Artificial intelligence is no longer just a technical tool, but a moral crossroad in modern healthcare. Najib Tasleem, Master of Management Analytics student at Smith School of Business argues that while AI can enhance diagnostics and efficiency, it must be guided by human judgment, empathy, and equity. The real question is not whether AI will transform medicine – but whether we will ensure it does so for the common good.

Healing with Intelligence: AI, humans, and the pursuit of the common good in healthcare by Najib Tasleem.

A Nodule Spotted; a Life Saved?

As a radiologic technologist, while performing a routine chest x-ray on a patient, I initially overlooked what was a dark density on her x-ray and discharged the patient. Upon further review of her images, I noticed the dark density but was unsure if it was internal to the patient or an artifact (something external to the patient which can be removed). This density resembled a lung nodule. Still uncertain, I called the patient back and asked if she had anything in her pocket.

Upon receiving confirmation that she did have something inside her pocket, the chest X- ray was repeated. The dark density was no longer present on the new image. This made me reflect on a troubling possibility: if I had not noticed the dark density and repeated the exam, the radiologist would have misdiagnosed this as a lung nodule (possibly indicating early stages of lung cancer). Adverse patient safety events like this are not uncommon in healthcare. As a

a student of artificial intelligence, this got me thinking. Could AI be trained to detect these types of mistakes and prevent such safety events. How do we, as humans, interact with artificial intelligence in ways that promote the common good in healthcare where decisions can have life altering consequences.

The Rise of AI in Healthcare: Blessing or Burden?

AI has made great strides across various industries. In healthcare, it is being used in predictive analytics to track virus spread like COVID19. AI can be trained to read thousands of radiology images and detect abnormalities with high precision. The benefits are enormous. CT scans, MRIs, and X-rays that took a significant amount of time to interpret, can now be read in mere seconds. This leads to quicker diagnosis and treatment and improves patient outcomes (Topol, 2019).

The implementation of AI in healthcare presents a dilemma. What happens when a machine outperforms a human? Who bears responsibility if the AI makes a mistake? Can we trust AI with decisions that demand not only intelligence but compassion? Studies have shown that while AI can support decision-making, the optimal collaboration between AI and medical professionals remains an area of ongoing research (Jha & Topol, 2016; Gaube et al., 2021).

As a front-line healthcare worker, I have worked at the intersection of patient centered care and medical imaging science. I have seen how AI tools can assist in flagging critical findings, suggesting protocols, and improving department workflow. AI cannot, however, replace the judgement, experience, and context driven decisions made by clinicians.

AI and Healthcare: Empathy Cannot Be Coded

Machine learning models can analyze large data sets, but they cannot feel. An algorithm can be coded to detect a tumor, but it cannot explain a life altering diagnosis with sensitivity to a patient and their loved ones. AI can suggest a treatment, but it cannot hold a patient’s hand. The emotional, cultural awareness, and moral judgment required in healthcare are inherently human (Chen & Asch, 2017).

Returning to my example earlier, could AI have distinguished that the dark density on the patient’s lung chest x-ray was an external artifact? Would it have misdiagnosed it as a lung nodule? My own clinical intuition, brought about by years of experience, led me to believe that the dark density was not a lung nodule. It allowed me to follow my instincts and call the patient back to have the imaging repeated. No algorithm could have made the same call. AI is a good tool to use, but it cannot replace human experience.

Augmentation, Not Automation

The role of AI is not to replace healthcare providers, but to empower them. If implemented properly, AI can act as a powerful partner that assists in reducing diagnostic errors. This will allow healthcare providers to focus on the most important thing: patient centered care.

In imaging departments, for example, AI can pre-analyze scans and highlight potential areas of concern, which radiologists then review with their expertise. This human-in-the-loop model ensures that the final judgment incorporates both computational precision and clinical insight (Davenport & Kalakota, 2019). It also helps reduce burnout by streamlining administrative burdens and repetitive tasks. Rather than viewing AI as a threat to autonomy, healthcare professionals should be equipped to see it as an extension of their capabilities, a second set of eyes, not a replacement for their own.

Nowhere is this more evident than in the administration of contrast media for CT scans (x-ray dye). When x-rays attenuate through the different anatomical structures of the patient’s body, they produce different shades of grey on the display screen. Being able to distinguish these shades of grey is what a radiologist tries to do, as it is central to accurate diagnosis. However, sometimes adjacent anatomical structures appear the same shades of grey. Contrast media helps clarify these differences, making pathologies like cancer more visible.

But not all patients can receive contrast due to severe allergic reactions. AI can play a pivotal role in helping to distinguish subtle differences in grey shades on CT scans, potentially eliminating the need for contrast in some cases (Hussein et al., 2021). This could ensure that patients who cannot tolerate contrast media still receive accurate diagnoses and appropriate care.

Bias, Inequality, and the Role of Leadership

Despite all its benefits, AI in healthcare is not perfect and has many downsides. AI is dependent on data. If the data is skewed or non-representative, AI systems can perpetuate existing health disparities. Diagnostic algorithms could perform less accurately for certain ethnic groups due to biased training datasets.

Ensuring equity in AI requires diverse and inclusive data collection, transparent algorithms, and rigorous human oversight. This is where good leadership is crucial, both in healthcare institutions and policymaking bodies.

Healthcare administrators, clinicians, and AI ethicists must collaborate to set standards that prioritize fairness, inclusivity, and accountability. Building AI for the common good means embedding ethics and equity into every stage of design, deployment, and evaluation.

AI and Healthcare: A Future Worth Healing

We are at a pivotal moment in history. AI has the capacity to redefine healthcare in ways never been done before. It can make it more efficient, accurate, and responsive. But the true measure of progress lies not in technological capability, but in human-centered outcomes.

When we design AI systems that support, not replace human professionals, and when we ensure they serve the needs of all patients, not just the privileged few, we advance toward a future that is both intelligent and compassionate.

The question is not whether AI will shape the future of healthcare. It already is. The real question is: will we guide it to heal, or will we let it divide?

References used in this article:

• Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine—Beyond the peak of inflated expectations. The New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/NEJMp1702071
• Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare.
• Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
• Hussein, R., Asif, M., & Ali, H. (2021). Artificial intelligence applications for radiology: The future is bright. Journal of Clinical Imaging Science, 11(1), 3.https://doi.org/10.25259/JCIS_240_2020
• Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 316(22), 2353–2354.
• https://doi.org/10.1001/jama.2016.17438
• Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Useful links:

• Link up with Najib Tasleem on LinkedIn
• Read a related article: E-Referrals: What is the impact on South Africa’s healthcare ecosystem? 
• Download this and other student articles in the special issue Global Voice #32 magazine
• Discover Smith School of Business, Queen’s University Canada
• Apply for a Smith MBA.

Learn more about the Council on Business & Society

The Council on Business & Society (CoBS), visionary in its conception and purpose, was created in 2011, and is dedicated to promoting responsible leadership and tackling issues at the crossroads of business, society, and planet including the dimensions of sustainability, diversity, social impact, social enterprise, employee wellbeing, ethical finance, ethical leadership and the place responsible business has to play in contributing to the common good.  

• Follow the CoBS on LinkedIn
• Download magazines and learning content from the CoBS website downloads page.

Member schools of the Council on Business & Society.

• ESSEC Business School, France, Singapore, Morocco
• FGV-EAESP, Brazil
• School of Management Fudan University, China
• IE Business School, Spain
• Indian Institute of Management Bangalore, India
• Keio Business School, Japan
• Monash Business School, Australia, Malaysia, Indonesia
• Olin Business School, USA
• Smith School of Business, Queen’s University, Canada
• Stellenbosch Business School, South Africa
• Trinity Business School, Trinity College Dublin, Ireland
• Warwick Business School, United Kingdom.