 

• (Analysis: I would try to find aspects where AI is being used regularly) What are the parts in the healthcare sector where AI is heavily used? 

• (Evaluation: I would mention effective ways AI could help us) In what areas of healthcare would AI be safe to use? 

• (Synthesis: I would find ways that AI can help people who work in healthcare without jeopardizing their job) How should AI be designed so that it wouldn't impact jobs in healthcare? 

I think the sub question I will focus on will be, “In what areas of healthcare would AI be safe to use” This will help my need-to-know question of “How is AI impacting healthcare”

Resources

Berry, Melissa D. “Understanding the advantages and risks of AI usage in healthcare.” Thomson Reuters, 27 September 2023, https://www.thomsonreuters.com/en-us/posts/technology/ai-usage-healthcare/. Accessed 30 October 2024.

Moore, Jon. “AI in health care: the risks and benefits.” Medical Economics, 15 March 2023, https://www.medicaleconomics.com/view/ai-in-health-care-the-risks-and-benefits. Accessed 30 October 2024.

“Revolutionizing Healthcare: How is AI being Used in the Healthcare Industry?” Los Angeles Pacific University, 21 December 2023, https://www.lapu.edu/ai-health-care-industry/. Accessed 30 October 2024. 

October 23rd Journal Post:

After reading through these sources, I think I still need to learn exactly how accurate AI diagnosis and treatment is. One of the sources I read mentioned opportunities that AI could bring, which I'd like to research further.

Here are the two sources that could help me: 

“AI in healthcare: The future of patient care and health management.” Mayo Clinic Press, 27 March 2024, https://mcpress.mayoclinic.org/healthy-aging/ai-in-healthcare-the-future-of-patient-care-and-health-management/. Accessed 30 October 2024.

Brady, Adrian P. “Five Ways Artificial Intelligence (AI) Is Improving, if not Transforming, Healthcare | Michigan Tech Global Campus.” Michigan Technological University, https://www.mtu.edu/globalcampus/five-ways-ai-helps/. Accessed 30 October 2024.

November 6th Journal Post:

S - My need-to-know questions matter because they can help me gain more detailed knowledge about the field. It is important to understand when AI can be used in healthcare so that it benefits people and not harm them. AI can help diagnose and treat patients quicker which can save lives.

P - AI in healthcare affects patients and medical staff. Patients can benefit from quicker diagnosis and treatments. AI can assist medical staff by helping them lightening up their workload and help with more complex cases. 

E - This information is valuable because the quick diagnosis will lead to a sooner treatment. This faster treatment has potential to save lives and is efficient. My information is coming from using a reputable website which makes my research reliable.   

C - This is connected to computer science. The ability AI has to analyse information comes from data science which enables it to diagnose and treat patients. This also might be connected to engineering. The ability for AI to monitor a patient and effectively diagnose and treat patients comes from.

S - I initially thought that AI had no place in healthcare due to the high stakes involved. I didn't think it was safe for medical staff to be relying on AI. I later learned that when used appropriately, AI can be very beneficial and help improve countless lives.

https://www.forbes.com/councils/forbestechcouncil/2024/01/18/the-role-of-ai-in-healthcare/

November 13th Journal Post:

For the November SDA, I am planning on doing the QFT. Here are my 20 questions: 

• Is AI currently used in healthcare for diagnosis?

• Can AI improve its diagnostic accuracy for diseases?

• Does AI play a role in x-ray imaging?

• Is AI reliable to assist in early detection of disease?

• Could AI predict a patient's recovery time?

• What are (if any) the ethical concerns of AI in healthcare?

• Is AI capable of monitoring a patient's health?

• Is AI able to analyze medical records?

• Can AI possibly predict disease outbreaks?

• Will AI's accuracy ever be better than medical professionals?

• Will AI be able to take over simple decision making in healthcare?

• What are challenges with AI's integration in healthcare

• Is AI able to protect patient privacy and data security?

• Will patients be comfortable integrating AI in healthcare?

• Will AI's diagnosis accuracy be better than medical professionals?

• Can AI identify rare diseases?

• Can AI provide support to patients?

• Can AI be used in an emergency room setting?

• Can AI assist with quick diagnosis in an emergency setting?

•  Are there any aspects of AI that is being heavily utilized in AI right now?

Here are my top five questions: 

• Is AI reliable to assist in early detection of disease?

• Can AI assist with quick diagnosis in an emergency setting?

• Is AI able to protect patient privacy and data security?

• Is AI capable of monitoring a patient's health?

• Will AI's accuracy ever be better than medical professionals?

Sources

Brady, Adrian P. “Five Ways Artificial Intelligence (AI) Is Improving, if not Transforming, Healthcare | Michigan Tech Global Campus.” Michigan Technological University, https://www.mtu.edu/globalcampus/five-ways-ai-helps/. Accessed 30 October 2024.

“Council Post: The Role Of AI In Healthcare.” Forbes, 18 January 2024, https://www.forbes.com/councils/forbestechcouncil/2024/01/18/the-role-of-ai-in-healthcare/. Accessed 13 November 2024.

Moore, Jon. “AI in health care: the risks and benefits.” Medical Economics, 15 March 2023, https://www.medicaleconomics.com/view/ai-in-health-care-the-risks-and-benefits. Accessed 30 October 2024.

December 7th Journal Post:

I dedicated around three hours on my SDA. During my research, I learned a lot about AI's ability to monitor a patient's health. I learned that AI is capable of keeping an eye on a patient's health through a program called RPM (Remote Programming Monitoring). RPM is used to help track symptoms of diabetes and heart disease. Some notable benefits of RPM are early detection, its ability to spot small changes a patient's health and RPM helps with better treatment. However, RPM isn't always accurate. There may often be missed issues or false alarms. The most important thing I learned is that AI is highly reliable in assisting early detection. Medical imaging has an accuracy rate of over 90%. Looking back, I am most proud of using the best information from my research to use for my SDA. I still need to work on the creative component, I will try to make sure to make my next SDA more interactive. Using this information, I might want to research about the long-term effects of AI. I might look into what aspects AI will be able to take full control in. My new essential question is: to what extent is AI affecting medical diagnosis and medical professionals?

The Academic Minute: How Science Really Works

1. Soazig Le Bihan argues that our understanding of science may be outdated and that we need a new perspective. The creators highlighted this to keep the audience thinking and share their insight on the topic.

2. The podcast was informative, professional and engaged the audience.

3. The podcast incorporated a lot of research and important information to support the argument. 

4. It was a short video so which allows the listener to stay focused throughout the podcast.

January 13th Journal Post:

Our midterm research project explores the role of the healthcare system in the aftermath of Formula 1 (F1) accidents, specifically on immediate medical response. The article "Formula One: a 'crash' course in motorsports medicine" provided valuable insights to improvements in both vehicle and medical safety. I found this article by using PubMed's advanced search with the terms "F1 driver injury recovery." 

The article highlights the integration of specialized medical teams and facilities to reduce the risk of severe injuries. The Halo device, for example, has significantly improved driver safety by decreasing the chance of injuries. The Accident Data Recorder has also improved safety for F1 drivers. This article is directly related to our research question as it demonstrates how medical responses have improved recovery outcomes and the importance of improving medical reponses with high-risk sports like F1 driving. 

A key takeaway from the article is the importance of enhancing medical responses to high-risk sports like F1 racing. Additionally some new key terms I learned are Halo device and Accident Data Recorder, which are important terms to know to understand safety in F1 driving. 

The RadioLab episode "Animal Minds" demonstrated the power of emotion while storytelling. The episode emphasized on emotions such as fear, curiosity and surprise through the tone of the narrators' voice. This technique is demonstrated throughout the podcast especially when the narrators shared the story of whales communicating. The narrators changed their voices, used music and descriptive language to help the listeners visualize the story. This approach showed me how to captivate the listener by using emotion. 

“Animal Minds.” Radiolab, https://radiolab.org/podcast/animal-minds-211125. 

Accessed 13 January 2025.

Kempema, James Michael. “Formula One: A ‘crash’ Course in Motorsports Medicine.” Trauma Surgery & Acute Care Open, U.S. National Library of Medicine, 15 Apr. 2024, pmc.ncbi.nlm.nih.gov/articles/PMC11029468/. 

January 20th Journal Post:

The article, "Design and Simulation of a Machine-learning and Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform" from Procedia Manufacturing explores the role of AI on Formula 1 (F1) drivers. The organization's goal is to increase Formula 1 drivers' safety and recovery process. The problem identified in the article is the complexity of improving driver safety and recovery.

The three authors of my article are Alexandru Tatulea-Codrean, Tommaso Mariani, and Sebastian Engell. Alexandru Tatulea-Codrean studied at the Politehnica University of Timisoara (2007-2011) for Automation and Computers. He currently works as a Technology Expert at Bayer. Tommaso Mariani studied applied physics and now works at Energee3. Sebastian Engell is currently a professor at Process Dynamics and Operations at the TU Dortmund University. 

My article this week focuses more specifically on how AI is used after a crash. This article also mentions real-time data collected by AI from crashes. My article last week mainly focused on healthcare side and how it was used to help driver recovery. 

I think my partner and I can use this information to connect how AI tools can be used to prevent severe injuries to F1 drivers. The article mentions a lot about data analysis and their ability to create crash scenarios to better protect the drivers, and not as much on AI. 

Telemetry data from F1 cars include real-time information like the car's speed, g-forces, brake pressure, and its impact points. After a crash, AI processes this telemetry data quickly to see the amount of damage occurred and helps determine adjustments that should be made to improve the car's safety. Crash impact simulation is an AI tool that creates crash scenarios to better understand what needs to be improved in a car to enhance driver safety. AI can analyze the amount of damage to a car, point out its weaknesses, and analyze how well the car can withstand the high-speed impacts in a race.  

A major limitation in the article is the lack of any specific case study. There were no specific examples of any case studies regarding the use of AI in the aftermath of a crash. The lack of these examples made it hard for me to understand how exactly AI can be applied to real F1 crashes.

Some new key terms I learned from this article are telemetry data and crash impact simulation. Telemetry data is information collected from a race car's sensors that shows real-time data about the car's performance and driver's behavior. Crash impact simulation is when AI is used to model a crash scenario to help better understand the risks for F1 drivers, learning this can help improve safety for drivers. The database I used to find this article is ScienceDirect and used search terms like "AI in Formula 1 safety." 

In the Relative Genius podcast, mystery and storytelling played a huge role in capturing my attention. The podcast builded up suspension which kept me hooked on to the unsolved mystery. I liked that the podcast also kept me on my toes as I was listening, there were often pauses in between to keep me thinking about the mystery. 

Overington, Alex, and Jad Abumrad. “G: Relative Genius.” Radiolab, 28 June 2019, https://radiolab.org/podcast/g-relative-genius. Accessed 22 January 2025.

Yang, Guangwei, et al. “Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.” Sensors (Basel, Switzerland), U.S. National Library of Medicine, 12 Apr. 2024, pmc.ncbi.nlm.nih.gov/articles/PMC11055067/.

January 27th Journal Post:

The article, "Diagnostics Applications of AI in Sports: A Comprehensive Review of  Injury Risk Prediction Methods" explores how AI is transforming prediction and prevention of injuries in sports. The problem identified in the article is the need for higher accuracy rates of AI when used in injury risks in sports. The authors conducted a literature review where they gathered studies from 2014 to 2024 on applying AI tools of machine learning, deep learning, particularly random forests, convolutional neural networks, and artificial neural networks. The article was published in Diagnostics. Its organizational goal is to help promote advancements of AI in their application in healthcare.

The authors of my article are Carmina Liana Musat, Claudiu Mereuta, and Aurel Nechita. All of these authors are professors of the Lower Danube University of Galati. Carmina Liana Musat published many articles on how AI is improving injury recovery. Claudiu Mereuta published articles on AI in sports. Aurel Nechita published articles on how AI can help lower the risk of certain diseases. 

the authors reviewed articles from databases like PubMed, Google Scholar, and Science Direct and used keywords such as "artificial intelligence," "sports injury," and "risk prediction." The findings highlight AI's potential to improve injury recovery by analyzing large datasets. Some flaws or limitations is that there could be bias in the studies reviewed. The database I used is PubMed and used the search terms "AI in sports injury."

The integration of AI in sports injury demonstrated the shift from reactive to proactive health management, which shows how this paper connects to the real world. The article connects to the theme of AI applications in sports medicine by highlighting how technical advancements can improve safety and enhance injury recovery. 

Some key terms that are new to me are random forests, convolutional neural networks, and artificial neural networks. Here are some HOTQs from the article: What approach would you use to integrate AI-based tools into existing sports medical practices? What do you think would be additional challenges for injury data integration as mentioned in the article?

Research played a key role in "The Queen of Dying" by providing detailed insights into the medical and emotional aspects of dealing with end-of-life care. The use of case studies and interviews added realism, giving it a solid foundation based on real experiences. 

“Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.” PubMed Central, 10 November 2024, https://pmc.ncbi.nlm.nih.gov/articles/PMC11592714/. Accessed 26 January 2025.

“The Queen of Dying.” Radiolab, 23 July 2021, https://radiolab.org/podcast/queen-dying. Accessed 26 January 2025.

February 26th Journal Post:

My partner, Nana, and I worked on a RadioLab style podcast where we combined our topics: Nana focused on Formula One car analysis, while I explored the role of AI in healthcare. We met at the Guilderland Public Library to record our discussion, and overall, our collaboration went smoothly. 

One challenge we faced was our introduction being too short. We re-recording it several times and lengthened our introduction by adding more details and enhancing out hook. 

Initially, I wanted to incorporate many sound effects to keep the listener entertained. However, I realised that too many background elements made it difficult for the audience to focus on my voice. I shifted my approach, minimizing sound effects and choosing more calm background music instead. This change surprised me as I noticed that subtle music helped to stay more engaged with what I was saying.

 To stay organized, Nana and I wrote down key ideas for both our individual segments and discussion in a shared Google Docs. During recording, we used this document as a blueprint, and expanded on our points. This method helped us avoid awkward pauses while we were recording. 

My script writing skills improved throughout this project. I learned how to properly balance between writing enough to guide my thoughts while also avoiding writing so much that I am reading off a script. Recording was the most challenging part for me. I often lost my train of thought or encountered awkward pauses. 

I learned a lot of presentation skills from the RadioLab assignment. I initially struggled with expanding on the ideas I wanted to mention. I later on got more comfortable with recording and was able to more fluidly explain my research. 

One of my proudest moments was discussing the proactive uses of AI. This segment was my favorite because these advancements can truly change the game in AI. If I had an extra week to work on the project, I would add more depth to my individual section and maybe include more information in our introduction. 

For future podcasters, my advice would be to start early. Giving yourself enough time to plan, record, and edit makes a huge difference in the final product. As for my takeaways, this project taught me more about valuable time management and presentation skills. I became more comfortable speaking fluidly about my research on the spot, which was something I struggled with at the start of the project.

March 6th Journal Post:

For the upcoming SDA, I have chosen the case study of Sickbay, an AI-driven healthcare system, because of its innovative use of AI in patient monitoring and predictive analysis. The system caught my attention by its ability to detect early signs of health deterioration, potentially saving lives by offering real-time interventions.

Sickbay uses artificial intelligence to continuously monitor patient data, identifying patterns and anomalies that human doctors might miss. What makes it unique is its application in a non-clinical setting, like a general hospital or emergency services, where it acts as a second layer of defense for early intervention. The system integrates data from various sensors and devices, constantly analyzing health metrics to predict potential issues before they become critical. 

For example, in a cardiac study at UAB, Sickbay analyzed real-time data from 55 patients during surgery, calculating their optimal blood pressure levels every few seconds. This allowed doctors to make immediate, personalized adjustments, improving patient outcomes, which was something previously impossible. This demonstrates how AI-driven monitoring can enhance precision in treatment and patient care. 

Key Questions to Explore:

• How does Sickbay's AI model compare to traditional patient monitoring systems in terms of accuracy?

• How can AI systems like Sickbay be implemented so that it could replace human practitioners?

Source(s)

Fujisan.sickbay. “How Sickbay Enables AI-Driven Healthcare.” Sickbay, 13 Jan. 2025, sickbay.com/how-sickbay-enables-ai-driven-healthcare/.

Intel, www.intel.com/content/dam/www/central-libraries/us/en/documents/mic-sickbay-platform-uab-case-study.pdf. Accessed 14 Mar. 2025. 

March 13th Journal Post:

Biggest unanswered questions:

• How reliable is AI-driven monitoring in real-world hospital settings?

• Does AI have the ability yet to outperform human decision-making without risks?

Through a scientific perspective, AI-driven systems like Sickbay can enhance patient monitoring and predictive analysis, potentially improving healthcare outcomes. However, there are concerns about data accuracy. Through an ethical perspective, some argue that AI could dehumanize patient care or lead to over-reliance on technology, reducing doctors' autonomy in decision-making. From a patient's personal perspective, real-time AI monitoring could provide a sense of security, but some might be skeptical about trusting an AI with life-or-death decisions. 

A surprising detail from my case study would be how Sickbay is able to process data from multiple patients at the same time. This feature saves a lot of time compared to the traditional patient monitoring which would take much longer. I was also surprised when I learned that the Sickbay system can calculate personalized, real-time blood pressure recommendation during a surgery.  This is groundbreaking because we are starting to see AI being integrated into emergency response in healthcare which benefitting healthcare workers in stressful situations. 

If I could interview someone regarding my case study, I would interview Dr. Ryan Melvin. He is a data scientist and professor at UAB's Department of Anesthesiology and Perioperative Medicine. Dr. Melvin played a major role in implementing Sickbay. His vast knowledge in data-managed healthcare makes him a credible source. Some questions I would ask are: What are the biggest limitations of Sickbay? How do you see Sickbay advancing over the next decade? 

Sources

Fujisan.sickbay. “How Sickbay Enables AI-Driven Healthcare.” Sickbay, 13 Jan. 2025, sickbay.com/how-sickbay-enables-ai-driven-healthcare/.

Intel, www.intel.com/content/dam/www/central-libraries/us/en/documents/mic-sickbay-platform-uab-case-study.pdf. Accessed 14 Mar. 2025. 

March 20th Journal Post:

I am doing my case study documentary on Sickbay and its significance in modern healthcare. My plan is for this project is seen below: 

Introduction:

• I will start my documentary off with a hook on a real-life scenario where AI monitoring could save lives. 

Background: 

• I will be discussing the evolution of healthcare, focusing on patient monitoring and predictive analytics. I'm planning on diving into the specifics on how Sickbay works, its applications in hospitals, and its impact on patient care. I'll make sure to understand the benefits, challenges, and controversies around AI-driven healthcare. 

Conclusion: 

• I will be reflecting on the future of AI in healthcare and the potential for systems like Sickbay to become a standard in patient monitoring. 

Visuals, Footage, or Images: 

• I will use stock footage of hospitals, patient monitoring, and medical professionals as well as visualizations of AI data analysis and patient metrics. I will also try to find graphics showing real-time monitoring data to illustrate Sickbay’s capabilities.

Engaging the Audience:

• I will try to find compelling patient stories or hypothetical scenarios to show the impact of AI. I will try to incorporate interviews with healthcare professionals or AI experts. I will maintain an even balance between technical information and relatable storytelling.

Challenges & Solutions:

• A challenge I may face is balancing technical depth and also making sure the information I'm displaying is easy to understand. To solve this I will use simple analogies and clear visuals to explain AI concepts.

March 27th Journal Post:

The main message I want the audience to remember is how AI-driven systems like Sickbay are revolutionizing patient monitoring, improving early detection of health issues, and ultimately saving lives. While AI in healthcare presents challenges, its potential to enhance medical decision-making will change the game in healthcare. 

Researching Sickbay has deepened my understanding of AI's role in its predictive analytics, real-time patient monitoring. I've also explored the ethical concerns surrounding AI in medicine, such as data privacy and the balance between human expertise and AI assistance. 

Before I begin filming, I still need to gather expert opinions from AI specialists on the effectiveness and the limitations of AI in monitoring. I would also like to collect more visuals on AI data analysis graphics and real-time patient monitoring examples.

My step-by-step plan for creating and editing the documentary starts with finalizing the script, narration, and key visuals. I will gather stock footage, graphics, and try to get expert opinions. Next, I will record voiceovers and collect real-world footage related to AI in healthcare. I will add transitions, background music, and visuals to keep the documentary engaging for my audience. Finally, I will review and make revisions to ensure clarity and balance between technical details and storytelling. 

April 24th Journal Post:

As the year wraps up, we’re working on our final EMC project — a TED Talk-style presentation for our Symposium. The theme this year is “Pushing Boundaries,” and I’ve chosen to focus on a topic that’s not just about the future, it’s already here: artificial intelligence in healthcare. I included a draft of my introduction for the Symposium below.

Symposium Speech

Symposium Slides

May 1th Journal Post:

I began building the body of my Symposium speech by focusing on establishing ethos. Then, I outlined my three main points for the logos component and included evidence in each. Each point shows a different standpoint of AI (the good and the bad). The link for my Symposium speech draft and slides are below:

Symposium Speech

Symposium Slides

May 8th Journal Post:

For this post, I worked on writing the conclusion for my symposium speech about the impact of AI in healthcare. I wanted the ending to bring everything together clearly and leave my audience with something to think about. I also reminded the audience that even though AI is exciting, we have to be careful with how we use it, especially when it involves real people's health. 

Symposium Speech

Symposium Slides