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You may have heard the quote, "You have a 1000 problems, until you have a health problem. Then you only have one problem:" Our health, as a community, matters, so it is knowing how healthcare providers operate. Similar to choosing the healthier option when grocery shopping or taking daily walks, getting the right care is significant for our health. AI implementation has become widely used by many healthcare companies, providers, and practices. I chose to research this field due to my interest in a future career in the healthcare profession. I understand the concern people have about AI stealing their jobs, creating uncertainty in my personal choice of wanting a job as a surgeon. However, seeing how far we've advanced, a doubled global life expectancy over the last 71 years, these advancements hold great promise. I would like to further research how AI is administered in healthcare to better comprehend it. I personally believe healthcare is something that should be free of cost, as it is one of the basic needs of human beings. I have high hopes that better advancements will entail easier access around the globe as well. The use of AI in healthcare has significantly expanded over the years. Implementing AI in medical fields ensures more complex procedures to eliminate human errors. It improves the process of diagnosing and assisting healthcare professionals in providing the best care for their patients. The application of technology thus far has been mostly human centered, with assistance from AI during small procedures or surgeries while monitoring it. Let's research this topic and learn together just how much AI has been integrated in providing us the quality care we deserve.

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Healthcare - AI in Blood Infection Treatments

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Healthcare - AI in Blood Infection Treatments

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MYCIN, the earliest known AI program to be used in healthcare, helped treat blood infections. Developed during the early 1970s at Stanford University, we can thank Edward Shortliffe and the Stanford Heuristic Programming Group. It was invented to mimic the decision making process of medical professionals. It was also capable of explaining the reasoning behind the diagnosis, recommending a course of treatment. This program had all the recorded information on a patient, based on symptoms and medical test results. It relied on its knowledge base(an organized collection of facts) as well as an inference engine(which used the knowledge base to interpret the facts and reach a conclusion). The information instilled in the system was provided through human experts, using thousands of rules. According to Britannica, about 500 production rules were used in MYCIN. The backward chaining inference engine (system working backward from a conclusion to find the supporting argument using facts and rules to test the conclusion) was used in the creation of this technology. This advancement marks the beginning of a new era, introducing future and more personalized technology use in healthcare. Knowledge acquisition occurred when this system was able to explain the reasoning behind its findings. This solved the difficult and time consuming process of determining which infection to treat. While MYCIN's system was narrowed down to only bloodstream infections and meningitis, it opened the floodgates for other AI-powered systems to be executed. I enjoyed learning about this system, as it truly shows how far we have come. Having this system allows professionals to focus on HOW to provide care instead of wasting time on the WHAT. I believe this was significant in healthcare, allowing patients to receive critical care quickly, highlighting the importance of AI.

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In the history of AI's involvement in surgery, most of it has been to assist surgeons with precision and accuracy in their performance. Beginning with just video cameras, and assistance from robotic arms, providing surgeons with a 'third hand'. The whole concept was to have a robot serving as an assistant. One example is the ZEUS robot, performing Laparoscopic Cholecystectomy (a procedure to remove the gallbladder through small incisions in the abdomen using a laparoscope, which is a thin tube with a camera at the end). The robot included a camera arm, voice control, three robotic arms, and an operator console. This study included 40 operations that had no post-op complications. This reduces the limitations in surgeries, using voice activated cameras, robotic arm manipulation, and visualization systems. This improves precision and surgical performance through robotic assistance. Some challenges and limitations include ethical and legal concerns, as well as errors from robot malfunctions. The most widely used system today is called 'da Vinci' which implements a "master-slave" relationship with a human surgeon and the robot, using a console to operate. The robot can then act itself, utilizing robots as assistants. I find this innovation to be extremely cool. This truly eliminates human error in surgeries, as the robot will have precise movements. The only thing that scares me is the off-chance that the robot ends up making a mistake, or mistaking one thing for another, doing the surgery completely wrong. I'd personally be really terrified of a robot operating on me, but the results speak for themselves.

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AI has significantly transformed drug discovery, aiding in the process by identifying biological targets and analyzing different aspects of the drug. The AI is efficient in precisely identifying targets for drug development. They do this by predicting molecular interactions and generating drug like molecules. The machine is more cost effective, as traditional methods take more time and money. AI models are now able to predict toxicity, effects, and other details. This improves the success rates while lowering the costs of this analysis. An example would be during COVID-19, when AI was able to identify how to trigger a strong immune response, for an effective vaccine. This eventually led to the identification of the protein that was the target for priming our immune systems. This allowed scientists to quickly determine how to make the vaccine that would get an immune response. Clinical trials also used AI in the process, improving the sorting of participants based on individual risk factors and allowing researchers to recruit participants productively. Using AI for drug discovery significantly drops down the costs, and accelerates the discovery time, as well as raising success rates. Some challenges could include the difficulty of regulating AI or the uncertainty that comes with this technology. Further trials might be needed, as well as experts, to ensure the information presented is correct. Use of AI also brings no accountability, as well as transparency or biases. I believe most companies already integrate AI for their drug discovery process. This use of AI should be carefully done, as the drugs will be mass produced for the public, and the clinical trials should be taken more seriously. My personal opinion is that using AI in this manner can aid in making the process so much quicker and efficient, but this can only occur if AI is used with caution. It shouldn't replace researchers, but instead should be a tool for them to use wisely.

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Further evolution in AI will entail personalized medications, treatments, and more early disease and infection detections. I believe that drug discovery will go further, and we will have more AI diagnoses. More efficient algorithms will include imaging, electronic health data, and technological disease management models for improved patient care. Some websites believe that mental health therapy will rely more on AI than ever, using it to detect mental health disorders, present personalized treatment plans, and have AI virtual therapists. Due to the increasing demand for affordable and accessible healthcare, using AI might turn out to be the solution. I think that while AI can transform many aspects of healthcare, it should still be human operated. We shouldn't completely be at the hands of AI, as while it is reliable, one small issue and it can malfunction. We shouldn't take chances when it comes to our health. Also, there are a lot of ethical concerns regarding AI driven healthcare. The privacy of patients should be protected, and no unauthorized access should be allowed. The bias that these AI have should be addressed and shouldn't affect the health of these people. This can lead to inequality when it comes to delivering healthcare. I am concerned that people will blur the line between collaboration and dependency, which can be concerning. Too much dependency on AI can cause organizations and individuals to lose the control they have over their own healthcare system.

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In the future, AI is meant to provide healthcare professionals with assistance and tools that allow further advancements in delivering care. AI already plays a vital role in detecting diseases, suggesting ways of treatment, performing surgery and drug discovery. Due to the major demands of healthcare to be less costly, more efficient and accessible, these advancements will allow the demands to be met. Rapid diagnosis through AI will ensure less harm to the patient, as the urgency of detecting the disease can impact health. Improving the imaging, radiology and diagnostics will further allow care to be more successful. Not only can AI help save lives, but it can improve treatments and procedures. AI might lead to job displacement in the future, as while demand for AI is rising, the demand for human workers might lessen. Most people are also concerned with their privacy, as breaches of security can threaten their safety, which is another concern. The growing distrust between people and AI might hinder the integration of AI in healthcare. There is also the issue of trust with AI, as deepfakes and having data forever exist raise concerns. A third problem with further AI reliance in healthcare would be equality. Some systems can be biased and create unequal treatment across groups of people. Due to these machines being programmed with historical events and past experiences, they might unconsciously hold bias and stereotypes. While the pros outweigh the cons, these conflicts are still important to look into. Establishing trust regulation guidelines between use of AI and humans would be a good first step.

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Doing research for this project, I learned many things that I previously did not know. I learnt the true extent of AI usage in healthcare. It is truly more than I imagined. We use AI in diagnosing, in analysis, and have even begun using it in surgery. As a matter of fact, the first ever surgery was done by a robot itself recently! It is truly exceptional how far we, as a society, have come when it comes to healthcare, and much of it is also thanks to AI. I now understand not to be afraid of the unknown, but rather to understand AI and how it works. It should be integrated with human monitoring to not create hyper dependency on AI. This was a very interesting topic to research, and I am excited to see the future advancements in healthcare and AI together.

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References

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), 188–194. NCBI. https://doi.org/10.7861/fhj.2021-0095

( The future of healthcare)

Copeland, B. J. (2019). MYCIN | artificial intelligence program. In Encyclopædia Britannica. https://www.britannica.com/technology/MYCIN

(The examples of AI in healthcare)

Dattani, S., Rodes-Guirao, L., Ritchie, H., Ortiz-Ospina, E., & Roser, M. (2023). Life Expectancy. Our World in Data. https://ourworldindata.org/life-expectancy

Kang, Y., Jin, K., & Pan, L. (2025). AI designed, mutation resistant broad neutralizing antibodies against multiple SARS-CoV-2 strains. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-98979-w

(AI use in infection treatments)

Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024). Clinical applications of artificial intelligence in robotic surgery. Journal of Robotic Surgery, 18(1). https://doi.org/10.1007/s11701-024-01867-0

(ZEUS robotic surgery)

The Evolution of AI in Healthcare. (2021, February 2). Xsolis. https://www.xsolis.com/blog/the-evolution-of-ai-in-healthcare/

Varol, D. (2024, January 30). AI in Pharma: Innovations and Challenges | Scilife. Www.scilife.io. https://www.scilife.io/blog/ai-pharma-innovation-challenges