AI Applications in Medicine: Opportunities and Challenges

~Understanding How Artificial Intelligence is Transforming Healthcare~

Artificial intelligence (AI) is increasingly becoming part of modern healthcare. In certain well-defined tasks, carefully validated AI can match or even exceed clinician performance; in others, it speeds up workflows or helps extend access to care [1–3].

While news headlines can be confusing, it helps to know what AI actually does for your care. From analyzing medical images to predicting health risks, AI tools are being integrated into many parts of medicine, bringing exciting possibilities and important considerations for patients.

What is AI?

Think of AI as a sophisticated assistant that can process very large amounts of health information and spot patterns that may be hard for humans to see.

In healthcare, AI refers to computer programs that can analyze medical images, summarize research, flag risks from electronic health records, and support doctors in making diagnoses and treatment plans [3].

Importantly, AI does not replace your doctor; it works alongside healthcare professionals to enhance their capabilities and help them provide better care.

Benefits for Patients

• Faster results: AI triage can prioritize urgent studies and streamline workflows, helping clinicians act sooner in time-sensitive conditions like stroke [6].
• Strong performance in specific tasks: Examples include certain breast imaging and diabetic-retinopathy screening scenarios, where AI supports clinicians as a second set of “highly trained eyes” [4–5].
• Improved access (with care): When designed and evaluated responsibly, AI-enabled tools can help extend specialist-level capabilities to underserved settings (for example, by automating screening and triage) [3,15].

How is AI Currently Being Used?

Medical Imaging and Diagnosis

AI is most mature in image analysis. For example, a large international study found an AI system for breast cancer screening that reduced both false positives and false negatives compared with radiologists on retrospective datasets [4].

In eye care, an FDA-cleared system for diabetic retinopathy achieved high performance (about 90% sensitivity at ~98% specificity) in validation studies, reliably flagging patients who need referral [5].

In emergency care, AI tools can help identify signs of stroke and alert teams, which in some implementations has been linked to faster key steps along the treatment pathway; important because “time is brain” [6].

Personalized Treatment Planning

Every patient is unique. By combining your medical history, imaging, and sometimes genetic information, AI models can help doctors estimate prognosis and consider which treatments might work best for you.

This is especially active in cancer care and cardiology; these tools are promising but still require prospective validation and careful guardrails before routine, stand-alone use [7–9].

Early Detection and Prevention

AI models trained on routine health records can improve cardiovascular risk prediction compared with traditional calculators; results vary by dataset and must be tested in new populations [10].

On the consumer side, the Apple Heart Study (over 400,000 participants) found that when the watch flagged an irregular pulse, the positive predictive value for atrial fibrillation was 0.84 when confirmed with ECG [11]. In other words, out of the individuals who had an irregular pulse, 84% of them ended up being diagnosed with atrial fibrillation ~ an irregular heartbeat (arrhythmia).

In hospitals, sepsis-detection systems such as TREWS have been associated with earlier antibiotics and lower death rates when embedded into clinical workflows, showing what’s possible when AI is tightly integrated with teams [12]. TREWS is used to analyze a patient’s electronic health records in real time to detect early signs of sepsis ~ a life threatening response to an infection that damages a persons own tissues and organs.

Important Considerations

Privacy and Data Security (Canada)

AI systems need data to work. In Canada, privacy laws such as PIPEDA (and provincial health-privacy statutes) give you rights to know how your information is collected, used, and protected, and to request access and corrections [16]. Health Canada also provides guidance for machine-learning medical devices, emphasizing safety, transparency, and post-market monitoring [19].

Questions you can ask:

• How is my data stored and who has access?
• Is my data de-identified, and what security measures are in place?
• Can I opt out of certain AI uses?

Accuracy and Bias

No technology is perfect. AI systems can inadvertently perpetuate disparities if trained on skewed data [17]. Best practice includes diverse training data, ongoing human oversight, and regular audits for bias and errors.

The Human Element

Medicine is about more than data. Patients value the option to involve a human decision-maker, and professional bodies emphasize that AI should enhance, not replace, clinician judgment. Clinicians remain accountable for your care and for how AI is used in that care [18,23].

What You Should Know as a Patient

Questions to Ask Your Healthcare Provider

• Is AI being used in my diagnosis or treatment planning?
• What are the benefits and limitations in my situation?
• Who makes the final decisions about my care?
• Are non-AI options available if I prefer?

Your Rights (Practical Takeaways in Canada)

• Information & access: You can ask how decisions were made, what data were used, and request access or corrections to your information [16,19].
• Transparency & consent: Expect clear explanations about AI-enabled tools used in your care.
• Human oversight: You can request that a clinician review and discuss AI recommendations; clinical accountability and human oversight remain the standard of care [23].

Looking Ahead

Analysts estimate the global medical-AI market could approach ~US$188 billion by 2030, reflecting rapid growth – though rigorous evaluation and regulation must keep pace [20].

In drug discovery, dozens of AI-generated candidates have reached early clinical testing, but results are still emerging and none has yet completed phase 3 trials (a research study that includes large numbers of patients) [21]. Surgical robotics continues to advance toward very fine, sub-millimeter tasks; typically, with close human control and oversight [22]. The goal isn’t to replace doctors, but to enhance their capabilities while preserving the human side of medicine [23].

AI in medicine is a powerful tool already improving parts of care. It should help your healthcare team work with you: faster where speed matters, more personalized where data help, and always with attention to privacy, safety, and equity. Your questions, values, and preferences remain central.

Expert Contributor

Gavin Thomas, MSc, MD Student, University of Calgary

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