Anterior

Christopher Lovejoy, MD breaks down the key differences between machine learning / deep learning versus today's world of LLM-native AI and its impact on healthcare. If you've been running procurement cycles for AI platforms, this post provides an easy breakdown of performance, integration, and data considerations for your vendor comparisons. https://lnkd.in/esBtVQUC

We’re honored to share that Anterior has been named “Best Use of AI for Healthcare” by The International Cloud Artificial Intelligence Awards. This recognition celebrates our mission to transform how health plans work by building AI that’s transparent, clinically grounded, and designed for real-world impact. A big thank you to our incredible team and partners who make this work possible every day and to The Cloud Awards for recognizing innovation that drives meaningful change in healthcare. All winners linked in comments!

Day 1 of #HLTH2025 was a blast. We especially are thankful to KLAS Research for featuring Anterior in their showcase, “Bridging the Divide: Payer Intelligence and Collaborations that Speed Care”. Mustafa Sultan, MD, alongside George Gjermano and Rahul Singal MD of HealthHelp presented how we helped achieved 99.24% clinical accuracy, 76% increase in auto-approvals, and 38 nurse days saved per 1000 reviews for a large payer enterprise. It's work like this that shows us: 🚀 AI can show immediate ROI impact - you just need the right team 🤝 When we embed and design AI directly with clinicians, patients benefit from better care Let's see what day two at HLTH USA holds!

Excited to share that Anterior is featured in Elion’s new AI Prior Authorization Buyer’s Guide. The guide offers one of the clearest looks yet at where prior authorization is headed—from upcoming CMS interoperability requirements to how AI is reshaping workflows across healthcare systems. We’re excited to see more focus on transparency and collaboration in this space. If you’re exploring how to modernize prior auth, this is a valuable place to start. Link in comments.

Thanks for including us amongst these great companies, Harmonic!

The headlines around AI in healthcare are coming fast as seen in this week’s ruling around AI and Medicare Advantage denials. But let’s be clear: this isn’t about one health plan. It’s about the bigger question of how we build trust and accountability into AI as it becomes a part of the fabric of healthcare. At Anterior, our stance is simple: AI should never be a black box. That’s why we designed our platform with trust at its core: - Every AI decision, action, or task has a full audit trail — just like a human reviewer. - All PHI is fully "dehydrated" until the right permissions are accessed. - Most importantly, our AI never makes a denial. Every case that suggests a denial is escalated to a human clinician reviewer. The future of AI in healthcare depends on building systems that don’t just work — but that are explainable, transparent, and trusted by regulators, providers, and patients alike. What are the non-negotiables you're seeing in building healthcare AI?

The recent reports we've been seeing on leaked AI guidelines is a reminder that scale without guardrails creates risk at human speed. What “good” looks like isn’t actually mysterious. It's about auditing what you are already good at: - Evidence over engagement. Clinical outputs must be grounded in guidelines and sources you can see, trace, and challenge. - Accountability by design. Full audit trails for human and AI interactions, versioned models, safety, and security checks before release, continuous monitoring after. - Clinician-in-the-loop where it matters. Automation for the approved, human review for the consequential. Escalates when uncertain. - Measurable safety as table stakes. Independent evaluations will prove your model says what it does, and most importantly is tested for what it should not. This is how we build at Anterior. We’re healthcare‑native, built with clinicians, and our platform is explainable with full auditability so that plans can meet quality and affordability goals without gambling on black‑box behavior. For health plan procurement teams, here are six quick questions to ask any AI vendor today: 1. Show me your product provides clinical recommendations. Ask follow up questions you would of an employee. 2. What happens when the agent does not have the information it needs to answer correctly?  3. What content is unacceptable for your product to enforce, and how is it enforced? 4. How can I audit your product’s outputs, as I would a human? 5. How do clinicians supervise high‑risk steps? 6. Give me an example of where your model’s output was wrong, and what steps did you take to remediate it? AI can relieve healthcare administrative burden and improve member outcomes but only if governance is real, not rhetorical.

Back from The Millennium Alliance in Atlanta, where Anterior's Haris Javed-Akhtar, MD led a keynote panel discussion on the real barriers in AI adoption in health plans. A few moments that stuck with us: Prof Angela L. Perri: Don’t just solve for members or providers -- solve for both. Neglecting one undermines the other. Dr Richard Buzard DO: Health plan employees will always be resource-constrained. If you want adoption, take work off their plate. Brett Bingham: We as an industry need to tell our story better, because there's a lot of good we do in the world. Dr Chris Wheelock: Engagement matters. AI has to be easy for patients and clinicians to use, and integrated into everything that is already happening. Ultimately, success for health plans isn’t measured by the sophistication of the AI, it’s measured by the outcomes it enables for members and providers.

We are hiring FDEs. Anterior

Abdel Mahmoud, MD summed it up perfectly: Building successful vertical AI requires a deep partnership between AI engineers and domain experts (in this case, our clinical scientists), powered by purpose-built systems that enable easy learning and adaptation so that we can unlock the full potential of AI. Check out Christopher Lovejoy, MD, Head of Clinical AI at Anterior's full talk at the AI Engineer summit. Link to the video in comments.