Reliable AI: Why Accuracy Isn’t Enough in the Real World

In the lab, accuracy asks a clean, binary question: Did the model get this right? In the real world, that question is dangerously incomplete.

For mission-driven AI products—especially in healthcare, climate, infrastructure, and industry—the real question is harder and far more consequential: Can we count on this system every time it counts?

That gap between getting the answer right once and getting it right every time is the difference between a promising prototype and a product trusted with lives, safety, or capital. That gap is reliability.

Reliability Is About Predictability, Not Peak Performance

Accuracy is a snapshot. Reliability is a trajectory.

A reliable system produces consistent, predictable behavior across time, deployments, populations, and operating conditions. It doesn’t just work on a curated benchmark; it holds up when the lab lights are off.

This is where teams moving from academic ML into products often struggle. As Tobias Rijken, CTO of Kheiron Medical, notes, real-world machine learning looks nothing like academic machine learning. A research model may hit a performance threshold once; a clinical product must deliver expert-level outputs across thousands of messy, real cases.

When reliability is achieved, consistency becomes a product feature. Dirk Smeets, CTO of icometrix, emphasized that reliable ML systems can perform at the level of highly specialized radiologists—and do so consistently. That consistency is what allows expertise to reach a rural clinic as reliably as an academic medical center.

Reliability isn’t about being good once. It’s about being predictable when it matters.

Why Reliability Fails Outside the Lab

Most reliability failures don’t look like crashes. They look like subtle inconsistency.

Performance degrades slowly. Outputs vary just enough to confuse users. Edge cases accumulate. No single failure is dramatic, but together they erode trust.

A common root cause is the benchmark fallacy: assuming that a strong score on a frozen dataset guarantees real-world performance. As Marcel Gehrung, CEO of Cyted, pointed out, solving a technical problem in a paper is very different from surviving a high-volume clinical screening pathway.

Sometimes the failure is mundane. Harro Stokman, CEO of Kepler Vision, described how early deployments struggled because the system mistook coat racks or Christmas statues for fallen humans. The model wasn’t wrong in the lab—it simply hadn’t encountered real homes.

Infrastructure variation can quietly undermine reliability as well. Emi Gal, CEO of Ezra, recounts how regulators flagged a dataset skewed toward Siemens scanners over GE scanners. The task hadn’t changed; the hardware context had.

In climate and infrastructure AI, the issue can be temporal. Subit Chakrabarti, VP of Technology at Floodbase, highlights how climate change breaks a core modeling assumption: that historical data reliably predicts the present. A static model becomes a monument to the past.

In each case, the model isn’t theoretically wrong; it’s misaligned with reality.

Reliability Has Three Dimensions

Reliability isn’t a single metric. It’s a performance profile maintained across time, context, and operation.

1. Temporal Reliability (Across Time)

Once deployed, models enter a world that changes continuously. Reliability over time requires managing multiple forms of drift:

• Model drift as environments evolve

• Data drift as sensors or physical systems change

• Concept drift as the meaning of data shifts

A concrete example comes from shipping. Konstantinos Kyriakopoulos, CEO of DeepSea, explains how hull fouling gradually changes vessel dynamics, increasing fuel consumption by 10–20%. Sensors still function, but the relationship between inputs and outputs shifts. Reliability requires detecting and adapting to that change.

Concept drift can be subtler. Bill Tancer, co-founder of Signos, has described how his glucose response to identical meals changed over several years as his metabolic health improved. The inputs stayed the same; the physiology did not. A model trained on his earlier state would quietly become unreliable.

Temporal reliability is not a modeling task alone—it’s a lifecycle commitment.

2. Contextual Reliability (Across Place and Population)

Contextual reliability asks whether a system holds up when moved across geographies, institutions, populations, or hardware.

In agriculture, Hamed Alemohammad of the Clark Center for Geospatial Analytics showed how models trained on large, homogeneous Midwest farms fail on smallholder farms with different practices and seasonal patterns.

In healthcare, population reliability carries life-and-death implications. David Golan, CTO of Viz.ai, warns that if certain age groups or ethnicities receive lower accuracy, they may miss life-saving treatment. Historical examples, such as breast cancer assays later found less accurate for Black women, underscore how reliability and fairness are inseparable.

Reliability isn’t just about averages. It’s about who the system works for.

3. Operational Reliability (Inside Real Workflows)

Even strong models fail if they don’t survive real workflows.

Operational reliability includes tolerance to operator variability, workflow integration, and reproducibility.

To address operator variability, John Bertrand, CEO of Digital Diagnostics, described using assistive AI to guide users during data capture, ensuring consistent input quality regardless of skill.

Workflow fit matters just as much. Dirk Smeets argues for AI that runs quietly in the background—automatically between data capture and expert review. When AI becomes plumbing rather than a science experiment, adoption follows.

Reproducibility anchors trust. Philipp Kainz of KML Vision notes that consistent outputs provide an objective baseline independent of fatigue, bias, or time of day.

Why Reliability Is the Foundation of Trust

A model that is 99% accurate but unpredictably wrong is often less valuable than one that is 95% accurate but consistently wrong in known ways.

Trust requires predictability.

Users can compensate for known limitations. They cannot safely compensate for randomness. When reliability falters, users disengage. Sean Cassidy, CEO of Lucem Health, notes that clinicians are already overwhelmed by alerts. Inconsistent AI outputs quickly become noise.

Regulators reflect this reality. Todd Villines, Chief Medical Officer at Elucid, explains that rigorous clinical studies with blinding and independent core labs exist to prove consistent performance across real-world variability. Reliability is the standard, not a bonus.

Reliability Is a Product Property, Not a Model Property

Reliability does not live in model weights. It emerges from the entire system:

• Input quality control

• Data standardization

• Guardrails and sanity checks

• Monitoring and observability

• Versioning and rollback

• Domain-specific metrics

In digital pathology, Proscia prevents blurred slides, air bubbles, or missing tissue from reaching downstream models. Kepler Vision detects compromised sensors before predictions are trusted. Taranis validates drone image quality in the field so unusable data can be re-captured immediately.

Monitoring must be first-class infrastructure. DeepSea uses uncertainty-aware metrics to detect when physical conditions shift enough to invalidate predictions. Perimeter Medical relies on strict version control to roll back models that fail objective performance gates.

Reliability is engineered, not assumed.

The Cost of the Reliability Gap

The Reliability Gap—the distance between lab performance and real-world dependability—is a business risk.

In heavy industry, Berk Birand, CEO of Fero Labs, notes that a single bad prediction can destroy an entire production batch. In healthcare, Junaid Kalia, founder of NeuroCare.AI, argues that deploying AI without robust QA when lives are at stake is an ethical failure.

At scale, human oversight collapses. Gershom Kutliroff, CTO of Taranis, points out that when monitoring billions of acres, manual quality control is impossible. Reliability must be embedded—or scaling stops.

Closing the Gap

Reliability is not something you achieve once. It’s something you maintain—across time, across context, and across workflows.

Teams that treat reliability as a core product requirement—not a downstream validation problem—are the ones whose systems survive long enough to matter.

Next, we’ll turn to robustness: what happens when conditions move beyond expected variability and into true chaos—and how to design AI systems that don’t break when they get there.

- Heather

Vision AI that bridges research and reality

— delivering where it matters

Research: FMs for EO

Sensor-Agnostic Earth Observation: Building AI that Understands Any Satellite

For years, Earth Observation has been trapped in a loop: new sensor, new model, new training cycle. But a fundamental shift is happening. We are moving toward "any-sensor" foundation models—universal systems that process arbitrary combinations of spectral bands and resolutions without skipping a beat.

Five recent breakthroughs show us exactly how this future is being built, but they are taking very different paths to get there:

𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗕𝗮𝘁𝘁𝗹𝗲: 𝗛𝘆𝗽𝗲𝗿𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝘃𝘀. 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀
How do you build one "brain" for 20+ different sensors? 𝗖𝗼𝗽𝗲𝗿𝗻𝗶𝗰𝘂𝘀-𝗙𝗠 uses dynamic hypernetworks and flexible metadata encoding to adapt its internal weights to any spectral or non-spectral modality, spanning from the Earth's surface to the atmosphere. Meanwhile, 𝗦𝗸𝘆𝗦𝗲𝗻𝘀𝗲 𝗩𝟮 pushes for parameter efficiency, using a Mixture of Experts module and learnable modality prompt tokens to handle vast resolution differences and limited feature diversity across sensor types.

𝗧𝗵𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗼𝗴𝗶𝗰: 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗧𝗼𝗸𝗲𝗻 𝗠𝗶𝘅𝘂𝗽
How do these models learn a "universal language" for Earth? 𝗣𝗮𝗻𝗼𝗽𝘁𝗶𝗰𝗼𝗻 treats images of the same geolocation across different sensors as "natural augmentations," forcing the model to learn features that remain constant regardless of the platform. In contrast, 𝗦𝗠𝗔𝗥𝗧𝗜𝗘𝗦 projects heterogeneous data into a shared spectrum-aware space, using cross-sensor token mixup to train a single transformer capable of reconstructing masked data from any combination of bands.

𝗠𝗼𝘃𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝗣𝗶𝘅𝗲𝗹𝘀: 𝗧𝗵𝗲 𝗖𝗹𝘂𝘀𝘁𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵
While most models focus on reconstructing pixels, 𝗣𝘆𝗩𝗶𝗧-𝗙𝗨𝗦𝗘 takes a different route by adapting the SwAV algorithm. By focusing on cluster assignments (prototypes) rather than pixel-space reconstruction, it creates embeddings that are independent of specific band combinations—making it uniquely robust for downstream tasks where certain sensors might fail or be obscured by clouds.

These papers prove that the diversity of training data and the flexibility of metadata encoding are now more critical than sensor-specific tuning. We aren't just building better models; we are building a more resilient, sensor-agnostic window into our changing planet.

PyViT-FUSE: A Foundation Model for Multi-Sensor Earth Observation Data

Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation

Towards a Unified Copernicus Foundation Model for Earth Vision

SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images 

SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing

Research: Benchmarking FMs for Pathology

Benchmarking foundation models as feature extractors for weakly supervised computational pathology

New research from Maurício Pinto Soares 𝘦𝘵 𝘢𝘭. challenges a core assumption in AI model development: that more training data always means better performance.

Their systematic benchmark of pathology foundation models reveals something critical for anyone deploying AI in clinical settings: 𝘥𝘪𝘷𝘦𝘳𝘴𝘪𝘵𝘺 trumps volume.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:
Foundation models are being rapidly adopted in computational pathology for tasks like cancer detection and prognosis prediction. Yet we've lacked rigorous comparative data on what actually makes these models work well in practice—especially for weakly supervised learning where only slide-level labels are available.

𝐊𝐞𝐲 𝐟𝐢𝐧𝐝𝐢𝐧𝐠𝐬:
- Performance doesn't scale linearly with training set size. Models trained on billions of images don't automatically outperform those trained on millions.
- Training data 𝘥𝘪𝘷𝘦𝘳𝘴𝘪𝘵𝘺 matters more—varied data sources, patient populations, and cancer types produced more robust encoders.
- Tile-level encoders consistently outperformed slide-level encoders across multiple benchmarks.
- While state-of-the-art giants like CONCH and Virchow set the overall performance ceiling, CTransPath and Phikon emerged as top-tier contenders, proving that high data diversity can rival models trained on substantially less data.

𝐓𝐡𝐞 𝐢𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
For organizations building pathology AI systems, this suggests strategic focus should shift from simply acquiring massive datasets to ensuring 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘷𝘦 training data spanning diverse patient demographics and tissue types. It's a reminder that quality and coverage matter as much as quantity.

The paper provides standardized benchmarks that will help the field move beyond vendor claims toward evidence-based model selection.

Research: Foundation Model Features

Do computer vision foundation models learn the low-level characteristics of the human visual system?

Both human vision and foundation models learn from exposure to natural images. But do they develop the same low-level perceptual characteristics?

Foundation models show exceptional generalization, but we don't fully understand how their visual encoding compares to biological vision. This matters for interpretability: if models share our perceptual "bottlenecks," their failures become more predictable. If they don't, we need different frameworks for understanding when and why they fail.

Yancheng Cai et al. took an elegant approach: instead of testing high-level tasks, they evaluated 45 foundation and generative models using psychophysical stimuli (Gabor patches, band-limited noise) designed to probe fundamental human visual characteristics. They compared model responses against established human data using cosine similarity in feature space.

Key findings across nine test types:
- Contrast detection: Most models showed different sensitivity patterns than humans. Some (DINOv2, SD-VAE) exhibited band-pass characteristics similar to human contrast sensitivity, but none matched human performance across spatial frequencies. Many showed invariance to low-frequency illumination changes—a useful property likely learned from training data.
- Contrast masking: This is where models performed best. DINOv2 and OpenCLIP showed surprisingly strong alignment with human masking curves, particularly for phase-incoherent masking. The authors suggest this might be because natural images contain abundant masking signals, unlike barely-visible patterns on uniform backgrounds.
- Contrast constancy: Only DINOv2 and OpenCLIP showed partial contrast constancy—the ability to perceive contrast consistently across spatial frequencies. However, both attenuated low frequencies more than humans do.

What this tells us
Foundation models don't share the same fundamental perceptual bottlenecks as human vision, despite being trained on similar visual data. They're better aligned with supra-threshold human vision (contrast masking, some constancy properties) than near-threshold detection. This suggests these models develop efficient contrast coding through training, but via different paths than biological vision.

Among tested models, DINOv2 showed the closest overall resemblance to human low-level vision characteristics.