Yale Journal of Medicine &Law

The story is simple. An unassuming patient waltzes into the doctor’s office. After months of their dentist harping on them, they’ve finally carved out time to make a wisdom tooth removal appointment. It’s a routine procedure. So they enter the waiting room, their mind at ease. Why would they worry? After all, they are surrounded by professionals who have undergone years of the most rigorous training and have spent even more time afterwards accruing experience with previous cases. The patient is called into the operation room. All is calm. Then the surgeon starts to perform.

Resource availability is often the limiting factor in modern medicine. That resource may be scientific knowledge or lab operating costs. In the case of 82-year-old Austrian blood cancer patient, Paul, that limiting factor may be time. With the typical cancer drugs failing one by one, and with nothing to lose, Paul’s doctors enrolled him in a clinical trial that used robotic automation and a field of artificial intelligence called computer vision to match people to cancer drugs based on individual biological differences (1). The idea behind this was similar to a traditional doctor’s approach; the machine learning models, trained to identify minuscule changes on the cellular level, were testing different drugs to find out what was effective. But instead of months of chemotherapy on an already frail physique, the computer system could do this all at once, requiring only a small tissue sample from Paul. Miraculously, one of the drugs identified by this process was successful, putting Paul into complete remission two years later. According to current knowledge, that drug was not shown to be effective in his type of cancer. But AI was able to predict something that doctors may never have known. In Paul’s case, AI saved a life by dramatically reducing the time and energy spent on drug testing. As AI increases in prevalence, it is essential to understand its prospective role in healthcare research and outcomes. It has the potential to expedite healthcare processes such as treatment and drug development by providing information that humans alone cannot possess. At the same time, there are critical ethical considerations to take into account when determining how best to implement AI in medical science.

In an era where the line between profit maximization and ethical healthcare practices is increasingly scrutinized, the pharmaceutical industry finds itself under close examination – at the heart of it lies the intricate balance companies like Pfizer must navigate among legal compliance, ethical responsibility, and profitability. This article aims to dissect this balance that has profound implications for the healthcare ecosystem. By critically analyzing Pfizer's settlements and analogous legal confrontations within the sector, this exploration seeks to illustrate patterns and practices indicative of the ongoing struggle between corporate ambitions and regulatory mandates. Moreover, it traverses the nuanced interplay between regulatory frameworks, corporate ethics, and their overarching impact on public health and market dynamics, offering a narrative that serves as a critical lens for examining the delicate balance between advancing medical innovation, ensuring fair market practices, and safeguarding access to essential healthcare services. This analysis contributes to the ongoing discourse on the ethical and legal dimensions of pharmaceutical business practices, underscoring the imperative for a balanced approach that serves both the interests of the industry and the welfare of the public.

Anti-racism interventions for healthcare professionals have grown increasingly necessary since the turn of the 21st century. The Civil Rights Act of 1964 spurred concerted efforts among national and state legislation to identify and eliminate racism in healthcare settings, but addressing medical professionals’ implicit biases has remained the responsibility of medical schools and hospitals. Such institutions instruct students and residents with curricula derived from racist roots, causing the majority of healthcare professionals to retain race-based misconceptions of the physiology and psychology of African-Americans today (1, 2, 3). A 2021 systematic review of 37 anti-racism intervention training workshops found that the curriculum disseminating knowledge of racist stereotypes is lacking (1). However, stereotype replacement and counter-stereotypical imaging are two evidence-based strategies that successfully reduced implicit racial prejudice as measured by an implicit association test in college students, accenting the need for a standardized organization of stereotypes contributing to medical professionals’ implicit biases (4).

The man lay still on the pavement, a soaked shirt and pair of shorts clinging tightly to his stiff form. His skin was cold and pale —his heart had stopped. John had been on a run when he saw the man fall off his paddleboard. It had taken John 2 minutes to realize that the man was in danger and 4 minutes to pull him out of the water. Now, John had two choices: attempt CPR or wait for the ambulance. In just 6 minutes, the stranger in front of him would die, while the ambulance would take at least 10. There was no other choice. John tilted the man’s head back, laced his fingers together, and pushed hard in the middle of the man’s chest. The stranger woke up the next day in the hospital, alive but with several broken ribs as a consequence of John’s attempted CPR. The man tried to press legal charges, but John was protected under the Good Samaritan Law.

We lead heavily legislated existences because we crave order; we want to uphold our fundamental rights to liberty, the pursuit of happiness, and especially life. However, a great legal, medical, and philosophical quandary arises when one seeks to surrender this basic right to life which undergirds the entire legal framework of the United States. To what legal obligations is one still bound when on the verge of death? Or when they actively pursue death? Multitudes of legislators and judiciaries have reckoned with these inquiries for decades over an extensive series of court cases. Looking at these legal and medical histories, a variety of semantic differentiations help to facilitate discussion on the broad topic that is end-of-life care.

It is time that the fatal repercussions of biases from our nation’s health care providers against Native Americans and other ethnic minorities is better scrutinized. Native American women have the highest maternal mortality rate of any ethnic group living in the United States. Indigenous women are approximately three times as likely as White women to die of pregnancy-related causes, and hemorrhage and hypertensive disorders during the gestational period are among the biggest contributors1. According to the CDC, sixty percent of maternal deaths are preventable, and it is well overdue to implement greater preventive efforts, including funding more and better research, as well as responding to factors that contribute to these disproportionate maternal mortality rates2, 3. This study deals with institutional racism within the US health care sector and its impact on Native American women during and after childbirth, as well as the related social determinants of health, social injustice, and structural racism that continues to permeate within this community .

Two years ago, Dr. Doug McGuff was working a shift at an ER in Upstate South Carolina when a group of first-year college students were rushed in, unable to breathe. From the first responders, he gathered that they had been found using marijuana, a mild psychoactive drug capable of inducing hallucinations; however, he knew that the marijuana alone could never be responsible for such an aggressive reaction. As Dr. McGuff worked to save the lives of his patients, he knew he was witnessing an episode of the same public health crisis documented around the country- a crisis that plagued patients from San Francisco to Baltimore. He was racing against a silent killer, one found in mere microgram quantities within the bloodstream of his patients: fentanyl.

With its potential to treat a wide range of debilitating diseases and injuries, stem cell therapy has captured the interest of the medical community and patients alike. However, most stem cell applications are still in their infancy and have yet to demonstrate clinical benefits. Within the US, the stringent FDA regulatory framework limits the availability of stem cell therapies that have not undergone rigorous clinical testing and approval processes. This framework is necessary to ensure the safety and efficacy of stem cell therapies for patients but also means that the development and approval of new stem cell therapies can be a lengthy and expensive process. In the interim, the provision of unproven stem cell interventions has developed into an unregulated global industry: desperate patients who are promised medical benefits beyond what the scientific literature can substantiate pay large sums of money to private clinics in countries with less rigorous regulations, such as Mexico and Thailand. This form of medical travel is called "stem cell tourism" and has been criticized for numerous ethical, legal, and health concerns. As reckless policies may drive patients away from clinical researchers, promote illegal activity, and tramp on other forms of medical travel, it is critical to strike a balance between recognizing the hope that stem cell treatments can offer and the harm that unproven treatments can cause. Thus, effective solutions can be devised only by understanding the nuances and challenges of this industry.

The release of ChatGPT in November 2022 resulted in the biggest and fastest boom in artificial intelligence (AI) technology. Within a week, 1 million people had signed up to use the technology; by January 2023, ChatGPT boasted over 100 million users, a feat that took giants like TikTok, Instagram, and Google far longer to achieve (9 months, 2.5 years, and 5 years, respectively). With the AI buzz only growing, everyone from start-ups to researchers to individuals is looking for their own way into the sphere. The Wall Street Journal reports that over $2.6 billion was invested into AI startup technology last year—a trend likely to continue, as AI is considered one of the most secure investments in modern technological innovation.