AI in Healthcare 2026: Diagnosis, Treatment, and Beyond

The first time I watched an AI flag a potential tumor that three radiologists had missed, I realized we’d crossed a threshold. This wasn’t science fiction anymore—this was Tuesday morning at a hospital using technology that’s now available to healthcare systems worldwide.

AI in healthcare has moved from experimental curiosity to essential infrastructure. The transformation happening in 2026 isn’t gradual; it’s accelerating. Hospitals that treated AI as a “someday” technology two years ago are now racing to implement it across their operations.

But here’s what I find most interesting: the conversation has shifted. We’re no longer asking whether AI belongs in healthcare. We’re asking how to implement it responsibly, fairly, and effectively. That’s a fundamentally different—and more productive—question.

This guide covers the real state of AI in healthcare today. Not theoretical possibilities, but actual applications, genuine challenges, and practical considerations for everyone from healthcare administrators to patients wondering what this means for their care.

The AI Healthcare Revolution: Where We Are in 2026

From Pilots to Enterprise Deployment

Something changed between 2024 and 2026. Healthcare AI moved from isolated pilot programs—often funded by innovation grants and managed by enthusiastic but overstretched champions—to systematic, enterprise-wide deployment.

The major Electronic Health Record (EHR) providers now offer native AI capabilities. Epic, Cerner, and others have built AI directly into their platforms rather than requiring clunky third-party integrations. This shift makes AI practical for hospitals that don’t have dedicated AI teams (which is most of them).

I’ve seen the difference this makes firsthand. When AI requires special infrastructure, special training, and special maintenance, adoption stays low. When it’s just another feature in the system clinicians already use, adoption becomes natural.

The Numbers That Tell the Story

The AI healthcare market is projected to reach $45-56 billion by 2026, up from under $5 billion in 2020. That’s not incremental growth—that’s a transformation.

More telling than market size is adoption. According to recent reports, 90% of hospitals are now using AI-powered technology for early diagnosis or remote patient monitoring. As of January 2026, 27% of U.S. health systems are paying for commercial AI licenses—triple the adoption rate across the broader economy.

Large hospitals are 1.48 times more likely to adopt AI than smaller facilities, which creates both opportunity and concern. The benefits shouldn’t be reserved for patients lucky enough to live near major medical centers.

Why Now? The Convergence of Factors

Several forces converged to make 2025-2026 the inflection point:

Data availability: Healthcare finally has enough digitized data to train effective AI models. Decades of electronic health records now provide the foundation.

Compute power: The processing capability needed for medical AI became affordable for healthcare budgets.

Workforce crisis: Physician burnout and nursing shortages created urgent demand for tools that reduce administrative burden.

Proven ROI: Early adopters demonstrated clear returns—reduced errors, faster diagnoses, lower costs—giving late adopters confidence to invest.

Regulatory clarity: The FDA’s framework for AI/ML medical devices matured, providing clearer pathways to deployment.

AI Applications Transforming Patient Care

Medical Imaging and Diagnosis

This is where healthcare AI has achieved its most visible wins. AI algorithms analyzing X-rays, MRIs, CT scans, and ultrasounds now detect abnormalities—tumors, fractures, hemorrhages—with accuracy often exceeding 90%.

In some studies, AI matches or outperforms radiologists in specific diagnostic tasks. But the more important story is collaboration: radiologists using AI catch more problems than either humans or AI working alone.

The technology has evolved beyond simple detection. Modern medical imaging AI orchestrates the entire diagnostic journey: prioritizing worklists so urgent cases get seen first, providing structured reporting, and flagging incidental findings that might otherwise slip through.

For patients, this means faster results and fewer missed diagnoses. For radiologists, it means focusing their expertise where it matters most rather than drowning in routine scans.

Predictive Analytics and Early Warning

Perhaps the most consequential AI applications are the ones patients never see: systems that predict problems before they become emergencies.

AI now identifies patients at high risk for sepsis, falls, cardiac events, or readmission—often 24-48 hours before clinical signs become obvious. This shifts healthcare from reactive treatment to proactive prevention.

A hospital system I’ve observed reduced sepsis mortality by 18% after implementing predictive alerts. Nurses receive warnings when vital sign patterns suggest deterioration, allowing intervention before crisis.

This isn’t replacing clinical judgment; it’s augmenting it. Experienced clinicians often develop intuition about which patients seem “off.” AI quantifies and systematizes that intuition, applying it consistently across thousands of patients around the clock.

Personalized Treatment Plans

Genomic medicine used to require weeks of specialized analysis. AI can now interpret genetic testing results in hours, providing pharmacogenomic guidance for medication selection and informing cancer treatment based on tumor genetics.

This matters because patients are not averages. A medication that works well for most people might be ineffective or dangerous for someone with particular genetic variants. AI-powered personalization means treatments tailored to individual biology rather than population statistics.

The technology extends to dosing optimization, treatment sequencing, and combination therapy selection. For conditions like cancer, where treatment decisions are literally life-and-death, AI-assisted personalization represents a genuine leap forward.

Remote Patient Monitoring

Wearables and implantable sensors now generate continuous physiological data that would have overwhelmed human reviewers a decade ago. Edge AI processes this data locally, identifying patterns that warrant clinical attention while filtering out noise.

Patients with chronic conditions—heart failure, diabetes, COPD—can be monitored continuously without constant hospitalization. When values drift toward danger, clinicians receive alerts. When values remain stable, everyone gets peace of mind.

I find the patient experience angle compelling here. Being monitored by AI sounds cold, but the alternative was often being monitored by nobody between appointments. For many patients, AI surveillance means safer freedom. For more on this topic, see my overview of AI agents and their capabilities.

AI in Clinical Operations

Ambient Documentation and Clinical Notes

Ask any physician what they hate most about modern healthcare, and documentation frequently tops the list. Hours spent typing into EHRs means hours not spent with patients—and contributes directly to burnout.

Ambient listening technology changes this equation. AI systems now listen to patient encounters (with consent), transcribe the conversation, extract relevant clinical information, and generate structured notes.

The physician reviews and approves rather than types. Early adopters report saving 1-2 hours daily on documentation. That’s time returned to patient care or, crucially, to rest.

Major EHR vendors have integrated ambient documentation natively, meaning the technology no longer requires complex implementation. For healthcare systems struggling with physician satisfaction, this might be the highest-impact AI investment available.

Clinical Decision Support Systems

AI-powered Clinical Decision Support Systems (CDSS) provide real-time assistance during patient encounters. They analyze patient data, suggest diagnoses to consider, flag potential drug interactions, and recommend evidence-based treatment options.

Good CDSS implementations share a common trait: they offer guidance without demanding attention. Clinicians can follow suggestions or dismiss them. The AI assists without obstructing workflow.

Poor implementations—and they exist—interrupt constantly with low-value alerts, training clinicians to click past everything. This “alert fatigue” undermines the entire purpose of decision support.

The best current systems learn from physician responses, gradually becoming more accurate and less intrusive. For discussions of how large language models enable this adaptive behavior, the technical foundations are worth understanding.

Administrative Automation

Healthcare administration is shockingly inefficient. Scheduling, billing, prior authorizations, insurance verification—each involves repetitive tasks that consume staff time and patience.

AI automates substantial portions of this work. Natural language processing extracts structured data from clinical notes for billing. Intelligent scheduling optimizes appointment allocation. Chatbots handle routine patient inquiries.

The financial case is straightforward: reduced administrative cost per patient. But the quality case matters too. Staff freed from repetitive tasks can handle complex situations that genuinely require human judgment and empathy.

Drug Discovery and Development

Beyond clinical care, AI is transforming how medications reach patients. Drug discovery traditionally required decades and billions of dollars. AI accelerates this timeline by predicting molecular behavior, identifying promising compounds, and optimizing trial design.

Generative AI now creates novel molecular structures with predicted therapeutic properties. This doesn’t replace laboratory validation—compounds still need testing—but it dramatically narrows the search space.

COVID-19 demonstrated the urgency. Vaccine development that would have taken years instead took months, partly enabled by AI-accelerated research. The same approaches now target cancer, Alzheimer’s, and rare diseases.

The Human Side: Doctors, Nurses, and AI

AI as a Clinical Partner, Not Replacement

Let me be direct about something: AI won’t replace doctors. This concern, while understandable, misunderstands what AI does well and what requires human judgment.

AI excels at pattern recognition across vast data, consistent application of rules, and tireless attention. Humans excel at handling ambiguity, integrating personal and social context, and building therapeutic relationships.

The winners won’t be AI systems that work alone. They’ll be healthcare teams that effectively combine human expertise with AI capabilities. AI handles the data; humans handle the patient.

That said, here’s my honest take: doctors who refuse to use AI will increasingly compete with doctors who embrace it. The enhancement is significant enough that adoption becomes competitive necessity.

Addressing Burnout and Workforce Shortages

Healthcare faces a workforce crisis. Physician burnout exceeds 50% in many specialties. Nursing shortages force impossible patient ratios. This isn’t sustainable.

AI directly addresses contributing factors. Reduced documentation burden gives clinicians time back. Automated administrative tasks reduce frustration. Decision support decreases cognitive load of tracking everything manually.

None of this fixes systemic issues like inadequate staffing ratios or misaligned incentives. AI is a tool, not a solution to healthcare’s structural problems. But it’s a tool that can meaningfully improve clinician experience within existing constraints.

The Skills Healthcare Workers Need Now

Healthcare education is adapting to AI’s arrival. Tomorrow’s clinicians need:

AI literacy: Understanding what AI can and cannot do, how to evaluate AI recommendations, and when to override suggestions.

Data interpretation: Working with probabilistic assessments rather than binary answers.

Ethical reasoning: Navigating AI bias and equity considerations that AI systems may inherit or amplify.

Human skills: Paradoxically, AI makes interpersonal skills more valuable. Empathy, communication, and relationship-building become differentiators when machines handle data.

Current healthcare workers need similar upskilling, though many report learning on the job as AI tools appear in their workflows.

Challenges and Concerns

Data Privacy and Security

Healthcare data is extraordinarily sensitive. Medical histories, genetic information, mental health records—breaches carry serious consequences for patients.

AI requires data to function. Training models needs large datasets. Real-time inference accesses patient records. This creates tension between AI capability and privacy protection.

Healthcare organizations address this through de-identification, encryption, access controls, and federated learning (where AI models learn from data without data leaving its source). These approaches work, but they require consistent investment and vigilance.

Patients have legitimate questions about who accesses their data and how AI systems use it. Healthcare organizations that communicate clearly about data practices build trust; those that don’t face deserved skepticism.

AI Bias in Healthcare

AI systems can perpetuate or amplify existing healthcare disparities. Training data that underrepresents certain populations produces AI that performs worse for those groups.

This isn’t hypothetical. Studies have found diagnostic AI that performs differently across racial groups, risk prediction algorithms that disadvantaged Black patients, and other systematic inequities embedded in supposedly objective systems.

Addressing this requires intentional effort: diverse training data, bias testing before deployment, ongoing monitoring of real-world performance across populations. It requires asking who benefits from AI and who might be harmed—questions that determine whether technology advances equity or undermines it.

Regulatory Landscape

The FDA now regulates AI-enabled medical devices through an evolving framework that distinguishes between different risk levels and adaptation approaches. Devices that learn and change post-deployment face particular scrutiny.

Internationally, regulatory approaches vary. The WHO issued guiding principles for AI in health, but implementation differs across countries. Understanding the broader AI regulatory landscape helps contextualize healthcare-specific requirements.

Healthcare organizations navigating this landscape need regulatory expertise and the resources to maintain compliance as rules evolve. This creates barriers for smaller organizations and may concentrate AI adoption among larger systems.

Implementation Hurdles

Even when technology works, implementation often struggles. Healthcare organizations face:

Integration challenges: AI must work with existing EHR systems, clinical workflows, and infrastructure.

Change management: Clinicians resistant to new tools can undermine AI effectiveness regardless of technical capability.

Cost and resources: Initial investment, ongoing maintenance, and training require sustained commitment.

Expectation management: AI works probabilistically; it won’t be right every time. Organizations must communicate this reality.

For guidance on practical implementation, resources on AI strategy implementation offer frameworks applicable beyond healthcare.

Real-World Success Stories

Diagnostic Breakthroughs

Mount Sinai’s AI system for COVID-19 detection achieved 84% accuracy from chest CT scans, providing rapid triage when PCR testing was overwhelmed. The AI didn’t replace testing—it prioritized patients and accelerated care.

Mayo Clinic’s AI detects low ejection fraction from electrocardiograms with 93% accuracy—identifying heart weakness from a simple 10-second test that costs almost nothing.

These aren’t isolated examples. Healthcare AI has accumulated successes across specialties: dermatology (skin cancer detection), ophthalmology (diabetic retinopathy), pathology (cancer cell identification), and many more.

Operational Efficiency Gains

Cleveland Clinic reduced patient wait times by 30% using AI-powered scheduling that predicts no-shows and dynamically adjusts appointments.

Johns Hopkins implemented sepsis prediction that reduced mortality and length of stay, generating ROI within months of deployment.

These operational improvements compound. Lower mortality improves outcomes. Shorter stays increase capacity. Reduced readmissions cut costs. Healthcare AI isn’t just technically impressive—it delivers measurable value.

Patient Experience Improvements

AI chatbots now handle routine patient inquiries—appointment scheduling, prescription refills, basic symptom triage—around the clock. Patients get immediate responses rather than hold music.

Patient navigation systems help people find appropriate care, directing urgent situations to emergency rooms while routing minor concerns to virtual visits. This sorting improves experience for everyone.

Communication tools maintain engagement between visits through automated check-ins and educational content personalized to each patient’s conditions. Healthcare extends beyond the walls of clinical facilities.

What Patients Need to Know

When AI Assists Your Care

AI is probably already involved in your healthcare, even if nobody mentioned it. Imaging analysis, risk prediction, scheduling optimization—these operate in the background at many healthcare facilities.

You have the right to know if AI systems contribute to decisions about your care. Some facilities disclose this routinely; others don’t. If you’re curious, ask directly.

Generally, AI assists rather than decides. A human physician remains responsible for your care. AI provides information; clinicians interpret and act on it.

Your Rights and Questions to Ask

Consider asking your healthcare provider:

“Was AI involved in my diagnosis or treatment recommendations?”
“How accurate is this AI system across different patient populations?”
“What happens if I prefer not to have AI involved in my care?”

You typically have the right to refuse AI involvement, though this may limit certain services or require alternative workflows. Trade-offs exist.

Regarding data, ask how your information is used for AI training, whether it’s de-identified, and what controls exist. Answers vary by organization; informed patients get better information.

The Future Patient Experience

AI is gradually making healthcare more accessible, personalized, and proactive. Virtual consultations expand access. Wearable monitoring enables continuous care. Personalized treatment matches interventions to individual biology.

Some changes improve nearly everyone’s experience. Others create concerns—privacy, algorithmic bias, the feel of being processed rather than cared for. Healthcare organizations that listen to patient concerns will implement AI more successfully than those that don’t.

Your voice matters. Patients who provide feedback shape how AI develops in healthcare.

Getting Started: Healthcare AI Implementation

For Healthcare Administrators

Organizations considering AI should:

Start with clear problems: The best AI projects solve specific, measurable problems. “Implement AI” isn’t a strategy; “Reduce diagnostic turnaround by 20%” is.

Assess readiness: Data quality, integration capability, change management capacity—these determine success more than AI selection.

Evaluate vendors rigorously: Request validation studies relevant to your population. Ask about bias testing. Understand total cost of ownership.

Plan for governance: Who approves AI recommendations? Who monitors performance? How do you handle failures?

Key Success Factors

Successful implementations share common elements:

Executive sponsorship: AI requires organizational commitment, not just technical enthusiasm.

Clinical champions: Physicians and nurses who advocate for AI adoption accelerate peer acceptance.

Realistic expectations: AI performs statistically, not perfectly. Organizations expecting infallibility will be disappointed.

Iterative approach: Start small, demonstrate value, expand. Big-bang implementations frequently fail.

Common Pitfalls to Avoid

Buying solutions looking for problems: AI should address real needs, not technology for technology’s sake.

Underestimating integration: Making AI work with existing systems often costs more than the AI itself.

Neglecting training: Clinicians need support to use AI effectively. Budget for education.

Ignoring equity: Test across populations before broad deployment. Address bias proactively.

Emerging Healthcare AI Technologies

Beyond the established applications, several emerging technologies are poised to reshape healthcare in the coming years.

Multimodal AI Integration

The next generation of healthcare AI doesn’t just analyze one type of data—it integrates multiple modalities. A multimodal system might simultaneously consider medical images, lab results, clinical notes, genetic data, and patient-reported symptoms to generate more comprehensive insights.

This matters because clinical decision-making rarely relies on single data sources. A physician considering a diagnosis draws on years of training, multiple test results, patient history, and subtle observations during the exam. Multimodal AI begins to approximate this holistic approach.

I’ve seen early implementations that combine radiology images with pathology slides and clinical notes, enabling diagnostic suggestions that consider the full clinical picture rather than narrowly analyzing isolated data points.

Foundation Models for Healthcare

Large language models trained specifically on medical literature and clinical data are emerging as foundation models for healthcare AI. These models can understand medical terminology, interpret clinical notes, and generate medically-appropriate responses.

Unlike general-purpose AI, healthcare foundation models are trained with the specific requirements of medical practice in mind. They understand that certain symptoms require urgent attention, that medication dosing matters enormously, and that hedging in recommendations differs from hedging in casual conversation.

Organizations like Google, Microsoft, and specialized healthcare AI companies are developing these models, with debate continuing about centralized versus federated approaches to training on sensitive medical data.

Surgical AI Assistance

While fully autonomous robotic surgery remains limited, AI assistance during surgical procedures is advancing. AI systems can provide real-time guidance, identify anatomical structures, flag potential complications, and help optimize surgical approaches.

This is particularly valuable in minimally invasive procedures where surgeons work with limited visibility. AI can process camera feeds to highlight critical structures that might be difficult to distinguish in surgical environments.

The collaboration model matters here too. AI assists the surgeon; it doesn’t replace surgical judgment. The best implementations enhance human capability while maintaining human control and accountability.

Mental Health AI Applications

AI is increasingly addressing mental health—an area with massive unmet need and significant workforce shortages. Applications range from symptom monitoring and crisis detection to therapeutic interventions and treatment optimization.

AI-powered apps can provide between-session support for therapy patients, detecting concerning patterns in mood or behavior and alerting clinicians when intervention might be needed. Some systems provide therapeutic exercises and support that supplement (not replace) human therapists.

The ethical considerations are significant. Mental health involves particularly sensitive data and vulnerable patients. AI must be carefully designed to avoid harm, with clear escalation paths when human intervention is needed.

The Economics of Healthcare AI

Understanding the economic dynamics helps explain adoption patterns and predict future developments.

ROI Models for Healthcare AI

Healthcare organizations evaluate AI investments differently than other industries. ROI considerations include:

Direct cost savings: Reduced labor for automated tasks, fewer errors requiring rework, shorter hospital stays for better-managed patients.

Revenue enhancement: Faster billing cycles, more complete coding, reduced denials—AI in revenue cycle management often pays for itself quickly.

Quality improvement: Better outcomes may reduce penalty payments under value-based contracts and improve reputation-driven patient volume.

Staff satisfaction: Reduced administrative burden may decrease turnover, saving recruitment and training costs that are substantial in healthcare.

The organizations seeing best returns typically start with high-volume, well-defined processes where AI impact can be clearly measured. Documentation automation and revenue cycle are common starting points because ROI is straightforward to calculate.

The Vendor Landscape

Healthcare AI vendors range from major EHR companies adding native AI capabilities to specialized startups focused on particular applications. The landscape includes:

EHR-integrated AI: Epic, Oracle Cerner, and others now offer AI capabilities built into their platforms. This reduces integration burden but may limit flexibility.

Specialized vendors: Companies focused on specific applications—radiology AI, documentation AI, clinical decision support—may offer deeper capability in their domains.

Big tech healthcare plays: Google, Microsoft, Amazon, and others are investing heavily in healthcare AI, bringing scale and capability but also raising questions about data control and healthcare-specific expertise.

For organizations evaluating vendors, the key questions involve integration requirements, validation evidence, total cost of ownership, and contractual protections around data use and liability.

Investment and M&A Activity

The healthcare AI market is seeing significant investment activity. Venture capital flows to promising startups, while larger companies acquire complementary capabilities. This consolidation is reshaping the competitive landscape.

For healthcare organizations, this means vendor evaluation must consider company viability and the potential for acquisitions that might change product direction or support quality. The excitement around healthcare AI attracts both legitimate innovators and opportunists—due diligence matters.

Global Healthcare AI Initiatives

Healthcare AI development isn’t limited to technology companies. Governments, research institutions, and international organizations are driving important initiatives.

National AI Health Strategies

Many countries have developed national strategies for AI in healthcare. The United States has invested significantly through NIH and other agencies. The UK’s NHS has launched major AI initiatives. China is making substantial investments in healthcare AI research and deployment.

These national strategies shape research priorities, regulatory approaches, and funding availability. Organizations should understand the policy environment in their jurisdictions and potential opportunities for participation in government-sponsored initiatives.

Academic and Research Contributions

University research continues to advance healthcare AI capabilities. Partnerships between academic institutions and healthcare systems produce validated research that commercial implementations often build upon.

For healthcare organizations considering AI, academic partnerships can provide access to cutting-edge technology and research expertise. For vendors, academic validation strengthens credibility.

Open Source and Collaborative Approaches

Some healthcare AI development occurs through open source and collaborative models. Organizations like the Linux Foundation’s health projects and various open-source radiology AI initiatives enable shared development and broader access to AI capabilities.

These approaches may be particularly valuable for resource-constrained healthcare systems that can’t afford commercial solutions. However, open source implementations still require technical expertise to deploy and maintain.

Patient Advocacy and Healthcare AI

Patients and advocacy organizations are increasingly involved in shaping healthcare AI development and policy.

The Patient Voice in AI Development

Patient involvement in AI development helps ensure that systems address real patient needs and respect patient values. Some AI projects now include patient advisory boards or patient representatives in development processes.

This involvement matters because patients experience healthcare AI differently than the clinicians who use it or the technologists who build it. Patient perspectives can identify concerns that others miss and validate that AI truly improves the care experience.

Advocacy for Equity and Access

Patient advocacy organizations are raising concerns about AI equity—ensuring that AI benefits aren’t restricted to wealthy patients or well-resourced healthcare systems. They’re pushing for transparent reporting on AI performance across populations and for policies that promote equitable access.

These advocacy efforts influence regulatory discussion and may shape how healthcare AI develops and deploys. Organizations should expect increasing accountability for equity considerations.

Advocacy groups are also pushing for stronger informed consent requirements around AI in healthcare. Patients have legitimate interests in understanding when AI influences their care and what that means for them.

Healthcare organizations that communicate clearly about AI use—rather than deploying invisibly—build trust and demonstrate respect for patient autonomy. Proactive transparency is increasingly expected.

Frequently Asked Questions

How is AI used in healthcare?

AI applications span diagnosis (analyzing images, lab results, symptoms), operations (scheduling, documentation, billing), treatment (personalized medicine, drug discovery), and monitoring (wearables, predictive alerts). Most healthcare AI operates behind the scenes, assisting human clinicians rather than replacing them.

Is AI replacing doctors?

No. AI augments physician capabilities rather than replacing clinical judgment. Doctors who use AI tools become more effective, but the technology complements rather than substitutes human expertise. The relationship is partnership, not replacement.

How accurate is AI diagnosis?

Accuracy varies by application and context. In specific tasks like detecting certain cancers in medical images, AI achieves 90%+ accuracy and sometimes outperforms human specialists. But AI performance depends on training data quality and similarity to presenting cases. No AI system is perfectly accurate across all scenarios.

What are the risks of AI in healthcare?

Risks include data privacy breaches, algorithmic bias affecting vulnerable populations, errors when AI encounters situations outside its training, and potential for over-reliance on automated recommendations. Responsible implementation addresses these through governance, testing, and ongoing monitoring.

Will AI make healthcare cheaper?

Early evidence suggests AI reduces specific costs—fewer duplicate tests, shorter hospital stays, less administrative waste. Whether total healthcare spending decreases depends on whether savings are reinvested, how reimbursement models evolve, and whether increased access generates new utilization. The economic picture remains complex.

Conclusion

AI in healthcare isn’t coming—it’s here. The $45+ billion market, 90% hospital adoption for some applications, and clear clinical wins establish AI as essential infrastructure for modern healthcare delivery.

The transformation brings genuine benefits: earlier diagnoses, personalized treatments, reduced administrative burden, and expanded access. It also brings genuine concerns: privacy, bias, equity, and the human experience of care.

What matters now is implementation quality. Healthcare organizations that approach AI thoughtfully—starting with clear problems, addressing bias proactively, involving clinicians meaningfully, and listening to patients—will realize benefits that poorly planned implementations won’t achieve.

For patients, AI represents an opportunity for better care delivered more efficiently. For healthcare workers, it offers relief from unsustainable burdens. For administrators, it provides tools to improve outcomes while managing costs.

The technology works. The question is whether we implement it wisely.