New foundation models.

Medical copilots.

Clinical reasoning systems.

AI-powered patient assistants.

But after following healthcare engineering projects for the last few years, I've noticed something interesting:

The teams that struggle in production usually don't have an AI problem.

They have a data problem.

More specifically, they have an interoperability problem.

And that's why I think HL7 and FHIR matter more than the latest healthcare AI model.

The Reality Check Most Healthcare Startups Face

Building a healthcare AI demo has never been easier.

You can connect an LLM, upload medical documents, create a chatbot interface, and have something impressive running within days.

The challenge starts when you try to deploy that system inside an actual healthcare environment.

Suddenly you need access to:

• Electronic Health Records (EHRs)

• Laboratory systems

• Imaging platforms

• Insurance systems

• Patient portals

• Wearable devices

• Clinical workflows

And none of them speak the same language.

This is where many AI-first healthcare products hit a wall.

Why HL7 and FHIR Are Becoming More Important Than AI

I recently came across an interesting breakdown on what it takes to build production-ready healthcare AI platforms using HL7 and FHIR standards.

Original article:

https://geekyants.com/blog/hl7-and-fhir-for-ai-healthcare-platforms-what-it-takes-to-build-for-production

What I liked about the article is that it focused less on AI hype and more on the infrastructure layer required to make AI actually useful.

Because healthcare AI doesn't fail due to lack of intelligence.

It fails because it cannot reliably access the right patient data.

What Are HL7 and FHIR?

For developers entering healthcare, here's the simplified version.

HL7

HL7 (Health Level Seven) is a healthcare data exchange standard that has been used across hospitals and healthcare systems for decades.

It helps different healthcare applications exchange information such as:

• Patient demographics

• Admissions

• Discharges

• Lab results

• Billing information

The challenge is that HL7 integrations can become complex, especially when dealing with legacy systems.

FHIR

FHIR (Fast Healthcare Interoperability Resources) is a modern API-driven standard designed to simplify healthcare interoperability.

Think of it as the healthcare industry's attempt to bring healthcare data closer to modern web development practices.

Instead of dealing with complicated message formats, developers can work with resources such as:

• Patient

• Observation

• Medication

• Practitioner

• Encounter

through REST APIs.

For AI products, that's a huge advantage.

My Opinion: Interoperability Is The Real Competitive Advantage

Most healthcare startups believe their moat is their AI model.

I disagree.

Models are becoming increasingly accessible.

Every major provider now offers highly capable foundation models.

What remains difficult is building reliable healthcare integrations.

The company that can seamlessly connect data from hospitals, EHRs, laboratories, and remote monitoring systems will likely have a bigger advantage than the company with a slightly better prompt.

Healthcare runs on data.

AI is only as good as the information flowing into it.

Production AI Starts With Production Data

A healthcare AI platform generally looks something like this:

EHR Systems
     ↓
FHIR / HL7 Integration Layer
     ↓
Data Validation & Governance
     ↓
Data Platform
     ↓
AI Models
     ↓
Clinical Applications

Notice something?

AI sits near the end of the stack.

Not the beginning.

Yet many teams spend months evaluating models and only weeks planning interoperability.

That seems backwards to me.

Companies That Understand This

Several organizations appear to be investing heavily in healthcare interoperability and AI infrastructure.

Large platform providers such as Google, Microsoft, and AWS continue expanding healthcare-focused cloud services and FHIR-compatible solutions.

Healthcare technology leaders like Epic Systems, Oracle Health, Philips, and Medtronic have spent years building around healthcare data exchange and clinical workflows.

Engineering firms such as GeekyAnts, Thoughtworks, EPAM, and Accenture increasingly discuss healthcare interoperability, cloud-native healthcare architecture, and AI implementation as foundational requirements rather than optional features.

The pattern is clear:

The industry is shifting from AI experimentation to production deployment.

And production deployment requires interoperability.

The Mistake Most AI Teams Make

Here's what I see repeatedly:

Phase 1

Build AI model.

Phase 2

Build chatbot.

Phase 3

Look for healthcare data.

Phase 4

Discover integration complexity.

Phase 5

Rebuild architecture.

Many teams would save months of work if they reversed the order.

Start with data access.

Start with interoperability.

Start with compliance.

Then build AI.

The Future of Healthcare AI

I don't think the winners in healthcare AI will necessarily have the smartest models.

I think they'll have:

• Better data access

• Better interoperability

• Better governance

• Better compliance

• Better integration with clinical workflows

Those things sound less exciting than AI breakthroughs.

But they are significantly harder to build.

And much harder to copy.

Final Thoughts

Healthcare AI discussions often focus on intelligence.

Healthcare engineering teams should focus on connectivity.

Because a brilliant model without healthcare data is useless.

A good model with reliable access to healthcare data can create real clinical value.

That's why I believe HL7 and FHIR are becoming some of the most important technologies in modern healthcare AI.

Not because they're new.

But because they're what make production AI possible.

For years, digital investment platforms focused mainly on automation. Robo-advisors simplified onboarding, portfolio allocation, and periodic rebalancing. They made investing more accessible, but many of them still worked on fixed rules and backward-looking data.

That model is no longer enough.

Markets move faster, investor expectations have changed, and financial platforms now need to respond to risk signals in real time. This is where AI is beginning to reshape WealthTech—not just by automating tasks, but by helping portfolio management systems become predictive, adaptive, and more personalized.

A recent article by GeekyAnts on AI in WealthTech and scalable portfolio management platforms explores how AI-native systems are changing predictive investing and risk forecasting. The larger takeaway is clear: the future of WealthTech depends less on simple automation and more on intelligent infrastructure.

From automated investing to predictive investing

Traditional portfolio systems usually react after something has already happened. They analyze past performance, rebalance based on predefined rules, and provide reports at fixed intervals.

Predictive investing works differently.

Instead of waiting for monthly or quarterly reviews, AI-powered platforms can analyze live market signals, economic indicators, financial reports, regulatory updates, earnings calls, and other structured or unstructured data sources. These signals can help identify risk patterns before they become visible through traditional reporting.

This shift changes the role of wealth managers and investment platforms.

They are no longer just reporting what happened in a portfolio. They can start preparing for what might happen next.

That does not mean AI can predict markets with perfect accuracy. It cannot. But it can help detect correlations, stress-test scenarios, and support faster decision-making when market conditions change.

Why real-time data matters

A predictive portfolio platform is only as strong as the data it can process.

In WealthTech, data comes from many places: market feeds, client profiles, asset performance, macroeconomic indicators, tax data, compliance rules, and even unstructured sources like filings or transcripts.

The challenge is not just collecting this data. The bigger challenge is processing it quickly and reliably.

If a platform depends on delayed data, its AI models may already be working with outdated assumptions. For predictive investing to work, data pipelines need to support near real-time ingestion, cleaning, classification, and analysis.

This is where engineering becomes just as important as the AI model itself.

A strong WealthTech platform needs scalable data architecture, reliable APIs, secure integrations, and systems that can handle large volumes of financial information without slowing down the user experience.

Risk forecasting is becoming a core product feature

Risk has always been central to investing. What is changing is how risk is measured and communicated.

Legacy systems often evaluate risk based on historical volatility, asset allocation, and periodic portfolio reviews. AI-enabled platforms can take this further by running continuous stress tests and scenario simulations.

For example, a platform could estimate how a portfolio may respond to interest rate changes, geopolitical events, currency fluctuations, or sector-specific downturns. This gives advisors and investors a more dynamic view of exposure.

The value here is not only in predicting losses. It is also in helping users understand why a certain portfolio decision may be recommended.

That explainability matters, especially in financial services.

If an AI system recommends reducing exposure to a particular asset class, the platform should be able to explain the logic behind it. Black-box recommendations may look impressive in demos, but they are difficult to trust in regulated financial environments.

Hyper-personalization is the next competitive layer

Another major shift in WealthTech is the move from generic portfolio models to highly personalized investment experiences.

In the past, many platforms grouped investors into broad risk categories such as conservative, balanced, or aggressive. While useful, these categories are often too simple for modern investors.

AI can help platforms create more individualized strategies by considering a wider set of variables: investment goals, liquidity needs, tax preferences, time horizon, risk tolerance, income patterns, ESG preferences, and life-stage changes.

This does not mean every investor needs a completely custom portfolio built from scratch. But it does mean platforms can offer more relevant recommendations and more adaptive financial planning experiences.

For wealth firms, this kind of personalization can improve client engagement and retention. For users, it can make financial advice feel less generic and more aligned with their actual goals.

The real challenge is infrastructure, not just AI

A common mistake in AI adoption is assuming that the model is the product.

In WealthTech, the AI model is only one part of the system. The real product depends on everything around it: data pipelines, security, compliance, APIs, user experience, audit logs, cloud infrastructure, and integration with existing financial systems.

This is why building AI-powered portfolio management platforms is not simply a machine learning problem. It is a full-scale product engineering challenge.

Financial platforms also need to manage strict privacy and governance requirements. Client data cannot be treated casually. Predictive systems must be secure, auditable, and explainable from the start.

This is especially important because financial recommendations can directly affect people’s wealth, retirement planning, and long-term security.

A platform that cannot explain its recommendations or protect sensitive data will struggle to gain trust, no matter how advanced its AI models appear.

Start with focused use cases

For companies exploring AI in WealthTech, the best path is not always to rebuild the entire investment platform at once.

A more practical approach is to begin with one high-value workflow.

That could be predictive risk forecasting for a specific asset class, automated tax-loss harvesting, portfolio drift detection, client segmentation, or advisor decision support.

Starting small makes it easier to measure model accuracy, infrastructure cost, business value, and compliance impact. Once the use case proves reliable, the platform can expand into more complex workflows.

This phased approach is important because AI in financial services requires more than experimentation. It needs production readiness.

The future of WealthTech will be predictive, explainable, and scalable

AI is pushing WealthTech beyond dashboards and static reports.

The next generation of portfolio management platforms will likely be judged by how well they can process real-time data, forecast risk, personalize investment strategies, and explain decisions in a way that users and regulators can trust.

The strongest platforms will not be the ones that simply add AI features on top of legacy systems. They will be the ones designed around intelligent data flows, transparent decision-making, secure infrastructure, and scalable engineering.

In that sense, AI in WealthTech is not just about smarter investing.

It is about building financial platforms that can adapt as quickly as the markets they operate in.