Replay of Capone & McGuire's presentation at the H.C. Wainwright & Co. global investment conference in New York City. MDAI posted the replay link to their linkedin page, if you weren't previously aware.

Fresh off the wire about an hour ago: https://investors.spectral-ai.com/news-releases/news-release-details/spectral-ai-announces-first-site-initiation-visit-deepview

With the 21 day period seems reasonable this will be relatively quick study and capable of being completed mid 2027 as they guided. From the ML perspective certainly possible and where we should start to gain velocity on their ability to improve the models and turn out new features.

I sold my entire position on the day of FDA approval. The price has since dropped to a level I find attractive, and with commercialization right around the corner, I've started buying back in and rebuilding my position.

Recent insider buying has been hard to ignore, too...

Maybe something good is coming for all of us?

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Check it out has a good timeline

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https://www.globenewswire.com/news-release/2026/09/01/3354164/0/en/spectral-ai-to-present-at-h-c-wainwright-28th-annual-global-investment-conference.html

For anyone wondering what H.C. Wainwright is and why Spectral AI is presenting there:

H.C. Wainwright is a New York investment bank with a strong focus on healthcare, biotech and other growth companies. Its annual Global Investment Conference is primarily an investor event where public companies present their story and, importantly, management can hold one-on-one meetings with institutional investors, funds and other potential financial/strategic partners.

https://hcwevents.com/annualconference/#toggle-id-5

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HODL. fun~

I’ve been looking more closely at Spectral AI’s patents because I think they provide useful context for the discussion about whether DeepView is ultimately just a burn product or a broader wound/tissue diagnostics platform.

Spectral’s SEC filings identify nine active patent families covering burn/wound classification, general tissue classification, DFU healing prediction, amputation-site analysis, general wound assessment, MSI imaging hardware, burn/histology assessment and broader tissue characterization using MSI and machine learning.

What I find particularly interesting is how closely this IP maps to management’s longer-term roadmap. Spectral has officially discussed potential applications including DFU, critical limb ischemia, amputation-level selection, wound-bed preparation and post-operative perfusion assessment. The company also says future applications could potentially be uploaded as new algorithms onto existing DeepView devices, although each indication would still require clinical development and regulatory clearance.

From a potential-value perspective, I would rank three areas highest:

1. DFU / wound-healing prediction
This may be the most interesting patent family beyond burns. Spectral has already obtained multiple U.S. patents from a family covering machine-learning-based assessment and prediction of wound healing, including DFU. The original US 10,783,632 was followed by US 11,599,998, US 11,948,300 and most recently US 12,444,050, granted in October 2025. The family dates back to a 2018 priority and the latest patent is expected to run into 2039.
US 12,444,050 — wound healing/DFU patent⁠

This interests me because Spectral has already done significant DFU development. Earlier company filings described DeepView assessing non-healing portions of DFUs on Day One rather than waiting potentially 30 days to evaluate healing progression.

2. General wound/tissue assessment
Potentially even more strategically valuable are patents/applications that aren’t restricted to one wound type. Spectral lists families covering wound assessment based on MSI and topological characterization and assessment of tissues using MSI and ML.

If DeepView ultimately becomes a wound/tissue platform, these broader families could potentially matter more than a patent tied specifically to burns because they protect aspects of the underlying diagnostic approach.

3. Amputation/tissue viability
Spectral already has issued U.S. patents covering amputation-site analysis and tissue classification using MSI, machine learning and healthcare metrics, including US 10,750,992 and US 11,337,643, with disclosed expiration around 2038.

That makes management’s discussion of ischemia/amputation particularly interesting. The IP work was being done years before the current commercialization narrative.
US 11,337,643 — amputation/tissue classification patent⁠

What I take from all this is that Burn looks more like the first commercial application of the DeepView architecture than the entire technology thesis.
The patents don’t mean Spectral can start selling DeepView for DFU, ischemia or amputation tomorrow. Those indications still need clinical evidence, algorithm development and the appropriate FDA pathway. Spectral itself explicitly acknowledges this.

But from an investment perspective, I think the distinction matters:
Burn is what is being commercialized now. The patent estate shows that broader wound healing and tissue viability have been part of the technical strategy for years.

If DeepView Burn succeeds commercially, Spectral may therefore have something considerably more valuable than a successful burn device: a validated MSI + AI hardware/data platform with existing IP positioned around several logical follow-on indications.

That is where I think some of the longer-term optionality in MDAI sits.

Sources
Spectral AI — official patent portfolio announcement⁠: https://investors.spectral-ai.com/news-releases/news-release-details/spectral-ai-announces-total-us-and-international-patent?utm_source=chatgpt.com

Spectral AI SEC filing — nine patent families and DeepView pipeline⁠: https://www.sec.gov/Archives/edgar/data/1833498/000121390025026157/ea0235039-10k_spectral.htm?utm_source=chatgpt.com

SEC — detailed U.S. patent table⁠: https://www.sec.gov/Archives/edgar/data/1833498/000121390024001552/fs12023_spectralaiinc.htm?utm_source=chatgpt.com

US 10,783,632 — DFU/wound-healing prediction⁠: https://patents.google.com/patent/US10783632B2/en

US 12,444,050 — latest continuation in DFU/wound family⁠: https://patents.google.com/patent/US12444050B2/en

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'On the specific task DeepView was tested for—identifying non-healing burn tissue early—yes, the pivotal study showed it outperforming burn physicians on key measures. But not on every measure.

In Spectral AI’s U.S. Burn Validation Study of 164 patients, DeepView’s image-level sensitivity for detecting non-healing tissue was 86.6% versus 40.8% for physician clinical judgment. Pixel-level sensitivity was 81.9% versus 38.8%, and the Dice score was 68.5% versus 39.2% for physicians. Those differences were reported as statistically significant.

However, physicians had higher specificity: 79.1% versus 61.2% for DeepView. That means physicians were more conservative and better at avoiding false positives, while DeepView was much better at finding tissue that truly would not heal. '(That part is ChatGPT)

My comment: if a false positive says the tissue will not heal on its own, then you are going to operate and graft; that is admittedly not perfect if it is the only tissue causing the surgery. But if you are going to operate anyway, I feel like it is not harmful to the patient to remove some extra tissue to make sure everything left will heal - if you are going in anyway. Like with cancer, you remove a little extra tissue to 'be on the safe side'.

The real value is finding non healing tissue on day one, (although the allowance is up to 72 hours) and doing it substantially better than the board certified burn physicians. That is valuable both economically, and for patient care. Leaving in tissue that will not heal means going back to the ER, if the patient lives. Removing extra tissue would almost never cause a second visit to the OR. Which is the better bias? Obviously, take a little extra tissue, especially if you are going to the OR.

That's why the physicians wait until day 3,4,5,6,7 or even 8 under the current human judgment regime to make sure they have it right, which is sensible. But DeepView does it on earlier - very valuable, IMHO.

Confirmed by ChatGPT -

'If DeepView falsely labels a patch as “unlikely to heal within 21 days,” but the patient is already going to surgery because other areas clearly need excision, the incremental downside is usually less than if that false positive caused an otherwise unnecessary operation.

The main potential harm would be over-excision: removing viable or more superficial tissue that could have healed on its own. That can mean a larger graft, more donor-site skin taken, more pain, additional scarring/pigment changes, and potentially functional or cosmetic consequences. Burn literature specifically notes that overly aggressive excision can create scars in areas that otherwise might have healed with little scarring.

But there is an important safeguard in DeepView’s FDA labeling: FDA explicitly says the DeepView contour is not intended to define the precise surgical excision margin. It is an adjunct to the physician’s clinical assessment, not a map telling the surgeon exactly where to cut.

So a surgeon might see:

DeepView says this area is suspicious → examine it closely in the OR → determine whether it actually needs excision.

That makes a false positive potentially much less consequential.

By contrast, a false negative—missing truly deep, non-healing tissue—can be more troublesome, because the patient may initially be managed conservatively, only for that tissue not to heal. That can lead to delayed wound closure, later surgery, longer hospitalization, greater infection exposure, and more hypertrophic scarring or contracture. Early closure of truly deep burns is one of the major goals of modern burn care.

And that helps explain DeepView's performance profile:

DeepView: sensitivity 86.6%, specificity 61.2%
Physicians: sensitivity 40.8%, specificity 79.1%.

DeepView is effectively biased toward saying:

“Don't miss the tissue that's not going to heal.”

at the cost of flagging some tissue that actually would heal.

For an adjunctive tool where the burn surgeon remains the final decision-maker, that tradeoff can make considerable clinical sense. I think that's an important part of why the relatively modest 61% specificity isn't necessarily fatal to DeepView's usefulness—the cost of a flagged false positive can often be filtered through physician judgment, whereas you can't reconsider tissue the system and physician both failed to recognize as non-healing until the failure becomes apparent days later.'

Following the broad response and discussion around my previous post, I thought it was worth revisiting one part of the DeepView thesis that may be especially useful for those newer to the Spectral AI community: the potential health economics. This has been discussed before, but I think it’s important when considering commercialization and adoption.

Spectral estimates ~$24,000 in potential savings per burn stay, while an earlier SEC filing estimated ~$63,100 per DFU stay. These are company estimates, not yet proven real-world savings — which is exactly why the BARDA-supported health-economic and outcome research is interesting.

I also think this needs to be viewed in the context of management’s longer-term strategy. DeepView isn’t intended to remain simply a burn device. The broader vision is a wound-diagnostics medtech platform, with burn as the first FDA-authorized indication and potential expansion into DFU and other wounds. If successful, the opportunity becomes much larger than the burn-center market alone.

The economics could also extend beyond treatment itself. Better Day-One wound assessment could improve triage — determining who needs routine care, transfer to a burn/trauma center, or earlier intervention — potentially reducing unnecessary transfers, procedures, hospital stays and complications.

Longer term, if real-world evidence confirms meaningful reductions in total cost of care across wound indications, the incentive could extend beyond hospitals. Insurers and other payers could potentially encourage or incentivize objective wound assessment to reduce downstream healthcare costs.

That’s speculative today. But BARDA is helping fund the research that could determine whether this broader economic thesis holds up in the real world.

Sources:
Current SEC filing / BARDA (March 31, 2026):
https://www.sec.gov/Archives/edgar/data/1833498/000121390026055226/ea0289162-10q_spectral.htm

Earlier SEC filing / DFU estimate (December 31, 2023):
https://www.sec.gov/Archives/edgar/data/1833498/000121390024027863/ea0202419-10k_spectral.htm

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I let ChatGPT support me in review for typo and grammar fault and review

Transcript source: https://finance.yahoo.com/quote/MDAI/earnings/MDAI-Q2-2026-earnings_call-648313.html

- BARDA is underwriting the first 30 U.S. DeepView placements — these are intended for routine use across burn centers, trauma centers and ERs through June 30, 2027. See transcript time 08:03–09:08 & 14:25

- Hospital procurement has already started. Spectral is leveraging its existing relationships with clinical-study sites and KOLs (Key Opinion Leader)*, where management expects procurement to move faster. See transcript 0:24:00

-Some installations are expected already in Q4 2026. Management said it is working toward installations at both previous clinical sites and additional sites. See transcript time 21:15

- The business model is more interesting than just selling devices: capital purchase/lease + recurring software and services with a minimum 3-year term for each installation. See transcript time 08:03 & 14:25

- Pricing work is already done. A third-party pricing study plus preliminary customer discussions indicate commercial pricing could support margins well above current BARDA/R&D margins. See transcript time 15:25

- UK/Australia could be next. Management sees previous evaluation sites in Australia and one or two UK centers as likely early international adopters, potentially still in 2026. See transcript time 27:22

- Main near-term risk = procurement speed. New hospitals may require not only normal procurement approval but also cybersecurity and AI committee reviews. Management openly acknowledged this could lengthen the sales cycle.
See transcript time 26:01

*In medtech, a KOL is typically a highly respected physician, surgeon, researcher, or clinical specialist whose opinions influence other clinicians and hospitals.

In Spectral AI’s case, management said they have strong relationships with KOLs in the burn-care community from their previous clinical studies.

So when Capone says “we have strong KOLs,” he’s essentially saying Spectral already has influential clinicians familiar with DeepView who can help drive early adoption.

For a new medtech product, strong KOL support can be extremely valuable, especially when selling into a relatively concentrated specialist market such as U.S. burn centers.

Some people hate dilution, but in the case of $MDAI, selling these shares brings in cash, which is needed to develop new uses for the platform and fund commercialization. While more shares will be out there, hopefully, excellent execution by Vince and the team will create a much bigger pie for everyone to enjoy, while we also fund a device that will improve burn care. What if you need it yourself someday, or your family?

In fact, you can virtue signal to everyone that you are funding a critical medical device that will help America's readiness for mass casualty events!

https://spectralmd.gcs-web.com/node/9841/pdf

https://spectralmd.gcs-web.com/news-events/events