The Rise of AI-powered Health Care
Albert Katz (WG23) embraces a fundamental truth: nothing matters unless you’re healthy.
That simple philosophy, along with undergrad and master’s degrees in finance and computer science, and a Wharton School MBA in health care management, motivated him to launch Flagler Health, which combines patient data and the power of AI to improve health care.
Flagler’s AI algorithms analyze patient data, including medical records, to provide doctors with insights and recommendations for treatment and to optimize patient care. “I wanted to save money for the health care system,” notes Katz, whose startup was a finalist in Penn’s 2023 Venture Lab Startup Challenge and continues to innovate in areas like remote therapeutic monitoring and behavioral health. “Let’s try to gather that data so eventually we can help specialists adopt value-based care – providing optimal care to patients.”
It’s been called the health care AI revolution – an abundance of algorithms analyzing health care industry data to make medicine more efficient and effective. What does that look like? Flagler Health’s machine-learning for health care is just one of many approaches. (Btw, if you want to go deep on how that works, check out this podcast featuring Will Hu (GED19), Flagler’s co-founder and chief technology officer).
AI in the ER?
Flagler Health illustrates that this era of AI is inspiring entrepreneurial problem-solving in the health care industry. And it is also sparking innovation in research and pioneering algorithmic tools to improve health care on a global scale.
The application of AI in health care is widespread – and a fundamental focus of the Wharton Healthcare Analytics Lab, a research center launched at the Wharton School in October 2023.
Since then, co-directors Hamsa Bastani, a Wharton professor of operations, information and decisions who develops machine learning algorithms for learning and optimization in health care; and Marissa King, a Wharton professor of health care management, have led research and discussions about where AI is being used successfully in health care and where the challenges lie.
In conversation with Eric Bradlow, vice dean of Analytics at Wharton, Dr. King helped define the health care-AI landscape. “Machine learning and artificial intelligence have touched almost all aspects of health care at this point. If you think of everything from how you get reminders to pick up prescriptions, from who’s reading your radiology reports, to even how you’re being triaged in the emergency department, machine learning plays a key role in all of those facets,” she noted. “If you think about radiology reports, that’s arguably the place where AI has had the greatest penetration. Many, many of our radiology reports are read now by machines.”
The Wharton Healthcare Analytics Lab is collaborating with different stakeholders – patients, providers and policymakers — to design better algorithms across health care. Here’s a snapshot of where Wharton-led analytics are helping to inform the new health care AI revolution:
🩺 Resource allocation refers to equitably allocating health resources in environments that struggle with access to treatment or medicines. Angel Chung, a PhD student in Wharton’s Operations, Information and Decisions Department, is working with the Sierra Leone government in West Africa to use machine learning and optimization for essential medicine distribution across thousands of health facilities in the country. In a profile of her work published by the Wharton AI and Analytics Initiative, Angel said that 40% of patients were being turned away without receiving the medicines they needed. “We used a synthetic difference-in-differences model to evaluate the impact of our approach,” she said. “Our result shows around 20% improvement in people’s access to essential medicines and medical supplies by the second quarter of 2023. While introducing an innovative change into an existing government system is tremendously difficult, we have successfully incorporated AI technology on a national scale and showcased improvement in this resource-constrained setting.”
🩺 Workforce wellbeing addresses issues affecting the health care workforce, such as burnout. Large language models (which process vast amounts of text data) provide a way to mine data from sources that haven’t been tapped before, like the clinical notes kept on patients. “One thing we’re trying to do is to utilize data from electronic health records to understand where there’s likely to be a high risk of burnout or emotional overload in clinicians, especially nurses,” said Dr. King in an article posted on Penn’s Leonard Davis Institute of Health Economics. “There’s immensely rich data within clinical notes.”
🩺 Innovative trials are an area where algorithms can drive innovations in medical practice by improving the design of trials, or research studies that test the safety and effectiveness of new medical treatments. “Clinical trials tend to be statically designed,” Dr. Bastani told Penn’s Leonard Davis Institute of Health Economics (where she is also a senior fellow). “They’re not actually personalized or dynamically customized in any way. We’ve been thinking about leveraging data from historical clinical trials or pilots to warm start these predictive models.” The Wharton Healthcare Analytics Lab is collaborating with Penn’s Health Incentives and Behavioral Economics for Better Health on this project.
🩺 Treatment and care form the heart of health-care delivery, and large-scale health systems data can help researchers identify promising treatment strategies. The Wharton Healthcare Analytics Lab says that “Big data and the use of new algorithmic methods have an enormous potential to inform everything from treating patients with substance-use disorders…to establishing best practices for human decision-making in medical settings.” Recent data-driven research has looked at everything from trends in Buprenorphine treatment in the U.S., opioid prescribing and overdose deaths, to building algorithms to help fight sex trafficking.
🩺 Health equity is a key challenge as algorithms and AI are more prevalent in the design and administration of medical care. Datasets trained by AI and machine learning systems are often biased and can compromise systems and lead to imbalances in care. For example, an algorithm might improve the outcomes for one population of people, but not others. Dr. Bastani’s research on Rethinking Fairness for Human-AI Collaboration in part addresses this issue. She regularly discusses health equity as a priority for the analytics lab.
“Any health care dataset encodes biases because patients that face structural barriers are underserved by the health system and so we don’t have high-quality representative data on them,” noted Dr. Bastani, who has talked about biased datasets often in her research. “We’ve been thinking about addressing that by leveraging proxy data – for example, cheaper but more available data like Google Search terms and so on. A big challenge of machine learning is that the training datasets are very large and so it’s not always feasible to debias the training data. But what we’re really interested in are the downstream decisions…by leveraging auxiliary sets we can never perfectly debias the model, but at least the decisions that are coming out will be less biased.”
Conversation Starters
How are the entrepreneurs at Flagler Health using AI to make an impact in health care?
What is resource allocation in health care and how is Wharton PhD student Angel Chung using AI to impact this health care challenge?
Why are datasets often biased in health care and what is being done to make them less so?
Which area of health care-AI focus do you find most compelling? Share your thoughts in the comment section of this article.
The hero image was provided by canva.com.
I resonated deeply with this article. As a self-proclaimed technophile and someone who ventures beyond the textbook to neuroscience with my own NeuroEd platform, I know how technology can enhance the quality of life experiences. But to read about Flagler Health’s efforts to take its patients’ data and apply AI to determine the most effective route for care and cost reduction taught me that this intersection of technology comes from something beyond skilled programming. It comes from caring people.
I was even more touched by the Wharton researchers, including Angel Chung, who apply machine learning to distribute essential medications across Sierra Leone, improving availability by 20%. That’s not genius programming; that’s lifesaving technology on the international scale.
From remediative suggestions on refill notifications to predictive uses before patients even step into the ER to improved radiology accuracy , AI is changing the healthcare experience for everyone involved . I’m also glad to read that the article acknowledges setbacks—data bias, burnout, patient privacy—allowed for within typically underserved socioeconomic communities . An approach to compassionate, ethical oversight is required to temper technological expansion with humanity.
This is important to me because I use AI within my NeuroEd platform to not only foster an academically driven environment but also champion mental wellness and equity of access for my colleagues and myself. Using the health-oriented wellness components from this article as inspiration, I will forever strive to apply such deliberate purpose to EdTech or HealthTech one day through compassion and ethics.
I completely agree, Suriya—empathy fuels technology’s effect, not just code. Your comment speaks to the human element of AI creativity.
Expanding on that: what would happen if AI projects included a community advisory committee—a handful of real users who review model output in real-time and flag issues? In my work at a nonprofit, stakeholders reviewing campaign drafts allowed us to catch messaging misfires early. Would a feedback loop add ethical review to AI pre-deployment?
This article’s exploration of AI in healthcare sparked a cascade of reflections, bridging the future of technology with generations of lived experience. As I read about AI’s expanding role in healthcare—from the precision of burnout detection to Angel Chung’s equity-driven work in Sierra Leone—I kept coming back to one central question: Who gets to be represented in the data? Dr. Bastani’s insight that “health care datasets encode biases” resonated deeply. It reminded me of my own experience with the underserved, particularly seniors.
I live with my grandmother, a Chinese immigrant whose relationship to healthcare is rooted in trust, intuition, and community—not in wearable tech or digital tracking. She doesn’t log symptoms online or search diagnoses on Google. Yet her health, like anyone’s, deserves visibility and care. But if AI models are trained on data from only the most digitally fluent patients, where does that leave people like her?
That’s where BridGEN, the intergenerational civic-tech initiative I co-founded, comes in. We teach digital literacy to seniors—helping them navigate telemedicine, manage online prescriptions, and stay connected to virtual health resources. In a society where older adults are increasingly marginalized by innovation, our work bridges the digital divide in a way that honors their wisdom while safeguarding their access to critical services. For me, this work underscored a powerful truth: healthcare transformation isn’t just about new technologies—it’s about who gets included in the future they create.
Wharton’s article affirms that AI in healthcare can’t be driven by efficiency alone. It must be rooted in representation and empathy. While patient-centered tools like Flagler Health’s algorithms are promising, they must also confront the inherent tension of biased data. What happens to those whose medical stories aren’t recorded, whose lives fall outside conventional datasets? If healthcare is shaped only by those who leave digital footprints, we risk automating exclusion at scale.
This is more than a technical issue—it’s a societal one. To build AI that truly advances care, we must interrogate what’s missing from the data and design models that reflect the full spectrum of human experience. That’s why Angel Chung’s work matters: her algorithms address structural inequity in medicine distribution, not just abstract performance metrics. And it’s why clinician burnout research matters too—because emotional labor in healthcare, especially among nurses, is too often invisible in the data and the discourse.
AI holds the potential to reshape healthcare—but only if it is built with intentionality. At BridGEN, we remind seniors that digital equity is about more than access: it’s about dignity, agency, and connection. If the future of medicine is to be just, it must be designed for those who’ve historically been left out of its narrative. Because no model can truly care if it forgets whom it’s meant to serve.
Yes, I agree with Lucia’s comment that everyone, no matter whether they are digitally fluent or not, should be represented in the integration of AI in healthcare. But, I believe that solely relying on the digital side of collecting information requires too much room for error. Yes, through google and other online medical providers is a fast and efficient way to collect data for underrepresented groups of people, especially through your startup BridGEN, other methods, such as collecting through questionnaires, telephone calls, and visits from actual clinicians will dramatically increase the effectiveness of the future AI in healthcare.
Although clinician visits uses substantially more time, effort, and money, statistically, seniors are more willing to open up and to express their symptoms and feelings more comfortably and accurately to another real person. And that human-human connection is something unreplicable by AI or through a screen. If we can strike a good balance between in person and online databases, the effectiveness of the AI will improve, with its biases slowly diminishing. In the end, no algorithm, no matter how advanced, should come at the cost of someone’s life; and no life is ever to expensive to save.
I couldn’t agree more—“health equity” is indeed the defining challenge as AI and algorithms become deeply embedded in medical care. Technology promises efficiency, but if our models are trained on narrow slices of the population, they’ll only widen gaps for underrepresented groups. What you’re doing with BridGEN—co‑creating tools with seniors who’ve been left in the “digital desert”—is exactly the kind of work we need to shift the needle toward true fairness.
In my neurodiversity research, I see how neurodivergent people are often under- or misdiagnosed. When I researched on my research project SpeakSense, a self-chargeable biosensor that helps people with communication disabilities by vocal cord movements and sign language recognition, I observed that people with communication disabilities still lack an accurate and affordable communication tool. I’ve seen a parallel issue: standard healthcare tools often leave people with disabilities or divergent situations behind because these groups are undersampled in mainstream datasets. That leads to misdiagnoses, poorly targeted interventions, and a sense of exclusion. To change that, we must intentionally design for diverse profiles and various voices.
From resource allocation to clinician burnout to algorithmic bias, every area highlighted in the Wharton piece hinges on representation and empathy. Angel Chung’s work in Sierra Leone and Dr. Bastani’s research are examples of “bringing warmth” to our algorithms.
Yet the road ahead remains long. We need healthcare institutions to embrace different voices and stronger bridges between academic research and real‑world situations. Only through this way can we let the data speak for everyone in need, regardless of their location, age, or differences.
Thank you for reminding us about who is not in the data—it’s crucial to build AI tools that truly deliver on the promise of equitable, human‑centered care.
This article highlights a deep truth: AI in healthcare isn’t algorithms—it’s redistributing human care and attention to where it matters.
The Flagler Health example is interesting, but I was especially touched by the case of Angel Chung—using machine learning to increase medication availability in Sierra Leone by 20%. That directly speaks to the ways that technology can be a bridge in low-resource environments.
My Python and AWS training showed me data pipelines are potent—but only if they are designed with equity in mind. When I worked on a project with SMS data from underbanked populations, I put fairness first by filtering language data meticulously to prevent misclassification—seeing firsthand how algorithmic bias can exacerbate inequalities.
The section on predicting clinician burnout spoke to me: Mining EHR notes for emotional cues is brilliant but also requires advanced model interpretation and continuous monitoring. My nonprofit internship grappled with data transparency vs. human review in donor reporting—a smaller-scale analogue of checks and balances for clinical AI.
This raises a fundamental question: How do labs like Wharton’s Healthcare Analytics Lab turn bias audits and open monitoring into a routine aspect of all AI projects—without slowing innovation cycles? That is: how do we build ethical governance into the DNA of AI development?
Thank you for these observations. It is clear that the future of healthcare is not just technological but ethical. If we design AI in a considered manner, it can be the greatest tool for equity in a generation.
That’s such a thoughtful comment by Hrithik! Based on his rich experience in AI world, he talks about the way we can ensure AI’s transparency. To answer his question; how do we build ethical governance into the DNA of AI development? – I think one of the ways is to always conduct bias auditing & fairness testing. This will ensure that all AI models are developed with ethics and fairness.
I agree that the use of AI in healthcare, as in Flagler Health launched by Albert Katz, comes with significant benefits such better treatment and improved efficiency. However, I also believe there are some ethical concerns and questions that must be addressed before widespread use.
One question that should be asked is; “ Is AI responsible for managing critical patient information?” As an example, in 2023, AI powered hospital systems led to the misclassification of critical patient documents and tests which led to the delay in urgent treatment. These incidents revealed the urgent need for proper oversight, and clear accountability measures. Without strict regulation and oversight structure, the integration of AI into health care would pose new risks, specifically when it comes to handling sensitive and life-saving patient data.
Additionally, while AI can help with reading radiology reports, or analyzing patient data, overreliance on these technologies could compromise human judgment. It should be reminded that AI is just a tool— not a total alternative to clinical expertise. Face to face communication that involves empathy is the fundamental part of medical treatment. Doctors often rely on the way they look or the way they sound as a means of diagnosing their symptoms, which AI can’t do right now.
In conclusion, the integration of AI in healthcare offers exciting chances for improvement. However, we must approach it with care and attention. We need to ensure patient safety, maintain the human aspects of care, and set up clear rules before fully adopting these technologies. AI should support human expertise, not replace it. Its development must prioritize ethical concerns and the well-being of patients. With proper oversight and careful implementation, we can use AI to improve modern healthcare rather than put it at risk.
I agree that the usage of AI in healthcare should be approached with care and protection. AI hallucinations are a significant problem where an AI model lacks adequate data but still produces an output, which can result in invalid information. We don’t always know how AI arrives at certain decisions, which can make it difficult for doctors to challenge or validate recommendations. In healthcare, the stakes are simply too high for such errors. Human caretaking and wellbeing is an art, not just a science. Two people with the same reports may require entirely different handling, and that comes from human understanding and empathy, something that develops after a doctor builds a real connection with a patient. I also agree with what you said about how AI could compromise human judgment. Even when working alongside a human doctor, AI may influence the doctor in the wrong direction when caring for a patient. AI can be useful for logistics, such as the distribution of medications and reading radiology reports, but its use for interpreting medical reports is messy.
This also reminded me of the incident in the movie I, Robot, where in an accident, the robots chose to rescue Del instead of the young girl. A human would have done otherwise, which shows the potential risks of unsupervised AI in life-or-death situations. One way to bridge these concerns in healthcare is to improve AI literacy among doctors. If physicians are trained to understand how AI healthcare tools work, they can better understand when to trust AI and when to rely on their own instincts.
Furthermore, as AI tools handle patient data, the risk of data breaches and misuse becomes serious. Patient records are among the most sensitive types of data, and any breach could lead to identity theft.
One thing I do think AI should be used for in healthcare is ensuring that some doctors are working for the well-being of their patients, and not just for money. I’ve recently heard of cases where medications were overprescribed because they brought doctors more profit. For example, opioids that should only be prescribed for a few days were given to patients for years, causing addiction. I think AI can play a role in overseeing independent doctors to ensure they are being ethical.
The rise of AI in many industries, including health care, is often met with fear – fear that machines will replace human jobs, that compassion and genuine empathy will be traded for code. This article gives proof that the reality is far more nuanced and hopeful. It shows that AI isn’t here to erase human roles; it’s stepping in where human limits prevail. It’s already analyzing large volumes of data, spotting patterns in cardiology maps, and optimizing resource allocation with precision and efficiency no human brain alone could achieve.
However, AI can’t yet replace what makes us human. It can’t hold a patient’s hand, read subtle emotions, or offer true empathy in difficult circumstances. The very traits that make AI powerful – its speed, objectivity, and data-driven accuracy – are also its limits. It can’t navigate the personal connections that lie at the heart of healthcare. It inherits human biases from the data it’s trained on and cannot deliver truly personalized care the way a physician can. Just as an article from the Annals of Internal Medicine by Yusuke Tsugawa et al. found, female doctors are often more effective than male doctors because of their communication skills and the trust they build with patients. It’s evident that healthcare is not just science, but also an art of human connection – something AI, with all of its capabilities, still cannot replicate.
Thus, at least for quite a while, health care will not be taken over by AI – it will be enhanced. Human beings are still essential to interpret, to communicate, and to care. Technology speaks in data; humans speak in compassion. The two will work in collaboration, not in competition, to deliver care that is both technologically advanced and profoundly human.
At the same time, the boundaries of what technology can do are evolving rapidly. Already, AI models like OpenAI’s GPT-4.5 are reported to have improved emotional intelligence, paving the way for future “emotional GenAI” systems that can match empathy and care of humans. The implications of this for health care are impossible to predict as of now, but it will no doubt improve the accessibility of healthcare. Even then however, while it is important to embrace new technologies, we must remain mindful that the differences between genuine empathy and feigned affection can make big differences to patients and their recovery.
While the future will always hold uncertainty, this article offers hope. It’s a powerful reminder that we’re moving toward a world where AI and human beings don’t just coexist but work hand in hand to create a healthier, more sustainable, and more equitable future for everyone. While technology may transform healthcare, it is humanity that will always define it.
Hello Sonya,
Your comment elegantly expresses the true tension between artificial intelligence and humans in the healthcare field, demonstrating that regardless of how efficient AI becomes, humans will still be essential to ensure personal patient care. One of the most distinctive lines in your comment was “Technology speaks in data; humans speak in compassion.” This statement fully encompassed the relationship between healthcare, artificial intelligence, and humans, and still echoes within me.
From a business perspective, your reflection of this topic refers to something that marketing professionals would call “emotional branding”. This is the idea that people don’t just care about the outcome of the situation, but also the emotional connection they would have to a product. Similarly, in this circumstance, patients do not just feel assured from feeling better, but they also need the human connection that doctors have with them to truly become recovered. However, if we rush development of AI in healthcare, this may become less and less possible. To effectively ensure that artificial intelligence is used hand in hand with human care like you mentioned in your comment, we need to establish it thoroughly and carefully.
Your insight into GenAI is incredibly fascinating, showing how AI truly can develop emotional intelligence in the long run. But as patient-care systems and hospitals continue to integrate this technology, we must ensure there are ethical standards as well. Yes, they should be able to connect with the patient on some level, but there should also be transparency in this process to create real trust and relationships.
Thank you for reminding us that the future of medical care is not just about making it advanced and efficient, but also about maintaining that genuine human connection that everyone needs. Like you said, if we do this right, hospital care won’t just be automated, it will be augmented, where both humans and artificial intelligence work together to fulfill true emotional needs.
Love,
Anvitha
Yes, Sonya, I completely agree with you. I also believe that AIs are a nice addition to patient care, but I don’t see them taking over the jobs of doctors, nurses, and other medical professionals. There are lots of aspects that we, as humans, have the capability of doing, but AI cannot, or at least not yet.
With the integration of AI into our everyday lives, the concern that AI will eventually take over our jobs is one that is held by many. We can already see this taking place in many different careers. For example, AI has taken over customer service, manufacturing, and even retail. With the improvement of technology, jobs have already been taken over by AI, reducing the need for companies to hire an actual person to work. Luckily, there have been some amount of backlash for some of these companies, with Duolingo being a prime example.
With this concern of people losing jobs to AI, another question that has arisen is: How will AI affect jobs in the hospital? I agree with the point you made, AI lacks true empathy and cannot replicate the emotions we have as people. I think an important aspect of patient care is human connection, just like you mentioned. The backbone of efficiency in healthcare is not having the most knowledgeable doctors or the most expensive equipment. Instead, it is the empathy medical professionals have for their patients, their ability to have emotions. AI do not have their own thoughts and while they can provide reassuring words to patients, they lack true empathy.
But in addition to human connection, I also believe that the human mind is irreplaceable. Yes, AI is smart and as technology advances, they will only become smarter. But there are moments in healthcare where providers are required to think quickly and make good decisions not only for better patient outcomes but also to respect decisions that are made by the patient and their family. I think this part of healthcare is irreplaceable. I don’t think that AI will reach a level where they are able to make decisions that will be in the best favor of the patient. Healthcare is such a multiple faceted dimension with several factors, I think it is unlikely that AI will be able to make these decisions, at least not anytime soon.
In addition, I find the article that you mentioned particularly interesting. I have never thought about the difference between female and male doctors and the difference they can have on how patients feel. I thought it was interesting that female doctors are often better at building trust with their patients. I read a similar article, which also proves that human connection is necessary in providing the most optimal patient care experience. I read an article by Matthew Ridd, and his research focused on the importance of knowledge, trust, and loyalty in a patient-physician relationship. It highlights how strong human connections, with feelings of trust and loyalty, are essential in optimal patient care.
While AI won’t replace physicians and other medical professionals, I do think that AI will supplement medical professionals and help hospitals become more efficient, ultimately making the patient experience better for everyone!
Thank you for articulating your thoughts on the role of AI in the healthcare sector. I agree that AI cannot simply replace humans in the doctor’s office or in the operating room. You point out that AI is deficient in certain areas such as empathy and connection, which is incredibly vital in healthcare. AI may advance and evolve to exhibit human traits, but as you pointed out, AI cannot hold a patient’s hand while they deal with a certain condition. Even if AI possesses emotional intelligence on par with human understanding, it is still simulated and not the same as if it was coming from a human. Patients know the difference between communication with a person and communication with software simulating a person.
Referencing Tsugawa’s study was a smart move to bolster your argument, and it is important to note that emotional and social skills such as communication and building trust are often overlooked in healthcare, with the focus being more on scientific approaches such as medicine and treatment.
However, we must still be cautious of AI. Despite the hopes that you address, AI is a product created by humans, and inherently it is not perfect as a result. While we discuss how AI can enhance healthcare, overreliance on AI can also be detrimental to healthcare. There may be consequences that would otherwise be unforeseeable with the integration of AI systems in healthcare, especially without proper supervision and oversight. Nevertheless, you argue that we must remind ourselves that AI is not here to compete with and replace humans, but rather integrate with human skills and assist us. Ultimately, humans assisted by AI can together provide healthcare even more effectively for the benefit of patients who need it most.
The area of AI in healthcare that I find most compelling is how AI can work alongside physicians to ease their workload, analyze physician burnout, and address the causes of this level of physician burnout nationwide. This article shares the collaboration between Penn’s Leonard Davis Institute of Health Economics and the Wharton Healthcare Analytics Lab. Through this innovative approach, AI uses large language models to analyze data from electronic health records like clinical notes that healthcare providers keep on patients to predict when healthcare providers, especially nurses, might be at a high risk for overwork or burnout. I find this extremely powerful because it shows how AI can go beyond just physical calculations and dive into the emotional side of human tendencies that many humans or even psychologists may overlook. This is extremely important because society often puts all the focus on patient care and the outcomes that a hospital can provide for the patient. However, this shift of focus completely to the patients can cause an innate failure to value the physical and mental well-being of those in charge of providing the care patients need. This kind of technology could help hospitals take care of patients from a different perspective because they can recognize patterns and take action sooner to protect their staff, which will allow their staff to take better care of their patients.
However, this also raises the question about AI ownership and responsibility. What if AI flags someone for being at risk for burnout? Who is responsible for acting on that information? Right now, AI systems aren’t held to legal accountability, so we need clear guidelines and a structure to balance the insights that AI can provide with human judgment.
This resonates heavily with me because after reading this article, I am building and have almost completed my student-led organization that creates monthly gift baskets for healthcare providers as a way that I am able to reduce burnout and appreciate. Although AI can detect patterns of stress and flag early signs for burnout, my organization works on reducing burnout from the human side by showing appreciation through physical acts and gifts. Nonetheless, I don’t see these approaches as different because I rather see this as a vision for a partnership. AI can provide hospitals with the insights drawn from the data the hospital provides, and organizations like mine try to bring emotional support, motivation, and a reminder of why these great professionals got into medicine. Love, Hope, and Passion. I believe that technology and human compassion can become a unified support system that ensures healthcare workers feel appreciated, important, and inspired. When innovation and humans work together, we not only protect the well-being of providers but we can also enhance the quality of care they give to others.