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I chose healthcare because it sits at the intersection of human wellbeing and fast-moving technology. Artificial intelligence (AI) is changing how patients are diagnosed, monitored, and treated, and those shifts already touch dentistry, surgery, radiology, and primary care. In this Padlet, I’ll give a concise overview and then walk through three concrete applications: (1) AI in diagnostic imaging, (2) AI for personalized medicine using genomics, and (3) AI-enabled patient monitoring through wearables. I’ll highlight the core technologies behind each (like machine learning and computer vision), the specific benefits they deliver, and the challenges they still face.

I picked healthcare because I’m pursuing a dental career and want to understand both the clinical opportunities and the ethical responsibilities that come with AI tools. For patients, AI promises earlier detection of disease, more precise treatment plans, and continuous, real-world monitoring. For clinicians, it can save time on routine analysis and documentation so they can focus on patient care. But AI also raises important questions about bias, transparency, data privacy, and accountability.

This Padlet previews where AI is already working well, where evidence is still developing, and what to watch next. Beyond the examples, I include a discussion of future trends (like multimodal models and edge AI in devices), ethical considerations (such as explainability and equitable access), and a broader look at societal impact (workforce shifts, privacy safeguards, and health equity). My goal is a balanced view: what AI gets right today, what it needs to prove, and how we can adopt it responsibly.

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Using artificial intelligence in radiology clinical practice

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AI models trained on large imaging datasets assist radiologists by detecting patterns linked to cancers, fractures, and other abnormalities. Technology: primarily computer vision and deep learning (convolutional neural networks) that analyze X-rays, CT, and MRI. Benefits: faster triage, consistent second-reads, and earlier detection that may improve outcomes. Challenges: variable image quality, data shifts across hospitals, the need for explainability, and rigorous clinical validation before deployment. Integration into workflow and liability concerns also matter. Overall, AI serves as an assistive “co-pilot,” not a replacement, helping experts handle heavy volumes while focusing attention on the hardest cases

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Using artificial intelligence in radiology clinical practice

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AI in healthcare affects society beyond the clinic. Employment: some tasks shift (e.g., pre-reads, summarization), but new roles emerge in data stewardship, validation, and AI operations. The priority is reskilling clinicians and staff so AI augments people rather than displacing them. Privacy: health data are uniquely sensitive; organizations need strict governance, encryption, and transparency about how data train and run models. Equity: access to AI-enhanced care can widen or narrow disparities depending on whether tools are designed, validated, and deployed across diverse communities, and whether underserved clinics can afford them.

There’s also public perception: trust rises when patients see AI improving safety (e.g., fewer missed findings) and convenience (e.g., at-home monitoring), and falls if systems are opaque or error-prone. Policy matters too: reimbursement, liability, and standards for reporting performance will shape real-world adoption.

My view: societies should measure what matters, health outcomes, patient experience, and fairness, not just algorithm accuracy. Investments in digital infrastructure and clinician education will determine whether AI amplifies the best parts of medicine: empathy, prevention, and personalized care. With careful oversight, the societal payoff can be earlier detection, fewer complications, and better access without sacrificing privacy or equity.

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Healthcare – AI for Personalized Medicine (Genomics)

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Healthcare – AI for Personalized Medicine (Genomics)

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In precision medicine, AI finds patterns in genomic and clinical data to suggest tailored treatments (e.g., oncology). Technology: machine learning for feature selection and risk modeling; NLP to mine clinical notes and literature. Benefits: matching therapies to tumor profiles, predicting drug response, and identifying patients who may benefit from trials. Challenges: data privacy and consent for genetic information, biases if datasets under-represent certain groups, and translating model predictions into clear, clinician-approved decisions. Governance, validation, and multidisciplinary oversight are crucial so these tools add value without over-promising.

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Healthcare – AI-enabled Patient Monitoring & Wearables

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Healthcare – AI-enabled Patient Monitoring & Wearables

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Wearables and home devices stream vital signs (heart rate, rhythm, sleep, activity). Technology: on-device and cloud ML detect anomalies (e.g., arrhythmias) and trend changes. Benefits: continuous monitoring outside the clinic, earlier warnings, and more personalized coaching for chronic conditions. Challenges: false positives that cause alarm fatigue, ensuring accuracy across skin tones and body types, battery/data costs, and protecting sensitive health data. Clinically, these insights need to flow into electronic records in ways providers can act on. With good design and oversight, AI monitoring can extend care between visits and support preventive health.

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AI-Driven Wearable Technology: Enhancing Patient Outcomes and Neurodiversity Support

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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Over the next few years, healthcare AI will push toward multimodal models that combine imaging, text notes, labs, and genomics into single predictions. We’ll see more edge AI in devices (processing data locally) to improve privacy and latency, and more human-AI collaboration features built directly into clinical software. Regulatory expectations will likely formalize “continuous learning” life-cycles and demand robust post-market monitoring.

Ethically, three issues matter most. First, bias and equity: models trained on unrepresentative data can underperform for certain populations. Addressing this means diverse datasets, subgroup performance reporting, and targeted mitigation. Second, transparency and explainability: clinicians and patients need clear model rationales or at least reliable confidence and uncertainty cues to support informed decisions. Third, data governance and privacy: consent, de-identification, on-device processing, and minimal-data designs help protect patient trust.

My perspective: AI’s benefits are real, but evidence should drive adoption. Tools must show clinical utility (they change decisions and outcomes), safety (reliable across settings), and workflow fit (save time rather than add clicks). Institutions should implement bias testing, model monitoring, and escalation policies. When patients are informed partners, and clinicians remain accountable, AI can responsibly extend high-quality care.

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I began with broad reading, then narrowed to three healthcare use-cases where evidence and clinical relevance are strong. The most useful step was comparing benefits with real constraints (data quality, bias, validation). Embedding videos and images helped me explain core technologies in plain language. My main challenge was balancing enthusiasm with caution; I addressed it by emphasizing clinical utility, safety, and workflow fit. Overall, this project clarified that AI can extend quality care if we prioritize transparency, equity, and patient trust. I now feel more confident evaluating claims and thinking about how to adopt AI responsibly in clinical settings

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Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
— Rationale: Clear, accessible overview of how AI can augment clinicians and patient care.

Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
— Rationale: Concise review of medical AI methods with strengths/limits across specialties.

Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. (2022). AI in healthcare: the road to deployment. Nature Medicine, 28(12), 2384–2394.
— Rationale: Practical discussion of validation, generalization, and monitoring in clinical AI.

U.S. FDA. (2021). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD): Action Plan.
— Rationale: Regulatory perspective on safety, transparency, and post-market oversight.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. NEJM, 375(13), 1216–1219.
— Rationale: Early but influential framing of opportunities and pitfalls for ML in medicine.

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of AI technologies in medicine. Nature Medicine, 25(1), 30–36.
— Rationale: Implementation-focused paper on workflow, data, and governance.