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In the 1970s, Stanford University researchers created an AI system that asked doctors questions about a patient’s symptoms and generated a diagnosis. In the 1990s, AI algorithms began decoding X-rays, CT scans and MRI images to spot abnormalities that humans might miss. We have come a long way since then, but what if I told you that currently there is AI being developed that could predict the probability a patient would be affected by a certain ailment, or die?

The power AI would have over the healthcare industry once fully integrated is unimaginable. It could save time in many ways: refilling prescriptions, providing instant responses to patient questions after hours, scheduling appointments, filling out billing and discharge paperwork, and much more. This would in turn decrease the number of professionals needed to staff a practice, and decrease the amount of hours they would need to work, saving money in the long run. While these are all relatively low risk tasks for AI to complete, other hospitals are looking to utilize artificial intelligence to optimize patient care in hospitals. One hospital that is spending millions to develop their own AI technology for clinical use is Mount Sinai. Currently, this hospital has a program that uses AI to generate scores that predict patient death risk, a measure which has been useful so far. One such score alerted a nurse that a patient had a kink in their chest tube that was causing their blood O2 levels to drop, saving their life. Other AI measures being explored by Mount Sinai is a program used to analyze mammograms to detect cancer, predicting and flagging drugs that may cause birth defects, and even to identify people with abnormal heart rhythms.

While such developments are undoubtedly impressive, it is also important to consider the potential limitations of AI use in its current state. Many medical professionals fear using AI in healthcare will come at a great cost to patients. Some issues that have been brought up are how AI would adhere to HIPAA and patient privacy concerns, the accuracy of such programs and how they may lead to incorrect diagnoses, and overall how this might introduce distrust in medicine. Researchers have also argued that it would be difficult to measure AI performance across different racial and ethnic groups; some AI programs have already been shown to exhibit racial biases.

While integrating AI into healthcare seems like a promising idea, a lot of research needs to be done to increase its accuracy and determine a suitable role for AI within healthcare systems . Nevertheless, AI will never be able to replace human interaction between medical professionals and their patients, and should instead be used as a tool to help with diagnoses.

Edited By: Heiley Tai

Designed By: Eugene Cho


Burke, G., & O’brien, M. (2023, October 20). Health providers say AI chatbots could improve care. but research says some are perpetuating racism. AP News.

Gupta, D. (2023, August 29). 10 Ways Healthcare Chatbots are disrupting the industry. Appinventiv.

Schwartz, N. (n.d.). How Mount Sinai uses AI to predict death risk. Becker’s Hospital Review.

Timsit, A. (2023, August 2). AI can detect breast cancer as well as radiologists, study finds. The Washington Post.

WP Company. (2023a, August 18). Hospital bosses love ai. doctors and nurses are worried. The Washington Post.

WP Company. (2023b, August 18). Hospital bosses love ai. doctors and nurses are worried. The Washington Post.

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Real-world data (RWD) is an emerging concept with a broad range of applications for studying patient health data including treatment safety and efficacy, short and long-term health outcomes, and disease burdens. RWD can include data from electronic health records, wearable technology, disease registries, patient-reported outcomes, social media, and various electronic health services and is a growing contributor to clinical and biopharma research on treatment efficacy. Traditional randomized control trials (RCTs) remain the gold standard for assessing treatment or diagnostic efficacy, but they often involve a homogenous population due to their strictly controlled inclusion criteria and limited sample sizes. Thus, RCTs can be limited in their ability to represent patient groups of minority backgrounds—including racial and ethnic minorities, patients with comorbidities, and patients with rare conditions.

Coupled with analysis using AI and machine learning technologies, vast amounts of real-world data from millions of patients have been analyzed to generate real-world evidence (RWE) more rapidly and with more cost-effectiveness than traditional clinical trials. With greater sample sizes, RWD may be better able to summarize the range of patient outcomes by including patient subgroups and demographics that are more representative of the true populations seeking healthcare. Additionally, this data may then be used to further efforts in precision medicine. Companies such as Flatiron Health, which generate RWE for oncology treatments, use data on patient genetic mutational variants and other biomarkers in an attempt to optimize treatment. This ability to capture diverse covariates is particularly useful in fields such as oncology and mental health, where patient outcomes are often associated with a multitude of genetic or demographic factors.

However, there are a number of limitations in the current capacities of RWD. A major limitation is the varying quality of data and lack of data standardization: patient notes involving free-text, incomplete fields, and different reporting formats across clinics all pose challenges. Additionally, rural healthcare systems or clinics with limited resources may be less able to anonymize data to share with RWD companies, potentially leading to their exclusion from RWD studies, and more broadly, the exclusion of minority populations whom their data represents.

Though further research and innovation is needed to improve data quality and analysis methodology, RWD continues to be a growing area of research that is taking steps toward addressing bias and disparities in healthcare.

Edited by: Anne Sacks

Graphic Designed by: Soojin Lee







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Imagine a giant football stadium, swarming with fans. Every single seat is filled up, and there are even people standing on the field and in the isles, just to be in that stadium. Now add 20,000 more people. That’s how many people were waiting for organ transplants in 2022.

Over 100,000 US patients each year are on a life-or-death organ transplant waiting list, but only around 40,000 transplants occur annually. For a majority of patients on that list, the wait for an organ is excruciating and can have a deadly toll, but the gap between the number of organ donors and the number of recipients is remaining relatively steady. When people are 16 and getting their driver's license, we can’t force them to check the ‘organ donor’ box. So, if we can’t increase the amount of donors, how can we fix this?

While we can’t increase the amount of human donors, we might be able to increase the amount of animal donors. Scientists have been experimenting with xenotransplantation, the transplantation of animal organs or tissue into a human. Within the past two years, there have been significant advances in this field including the first successful genetically modified pig heart transplant in 2022, at the University of Maryland School of Medicine, and the first successful pig kidney transplant in 2023, at NYU Langone. These are the first successful transplants in humans and with each new surgery, we continue to discover more and more about the human physiological response to animal organs.

While these transplants are promising, many physicians and patients are concerned with the ethical dilemma of xenotransplantation. Firstly, many patients may be resistant to receiving an animal organ as it conflicts with their personal religious beliefs. In Judaism and Islam, for example, pigs – or swine – are viewed as inferior and unclean beings. To have the organ of an animal that your religion forbids you from associating with can be mentally challenging. Even in patients who are not religious, the idea of having another animal’s organ inside of you is mentally tolling, especially with the stigma surrounding it. It’s hard to explain to someone who is not familiar with xenotransplantation treatment that you have a pig heart; to some people, it just seems gross. It may be hard for the patient to justify having that organ for themselves if people around them are not accepting of it.

Furthermore, the actual harvesting of pig organs is questionable morally and efficiency-wise. From birth, pigs are genetically modified in order to alleviate the human immune response from rejecting the foreign tissue. The caveat is that the pigs must be raised in a completely sterile lab and constantly undergo medical procedures until they are ready to be used for harvesting. The pigs – who are neurologically advanced animals – are not mentally stimulated, which raises concerns. Also, the entire process is costly and inefficient as a singular pig has to be raised in very specific conditions, so optimizing this procedure will take time.

Even if we develop an efficient way to harvest organs, it will still cost an exorbitant amount, leading to questions of who is prioritized for animal organs versus human organs. The financial costs of xenotransplantation often make it inaccessible for lower income families to obtain treatment immediately. This forces families to accept steep financial penalties if they choose to have the transplant rather than remaining on machinery such as a ventilator or dialysis machine as substitutes for failing organs.

However, if all of these ethical considerations are taken into consideration prior to xenotransplantation becoming a mainstream treatment option, then it will greatly benefit the medical community. So many lives will be saved because of this upcoming technology. It is only a matter of time before it becomes medically feasible.

Edited by: Sanjana Anand

Graphic Designed by: Shanzeh Sheikh


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   Duke Medical Ethics Journal   

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