AI remains a controversial topic, especially when it comes to its integration into fields that are built on a foundation of human trust, empathy, and innovation.

For instance, many educators have voiced concerns regarding the use of generative AI in schools, fearing that cases of plagiarism will skyrocket. In the field of medicine specifically, AI’s potential to enhance diagnostic accuracy and treatment efficiency conflicts with patient and medical professionals’ concerns about its impact on patient care and ethical implications. My interest in AI’s role in healthcare was catalyzed by a personal connection to breast cancer within my family, where I witnessed firsthand the challenges and importance of early detection and accurate diagnosis.

After this experience, questions regarding breast cancer detection and diagnosis were constantly on my mind. How could detection be more accurate? How could treatments be more specific and tailored? How can unnecessary invasive surgeries and inappropriate follow-up steps be prevented? Armed with these concerns, I started researching. Just a few Google searches later, I found information on applications of machine learning in tumor identification: algorithms that iteratively improve on detection through practicing on training data, allowing for more accurate detection of suspicious regions in mammograms.

I vividly remember the moment when I stumbled upon the application of machine learning in tumor identification. I was sitting alone at my computer, late at night, the glow of the screen the only light in the room. As I clicked through pages of Google results, I felt a flicker of hope ignite. The words on the screen seemed to leap out at me — machine learning algorithms, tumor detection, improved accuracy. My heart quickened, and I leaned in closer, absorbing every detail. I could almost see the algorithm in action, iteratively learning, adapting, and becoming more precise with each data point. It was as if I had unlocked a door to a new realm of possibilities. I was not just passively reading about a technological advancement; I was imagining the real-world implications, the lives that could be changed.

Such algorithms have been shown to more accurately recommend additional follow-up steps or screening when necessary. The potential of machine learning algorithms to improve the accuracy of mammogram interpretations and early detection of breast tumors offered a glimmer of hope amidst my apprehensions. Of course, there are ethical aspects of this method that need to be considered, such as patient privacy, potential bias associated with the algorithm, and the need for consistent human monitoring. In fact, these previously mentioned machine learning algorithms have been proven to be helpful when used as a supplemental tool, implying a need for constant human involvement. Furthermore, much of my research highlighted the many fears healthcare professionals and patients have of integrating AI into healthcare. More specifically, many report harboring the fear that an increase in AI in healthcare will eliminate a human-centered compassionate approach to patient care.

Spurred by this fear, I began to dig deeper and go beyond my initial Google searches, embarking on a journey of more rigorous research. This involved looking at peer-reviewed articles, watching webinars by experts in medical AI ethics, and engaging in discussions with current and future healthcare practitioners in my own life. Through my conversations, I uncovered that while machine learning algorithms can potentially improve diagnostic accuracy or patient outcomes, inaccuracies can be deadly. A medical student I spoke with discussed extensively how the potential for error is a deep concern of theirs and that a lack of human oversight in the training of AI could potentially harm patients. They highlighted the need for rigorous training and validation of AI systems to mitigate these risks.

And so, reflecting on this breakthrough of AI in healthcare, I am struck by the transformative possibilities it offers, yet I remain mindful of the ethical responsibilities that accompany its implementation. As a Gen Z student, I do fear that introducing AI in various industries could replace real human innovation and creativity. Especially in art-related fields or in healthcare, we must strike a delicate balance. However, knowing that computer-aided detection algorithms could potentially strengthen the accuracy of breast cancer detection fills me with hope. Applications of AI can be promising, especially when used as a supplementary tool to catch subtle abnormalities the human eye may miss rather than replace human expertise and compassion.

This experience did produce a personal interest in machine learning algorithms, and they’re surprisingly simple to create! While none of my projects will revolutionize the field of healthcare anytime soon, I’ve found enjoyment in creating data visualizations and predictions from virtually any data set. Exploring machine learning algorithms, mostly through self-paced online tutorials and community forums, has allowed me to gain small insights into what its applications could look like. For instance, in the realm of shopping and consumer trends, machine learning algorithms can predict consumer behavior based on past data in order to reduce production waste. From predicting trends in buyers’ behavior to classifying areas of concern in mammograms, the versatility of machine learning applications continues to captivate me.

Looking forward, I’m excited to leverage my growing understanding of machine learning to continue to research its applications. This excitement stems from a deeper realization I’ve had about my own goals: I’m passionate about using technology ethically to solve real-world problems. Whether it’s through conversations with medical practitioners, experimenting with new machine learning projects, or even surveying community members on their sentiments toward AI, I plan to gain more knowledge of what the integration of AI into healthcare would look like. While most of my past research was concentrated on AI’s role in medical imaging and diagnostics, I wonder how we can best implement this technological advancement into our daily lives without sacrificing safety, privacy, and human innovation.