---
Publish Now: null
author:
- Yujan Shrestha
cta_button_text: ''
cta_case_studies: []
cta_hero_details: ''
cta_hero_text: ''
date: '2025-12-23'
description: 44 clearances. 18 in 2025 alone. The dental AI market has reached its
  tipping point. This report dissects the Pearl/Overjet duopoly controlling 34% of
  the market, the 172-day FDA timeline you need to beat, and the \$2.5M validation
  cost barrier. Discover how the \"predicate network\" creates shortcuts for fast
  followers, why clinical validation is the biggest bottleneck, and why the window
  to establish a foundational regulatory moat is rapidly closing for new entrants.
related:
- 350bd5b7-a754-8067-a101-f6cf90367d32
title: 'The Dental AI Revolution: A Comprehensive Analysis of 510(k) Clearances (2021-2025)'
topics:
- AI/ML
- Regulatory
---

### Executive Summary

<figure>
  <img src="/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a75480dfa2e0c83f0905e8a8.png">
  <figcaption>
    2025 Dental AI/ML Medical Device Roundup
  </figcaption>
</figure>

Artificial intelligence in dentistry has transitioned from experimental technology to clinical reality. This analysis of FDA 510(k) clearance data shows a market at a turning point, with **44 AI/ML-powered dental Software as Medical Devices (SaMD)** cleared between May 2021 and December 2025. The year 2025 alone accounts for **18 clearances**---more than the previous two years combined.

We\'ll look at the companies leading the charge, the regulatory pathways they use, the predicate network shaping competition, the clinical applications gaining traction, and the economics of bringing these innovations to market.

Please note, we used AI to identify these devices. While we spot checked everything, we may have missed some devices. However, we feel the executive level directional insights are still valid even though some of the numbers may be a little off.

**Limitations:**

- Company name variations in FDA records may affect exact counts
- Some devices may have dental applications but be classified under different panels
- Funding data is based on publicly available information and may be incomplete
- Cost estimates are based on industry analysis and may vary significantly

### Part I: The Growth Trajectory

#### The Rise

The dental AI market picked up speed fast. After starting with just 2 clearances in 2021, the industry has since grown and will just had its largest jump yet.

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754807f8e62d111e3a58068.png)

| Year | Clearances | YoY Growth | Cumulative Total |
|------|------------|------------|------------------|
| 2021 | 2          | ---        | 2                |
| 2022 | 7          | +250%      | 9                |
| 2023 | 9          | +29%       | 18               |
| 2024 | 8          | -11%       | 26               |
| 2025 | 18         | +125%      | 44               |

The 2025 surge stands out. With 18 clearances representing **40.9% of all dental AI devices ever cleared**, this single year changed the board. The slight dip in 2024 looks like a temporary pause before the jump.

#### Quarterly Momentum

Examining the quarterly data reveals interesting patterns in regulatory timing:

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a7548018b42fdfdd2e556061.png)

| Quarter | Clearances | Notable Trend           |
|---------|------------|-------------------------|
| Q2 2025 | 8          | Peak quarter in history |
| Q3 2025 | 5          | Sustained momentum      |
| Q4 2025 | 3          | Year-end completions    |
| Q4 2023 | 4          | Previous peak           |

The concentration of clearances in Q2 2025 (8 devices) suggests a bolus of submissions happened in Q4 or 2024.

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

### Part II: The Competitive Landscape

#### Market Leaders: A Two-Horse Race

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a7548095a66fd491a9ca84b3.png)

Two companies dominate the dental AI space:

**Pearl Inc.** leads with **8 clearances** (combining \"Pearl Inc.\" and \"Pearl, Inc.\" entries), taking **18.2%** of all cleared devices. Their portfolio spans both MYN (Medical Image Analyzer) and QIH (Automated Radiological Image Processing) product codes.

**Overjet, Inc.** follows closely with **7 clearances** (combining variant company name entries), accounting for **15.9%** of the market. Overjet stands out for speed, with an average clearance time of just **98 days** for their most recent submissions---way below the industry median of 172 days.

| Company                 | Clearances | Market Share | First Clearance | Avg. Clearance Time |
|-------------------------|------------|--------------|-----------------|---------------------|
| Pearl Inc.              | 8          | 18.2%        | March 2022      | 247 days            |
| Overjet, Inc.           | 7          | 15.9%        | May 2021        | 146 days            |
| VideaHealth             | 3          | 6.8%         | February 2023   | 116 days            |
| Shanghai United Imaging | 2          | 4.5%         | May 2024        | 242 days            |
| Velmeni Inc.            | 2          | 4.5%         | 2024            | ---                 |
| Axial Medical Printing  | 2          | 4.5%         | 2024            | ---                 |
| Nobel Biocare AB        | 2          | 4.5%         | 2024            | ---                 |

Together, Pearl and Overjet control approximately **34%** of all dental AI clearances---a significant concentration that reflects both their early-mover advantage and sustained investment in regulatory execution.

#### The Rising Challengers

Beyond the top two, several companies are positioning for growth:

**VideaHealth** is a serious contender with 3 clearances and efficient regulatory execution (116-day average). Their focus on AI-powered diagnostics positions them well for the expanding caries detection market.

**Shanghai United Imaging Healthcare** shows the growing international side of dental AI, bringing Chinese innovation to the US market through the 510(k) pathway.

**Axial Medical Printing** combines AI with 3D printing and surgical planning.

#### New Market Entrants

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a7548010802fc7dbd7c11168.png)

The ecosystem continues to attract new players:

| Year | New Companies Entering | Cumulative Companies |
|------|------------------------|----------------------|
| 2021 | 2                      | 2                    |
| 2022 | 6                      | 8                    |
| 2023 | 6                      | 14                   |
| 2024 | 5                      | 19                   |
| 2025 | 9                      | 28                   |

The **9 new entrants in 2025** represent the largest single-year influx of new companies, suggesting the market opportunity is attracting significant entrepreneurial and corporate attention.

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

### Part III: Regulatory Pathways and Timing

#### The 172-Day Benchmark

Understanding regulatory timelines is critical for strategic planning. Our analysis reveals:

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754804a8ce0d2d686736408.png)

| Metric                 | Days |
|------------------------|------|
| **Median**             | 172  |
| **Mean**               | 189  |
| **Minimum**            | 28   |
| **Maximum**            | 389  |
| **Standard Deviation** | 78   |

The distribution varies a lot, with clearance times ranging from 28 days to nearly 13 months. This variability shows why submission quality and regulatory strategy matter.

#### Speed Categories

| Category   | Days   | Count | Percentage |
|------------|--------|-------|------------|
| Fast Track | \<90   | 2     | 4.5%       |
| Standard   | 90-180 | 21    | 47.7%      |
| Extended   | \>180  | 21    | 47.7%      |

Nearly half of all submissions take longer than 6 months, highlighting the complexity of AI/ML device review. However, the existence of fast-track clearances (under 90 days) demonstrates that well-prepared submissions can achieve rapid market entry.

#### Quartile Analysis

| Percentile    | Days | Interpretation    |
|---------------|------|-------------------|
| 25th          | 132  | Top performers    |
| 50th (Median) | 172  | Industry standard |
| 75th          | 260  | Extended review   |

Companies in the top quartile (under 132 days) have mastered the process. Their submissions share common success factors: clear predicate selection, comprehensive performance data, and well-defined intended use statements.

#### Third-Party Review and Expedited Pathways

Notably, **100% of dental AI devices** went through standard FDA review rather than third-party review organizations. Similarly, no devices received expedited review designation. This suggests that dental AI, while innovative, is not being prioritized under breakthrough or expedited pathways---manufacturers should plan for standard review timelines.

### Part IV: Product Code Analysis

**Understanding MYN and QIH**

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754804a9626dc8db0cd7af4.png)

Two FDA product codes dominate the dental AI landscape:

MYN - Medical Image Analyzer (22 clearances, 50%)

The MYN product code (regulation number 892.2070) covers Computer-Aided Detection (CADe) devices. These systems are designed to identify and highlight potential abnormalities in radiological images, directing the clinician\'s attention to areas that may warrant further review.

In the dental context, MYN devices typically:

- Automatically detect potential caries on bitewing and periapical radiographs
- Highlight areas of concern for clinician review
- Provide visual overlays or annotations on dental images
- Function as a \"second reader\" to augment clinical judgment

QIH - Automated Radiological Image Processing Software (14 clearances, 32%)

The QIH product code (regulation number 892.2050) covers AI-powered software for image processing and analysis. These devices provide the foundational image enhancement and processing capabilities that enable more advanced diagnostic functions.

QIH devices in dentistry typically:

- Enhance image quality and contrast
- Automate image processing workflows
- Provide measurement and analysis tools
- Enable integration with practice management systems

LLZ - System, Image Processing, Radiological (7 clearances, 16%)

The LLZ code covers general image management and processing systems, representing a broader category that includes some AI-enhanced functionality.

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a75480659dbce84aea798c03.png)

|              |       |            |                  |
|--------------|-------|------------|------------------|
| Product Code | Count | Percentage | Primary Function |
| MYN          | 22    | 50.0%      | CADe - Detection |
| QIH          | 14    | 31.8%      | Image Processing |
| LLZ          | 7     | 15.9%      | Image Management |
| PNN          | 1     | 2.3%       | Other            |

The dominance of MYN reflects the market\'s focus on detection-oriented AI---systems designed to find pathology that might otherwise be missed.

### Part V: Inside the 510(k) Summaries

**The Language of Clearance**

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a75480efbe62cf145bc0a5c9.png)

Analysis of 510(k) summary documents reveals consistent patterns in how successful submissions frame their intended use:

|                       |           |                                                |
|-----------------------|-----------|------------------------------------------------|
| Phrase                | Frequency | Strategic Implication                          |
| \"radiograph\"        | 72.7%     | Anchors device to established imaging modality |
| \"periapical\"        | 61.4%     | Specifies image type for clear scope           |
| \"assist\"            | 54.5%     | Positions AI as decision support               |
| \"detection\"         | 54.5%     | Defines primary function                       |
| \"bitewing\"          | 54.5%     | Specifies image type                           |
| \"dentist\"           | 50.0%     | Identifies intended user                       |
| \"caries\"            | 43.2%     | Specifies target condition                     |
| \"clinical judgment\" | 36.4%     | Preserves clinician authority                  |
| \"aid in\"            | 27.3%     | Alternative to \"assist\"                      |
| \"panoramic\"         | 25.0%     | Broader imaging scope                          |

The consistent use of phrases like \"assist\" and \"aid in\" (combined 81.8%) reflects a deliberate regulatory strategy: positioning AI as a decision support tool rather than an autonomous diagnostic agent. This framing is critical for regulatory acceptance and clinical adoption.

**Target Conditions**

The intended use statements reveal clear clinical priorities:

|                           |           |                                 |
|---------------------------|-----------|---------------------------------|
| Condition                 | Frequency | Clinical Significance           |
| Caries                    | 43.2%     | Most common dental pathology    |
| Calculus                  | 15.9%     | Periodontal health indicator    |
| Periapical radiolucencies | 25.0%     | Endodontic pathology            |
| Bone loss                 | 6.8%      | Periodontal disease progression |

Caries detection dominates the market, reflecting both the clinical prevalence of dental decay and the relatively well-defined nature of the detection task. Interproximal caries---cavities forming between teeth---are particularly amenable to AI detection due to their characteristic radiographic appearance.

**Common Acceptance Criteria**

While specific values vary by device, successful submissions demonstrate performance through standardized metrics:

|             |               |                        |
|-------------|---------------|------------------------|
| Metric      | Typical Range | Interpretation         |
| Sensitivity | 86% - 91%     | True positive rate     |
| Specificity | 79% - 89%     | True negative rate     |
| AUC         | 0.85 - 0.95   | Overall discrimination |

These metrics are typically validated through retrospective reader studies using a pre-defined ground truth established by consensus of board-certified dental radiologists or oral surgeons.

### Part VI: The Predicate Network --- Building on Regulatory Foundations

Every FDA 510(k) clearance needs a foundation. The substantial equivalence pathway requires manufacturers to prove their device is as safe and effective as a legally marketed predicate device. In dental AI, these relationships form a complex network that shows the regulatory lineage---and competitive dynamics---of the whole market.

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a7548066a1e0c49e6c455088.png)

**The Architecture of Regulatory Influence**

Our analysis of all 44 dental AI 510(k) clearances shows a highly connected ecosystem where early movers don\'t just gain market access---they become the foundation competitors must build on.

|                                             |       |
|---------------------------------------------|-------|
| Metric                                      | Value |
| Total Dental AI Devices Analyzed            | 44    |
| External (Non-Dental-AI) Predicates         | 26    |
| Total Predicate Relationships               | 59    |
| Dental AI Devices Now Serving as Predicates | 15    |
| Average Predicates Cited per Device         | 1.34  |

The data is clear: 34% of all dental AI devices (15 of 44) have become predicates for subsequent clearances. More than one-third of the market now builds on foundations laid by dental AI pioneers rather than external devices from adjacent categories.

**The Foundation Layer: First-Mover Advantage is Regulatory**

At the base of the dental AI regulatory structure sit two companies whose early clearances have become industry infrastructure:

|          |               |                |      |                        |
|----------|---------------|----------------|------|------------------------|
| K Number | Company       | Device         | Year | Citations as Predicate |
| K210365  | Pearl Inc.    | Second Opinion | 2021 | 8                      |
| K222746  | Overjet, Inc. | Caries Assist  | 2022 | 6                      |
| K210187  | Overjet, Inc. | Dental Assist  | 2021 | 4                      |

Pearl\'s K210365 stands as the most influential predicate in dental AI, cited by 8 subsequent devices from 6 different companies. When Pearl cleared \"Second Opinion\" in 2021, they didn\'t just enter the market---they created the regulatory template that competitors would follow for years.

Overjet\'s dual foundation (K210187 and K222746) demonstrates a deliberate strategy: by securing multiple early clearances across different indications, they established predicate pathways for both general dental analysis and specific caries detection applications.

**The Middle Floors: Building on Proven Pathways**

The middle tier of the regulatory structure shows how second-wave entrants leveraged the foundation:

|          |             |                                    |      |
|----------|-------------|------------------------------------|------|
| K Number | Company     | Predicate(s) Cited                 | Year |
| K213795  | VideaHealth | K210365 (Pearl)                    | 2022 |
| K230144  | Denti.AI    | K210365 (Pearl), K210187 (Overjet) | 2023 |
| K240003  | Velmeni     | K222746 (Overjet)                  | 2024 |

These companies made a strategic choice: rather than establishing novel predicate pathways from external devices, they built directly on dental AI predecessors. This approach offers several advantages:

1.Faster review times --- Similar predicates mean fewer questions from FDA reviewers

2.Clearer performance benchmarks --- Acceptance criteria are already established

3.Reduced testing burden --- Can reference predicate\'s clinical validation approach

4.Market validation --- Building on a cleared device signals regulatory feasibility

**The Top Floors: 2024-2025 Devices Still Under Construction**

The newest entrants (2024-2025) represent the \"top floors\" of the regulatory structure---devices that benefit from multiple layers of established predicates below them. These devices often cite second-generation predicates, creating multi-generational chains:

Example Chain:

K210365 (Pearl, 2021)  
└── K230144 (Denti.AI, 2023)  
└── K250525 (Denti.AI, 2025)

This three-generation chain shows how regulatory infrastructure compounds: Pearl\'s 2021 clearance enabled Denti.AI\'s 2023 clearance, which in turn enabled their 2025 follow-on device.

**Strategic Implications of the Predicate Network**

For New Entrants: The window for becoming a foundational predicate is closing. With 15 dental AI devices already serving as predicates, the regulatory landscape is increasingly defined by existing players. New entrants face a choice: build on competitors\' foundations (faster path to market, but you\'re implicitly validating their approach) or establish novel predicate pathways (slower and riskier, but creates differentiation and potential future leverage).

For Established Players: Your clearances are competitive assets beyond market access. Every time a competitor cites your device as a predicate, you gain insight into their development timeline, validation of your technical approach, and potential leverage in IP or partnership discussions.

For Investors and Acquirers: Predicate position is a moat indicator. Companies whose devices are frequently cited as predicates have proven regulatory pathways that reduce risk for follow-on products, established performance benchmarks that competitors must match or exceed, and strategic positioning that compounds over time.

**The 26 External Predicates: Opportunities Remain**

Despite the emergence of dental AI predicate hubs, 26 external (non-dental-AI) predicates remain in the network. These include general CADe devices from radiology, image processing systems from other medical specialties, and earlier dental imaging software (non-AI). For companies seeking differentiation, these external predicates represent potential pathways to establish new regulatory foundations rather than building on competitor devices.

### Part VII: The PCCP Frontier

**Predetermined Change Control Plans in Dental AI**

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754803481ebf9f7519e813c.png)

Only 3 devices (6.8%) have been cleared with a Predetermined Change Control Plan (PCCP), representing the cutting edge of adaptive AI regulation:

|                            |                        |                        |
|----------------------------|------------------------|------------------------|
| Device                     | Company                | Significance           |
| Overjet Charting Assist    | Overjet, Inc.          | Workflow automation    |
| VELMENI for DENTISTS (V4D) | Velmeni Inc.           | Comprehensive platform |
| Axial3D Insight            | Axial Medical Printing | 3D surgical planning   |

A PCCP allows manufacturers to make certain pre-specified modifications to their AI/ML algorithms without requiring a new 510(k) submission. This is particularly valuable for AI systems that benefit from continuous learning and improvement.

**Why PCCP Adoption Remains Low**

The low PCCP adoption rate reflects several factors:

1.Regulatory complexity: PCCP submissions require detailed documentation of the modification protocol, performance testing methodology, and change boundaries.

2.First-generation focus: Many current devices are first-generation products focused on initial market entry rather than iterative improvement.

3.Learning curve: Both manufacturers and FDA reviewers are still developing expertise in PCCP evaluation for dental applications.

As the market matures, PCCP adoption is expected to increase significantly, enabling faster innovation cycles and continuous performance improvement.

## The Dental AI Revolution --- Part 3: Economics, Clinical Applications, and the Road Ahead

### Part VIII: The Economics of Dental AI

#### Funding the Innovation

The leading dental AI companies have raised substantial capital:

| Company     | Total Funding  | Latest Round    | Valuation       |
|-------------|----------------|-----------------|-----------------|
| Overjet     | \$133 million  | Series C (2024) | \$550 million   |
| Pearl       | \~\$80 million | Series B (2024) | \~\$400 million |
| VideaHealth | \~\$70 million | Series B (2025) | ---             |

Combined, the top three companies have raised over **\$280 million**, reflecting significant investor confidence in the dental AI opportunity.

#### Estimating the Cost per 510(k)

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a7548053b6b5e1b16bae2ae7.png)

A simple ratio of funding to clearances is misleading, as funding covers all company operations. A more realistic estimate assumes regulatory activities account for an **estimated 15-25%** of a company\'s budget---a higher proportion than general medical devices because dental AI requires substantial clinical validation studies to justify intended use claims.

**Assumptions:**

1.  Regulatory budget is \~15-25% of total funding, reflecting the significant investment in clinical studies.
2.  Costs include FDA user fees, regulatory consulting, and **clinical validation studies** (reader studies, sensitivity/specificity testing, multi-site data collection) required to substantiate performance claims.
3.  First clearance is the most expensive; subsequent ones leverage existing infrastructure.

**Why 15-25%?** Unlike simpler medical devices, AI/ML dental devices must demonstrate clinical performance through rigorous studies. These include reader studies comparing AI performance to ground truth established by expert panels, multi-site validation to prove generalizability across different patient populations and imaging equipment, sensitivity/specificity testing at the acceptance criteria thresholds (86-91% / 79-89%), and data collection and annotation for training and validation datasets.

Based on these assumptions, the estimated cost for a single dental AI 510(k) submission is likely in the range of **\$750,000 to \$2,500,000**.

#### Cost Breakdown

| Cost Category                  | Range                  | Notes                                                  |
|--------------------------------|------------------------|--------------------------------------------------------|
| FDA User Fees                  | \$6,500 - \$26,000     | Small business vs. standard                            |
| Regulatory Consulting          | \$150,000 - \$500,000+ | Strategy, submission support, and regulatory expertise |
| Clinical Validation Studies    | \$150,000 - \$500,000+ | Reader studies, multi-site data                        |
| Data Collection & Annotation   | \$50,000 - \$200,000   | Training and validation datasets                       |
| Software Development & Testing | Variable               | Already part of R&D                                    |
| Quality Management System      | \$25,000 - \$75,000    | Documentation and compliance                           |

*Note: Cost estimates based on industry analysis. Actual costs vary significantly based on device complexity, intended use scope, and company infrastructure.*

The clinical validation component represents the largest variable cost, with complexity scaling based on the breadth of intended use claims and the number of target conditions.

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

### Part IX: Clinical Applications and Use Cases

#### The Diagnostic Focus

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754800db8b9fca52db96624.png)

**77.3% of cleared devices** are classified as diagnostic, reflecting the market\'s emphasis on detection and analysis rather than treatment planning or therapeutic applications.

This diagnostic focus makes strategic sense:

- **Clear clinical need**: Missed caries and early pathology represent significant clinical and liability concerns
- **Well-defined task**: Detection tasks have clear success metrics (sensitivity, specificity)
- **Regulatory pathway**: Diagnostic devices have established predicate history
- **Integration simplicity**: Detection overlays integrate easily into existing workflows

#### Image Type Coverage

| Image Type | Frequency | Clinical Context     |
|------------|-----------|----------------------|
| Periapical | 61.4%     | Single-tooth detail  |
| Bitewing   | 54.5%     | Interproximal caries |
| Panoramic  | 25.0%     | Full-arch overview   |
| Intraoral  | 18.2%     | General category     |

The emphasis on periapical and bitewing radiographs reflects the core use case: detecting interproximal caries and periapical pathology on the most commonly acquired dental images.

### Part X: The Road Ahead

#### 2026 Predictions

![](/img/articles/The_Dental_AI_Revolution_A_Comprehensive_Analysis_of_510k_Clearances_2021-2025-2d2bd5b7a754803a98d7fb1632a1dc88.png)

Based on the exponential growth trajectory, we project continued acceleration in 2026:

| Scenario     | Predicted Clearances | Methodology          |
|--------------|----------------------|----------------------|
| Conservative | 19                   | Linear extrapolation |
| Moderate     | 28                   | Exponential trend    |
| Aggressive   | 36                   | Accelerated adoption |

The **moderate estimate of 28 clearances** represents a **55% year-over-year growth rate**, which would push the cumulative total to approximately **72 devices** by the end of 2026.

### Case Study: Smile Dx: From Concept to FDA Clearance

We are proud of one of our clients for making it on the list of the 18 dental AI devices cleared this year.

#### The Challenge

Dentists often miss subtle caries (cavities) and periapical radiolucencies on standard X-rays. Cube Click wanted to solve this with AI but needed a partner who could handle both the complex deep learning engineering and the rigorous FDA regulatory process. As a bootstrapped startup, they couldn\'t afford missteps or delays.

#### The Solution

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  <figcaption>
    Screenshot of the software Innolitics built
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</div>

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    We built both the AI and the web application around it.
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Innolitics served as Cube Click\'s full-stack engineering and regulatory team. We:

- **Engineered the AI:** trained deep-learning models for automatic segmentation and detection of dental anatomy and pathology.
- **Built the Platform:** Developed a secure cloud-based web application with a built in image viewer.
- **Navigated the FDA:** Managed the entire regulatory lifecycle, from Pre-Submission to a successful 510(k) clearance.
- **Managed the FDA Study:** Managed the entire FDA validation study from ground truthing, multi-reader-multi-case study, standalone performance, and biostats analysis.

<div class="not-prose text-center cta-wrapper"><a href="https://innolitics.com/portfolio/smiledx/" class="bg-secondary text-white rounded hover:no-underline px-6 py-2 inline-block my-2 hover:transition-shadow hover:duration-150 hover:shadow hover:shadow-secondary/50 text-sm text-center">Read about the Smile Dx case study here</a></div>

#### The Result

Smile Dx received FDA 510(k) clearance in June 2025!

### Frequently Asked Questions

<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Why hire a specialist firm instead of a general regulatory consultant?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>General consultants often lack the engineering depth to handle
AI-specific challenges like cloud architecture, GPU training pipelines,
and large-scale data management. Specialist firms like Innolitics
integrate engineering and regulatory strategy, preventing the "hand-off
gap" where technical realities clash with regulatory promises.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>How many dental AI devices have FDA clearance?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>As of December 2025, <strong>44 dental AI devices</strong> have been
cleared. 2025 was a record year with 18 new clearances, signaling a
mature but rapidly expanding market.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Why does the "predicate network" matter for new entrants?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>Choosing the right predicate (e.g., Pearl K210365 or Overjet K222746)
can drastically reduce review times. Building on an established
predicate allows you to leverage existing acceptance criteria (86-91%
sensitivity) rather than negotiating new benchmarks with the FDA.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Should I include a PCCP in my first 510(k)?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>Likely not. Only 6.8% of cleared devices use a Predetermined Change
Control Plan (PCCP). For a first submission, adding a PCCP adds
regulatory complexity and risk. It is often better to secure the initial
clearance first and add a PCCP in a subsequent submission once the
device is on the market.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>What does Innolitics' "Guaranteed Clearance" actually mean?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>It means we share the risk. If your algorithm meets our technical
acceptance criteria during the initial audit, we contractually guarantee
FDA clearance. If we fail to secure it, we refund our fees. This aligns
our incentives with your success and eliminates the open-ended billing
models common in the industry.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>What is the biggest cost driver in dental AI clearance?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p><strong>Clinical validation studies.</strong> Unlike standard
software, AI SaMD requires rigorous reader studies comparing the AI
against a panel of board-certified radiologists. This typically consumes
15-25% of the total regulatory budget.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Do I need a Pre-Submission (Q-Sub) meeting?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>Yes. For AI devices, the Q-Sub is critical for agreeing on your
"Acceptance Criteria" (e.g., aiming for 90% sensitivity) before you run
your expensive reader study. If you skip this and the FDA disagrees with
your study design later, you may have to re-do the entire clinical
validation, wasting months and hundreds of thousands of dollars.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>How much data do I really need?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>Quality trumps quantity. FDA focuses on <em>generalizability</em>. A
dataset of 1,000 images from 5 different clinics using different X-ray
sensors is often more valuable than 100,000 images from a single clinic.
You must prove your AI works across different hardware, patient
demographics, and imaging techniques.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Can I use off-the-shelf AI models for my device?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>Generally, no. FDA requires "traceability" from data to model
performance. You must prove you control the data and the training
process. "Black box" APIs from third parties typically don't provide the
level of version control and data provenance required for a medical
device submission.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>Who owns the Intellectual Property (IP) if Innolitics builds our platform?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>You do. We operate as a service provider, not a venture studio. We do
not take equity or retain ownership of the algorithms, data, or software
we build for you. All deliverables are work-for-hire.</p>
</div></details>
<details class="[&_svg.faq-plus-icon]:open:translate-y-[calc(100%+10px)] [&_svg.faq-plus-icon]:open:opacity-0 [&_svg.faq-minus-icon]:open:translate-y-0 [&_svg.faq-minus-icon]:open:opacity-100 bg-white my-4 py-4 px-3 sm:px-6 rounded border-solid border border-gray"><summary class="flex items-center list-none cursor-pointer [&::-webkit-details-marker]:hidden not-prose"><span class="relative text-center text-xl text-white font-medium mr-4 mt-0 w-[28px] h-[28px] shrink-0 flex justify-center items-center overflow-hidden rounded-full bg-secondary"><svg class="faq-plus-icon absolute transition block w-[12px] h-[12px] fill-inherit" viewBox="0 0 14 14"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 0C7.55228 0 8 0.447715 8 1V6H13C13.5523 6 14 6.44772 14 7C14 7.55228 13.5523 8 13 8H8V13C8 13.5523 7.55228 14 7 14C6.44771 14 6 13.5523 6 13V8H1C0.447715 8 0 7.55228 0 7C0 6.44772 0.447715 6 1 6H6V1C6 0.447715 6.44772 0 7 0Z" fill="currentColor"></path></svg><svg xmlns="http://www.w3.org/2000/svg" class="faq-minus-icon absolute transition block w-[12px] h-auto fill-inherit translate-y-[calc(-100%-10px)] opacity-0" viewBox="0 0 24 24" stroke-width="4" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path d="M0 0h24v24H0z" stroke="none"/><path d="M5 12h14"/></svg></span><h3 class="text-heading m-0"><strong>How do you handle FDA "Additional Information" (AI) requests?</strong></h3></summary><div class="mr-2 mt-6 [&_ul]:list-image-[url(/img/icons/ul-img.png)]"><p>This is where our integrated model shines. When FDA reviewers ask
technical questions (e.g., "re-run analysis on subgroup X"), our
engineers immediately query the database and generate the new tables,
while our regulatory team drafts the response. This tight loop prevents
the weeks of back-and-forth common when regulatory consultants have to
ask client engineering teams for data.</p>
</div></details>

### Conclusion: Don\'t Get Left Behind

The dental AI market has reached a turning point. With 44 devices cleared, 15 serving as predicates for others, and 28 companies competing for market share, the regulatory landscape isn\'t a blank slate anymore. Early movers have set the foundation.

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You need a partner with experience in the Dental AI space and with deep engineering and regulatory experience to get you FDA cleared with speed and certainty.

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Don\'t let regulatory hurdles stall your innovation. Let\'s get you FDA cleared in 2026.

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Already on the market? Protect your market share by speeding up your regulatory clearance process.

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**Innolitics guarantees 510(k) clearance if:**

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- Your algorithm passes our suggested acceptance criteria as we determine during our regulatory strategy and presub service
- And getting FDA clearance is one of the top priorities of your organization

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**Innolitics guarantees 510(k) submission timeline if:**

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- Your algorithm is complete and can be containerized or be packaged as an SDK
- And you allow us to project manage the submission and allow us to communicate directly with your engineers
- And getting FDA clearance is one of the top priorities of your organization

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<div class="not-prose text-center cta-wrapper"><a href="https://innolitics.com/services/ai510ks/" class="bg-secondary text-white rounded hover:no-underline px-6 py-2 inline-block my-2 hover:transition-shadow hover:duration-150 hover:shadow hover:shadow-secondary/50 text-sm text-center">I want a guaranteed pathway to FDA clearance. Let's Chat!</a></div>

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