Evercred is an AI-driven credentialing platform that performs primary-source verification (state medical licenses, DEA registration, board certifications, NPI, OIG exclusion, Medicare PECOS enrollment) for physicians, nurse practitioners, and physician assistants, aimed at independent practices, IPAs, MSOs, and the practitioners they credential.

Theator is a surgical intelligence platform that captures and analyzes intraoperative video from minimally invasive procedures using AI and computer vision; its capabilities span surgeon self-review/quality-improvement analytics, an automated "Surgery-to-Text®" operative-report generator, and hospital-level surgical quality/safety analytics, aimed at surgeons, surgical quality/safety leaders, and hospital revenue-cycle teams.

RapidClaims is an AI-powered revenue cycle management platform for healthcare providers, offering autonomous agents for medical coding (RapidCode™), value-based care/risk-adjustment coding (RapidVBC™), denial management/recovery (RapidRecovery™), and RCM services aimed at physician groups, hospitals/health systems, ACOs, FQHCs, ambulatory surgery centers, and management services organizations.

MediCodio AI is an AI-powered medical coding platform combining an automated coding engine (CODIO) with certified human coders, offered in either AI-assisted ("CoPilot") or fully automated ("AutoPilot") modes; it targets hospitals, ambulatory surgery centers, physician practices, and revenue cycle management (RCM) companies handling ICD-10-CM, CPT, and HCPCS coding.

Bayesian Health is a real-time clinical intelligence platform that layers on top of a hospital's EHR to continuously monitor patient data and flag early signs of deterioration; its flagship module detects sepsis risk, and it is aimed at hospital clinicians (nursing and physician teams) and health-system informatics/quality leaders.

XpertDox is an AI-powered autonomous medical coding platform for outpatient and ambulatory practices; its flagship product, XpertCoding, automatically codes medical claims (ICD-10, CPT, HCC/risk adjustment, Category II) and submits them for billing, aimed at billing/coding teams and practice/revenue-cycle managers at clinics, urgent care groups, FQHCs, and similar outpatient settings.

AKASA is a generative AI platform for healthcare revenue cycle management, offering tools for medical coding, clinical documentation improvement (CDI), prior authorization/claim status tracking, and revenue cycle research assistance. It is built for hospital and health system revenue cycle teams.

Athelas RCM is an artificial intelligence-driven revenue cycle management platform that automates medical billing, claims processing, and denial management for healthcare practices.

Fathom evaluates clinical notes and automatically assigns ICD-10, CPT, and modifier codes for patient encounters. The platform processes charts in the background and sends successfully coded encounters directly to the billing system, bypassing manual human intervention. Any chart that the system cannot confidently code is routed to a human coding team, while the AI can also run concurrently to audit human-coded charts for potential errors prior to claim submission.

AEYE uses an artificial intelligence algorithm to analyze retinal images captured by a compatible tabletop or handheld fundus camera. In real life, a clinic staff member takes one photo of each of the patient's eyes without requiring chemical pupil dilation, and the software returns a diagnostic result for diabetic retinopathy in under a minute. This allows point-of-care staff to immediately identify patients who need an ophthalmologist referral while clearing those who do not for another 12 months.

Abstractive Health queries national Health Information Exchanges (such as Carequality) to locate a patient’s historical medical records based on geospatial data, pulling in structured data, CCDAs, and scanned PDFs. It then uses large language models to parse these disparate files into a unified, one-page longitudinal summary. When a clinician reviews the summary, each generated sentence includes a direct link back to the original source document, allowing for immediate verification of clinical claims.

Sully’s AI receptionist acts as the first line of contact for patients calling, texting, or chatting via a clinic's website. It uses natural language processing to understand patient requests, verifies insurance eligibility in real time, and reads the clinic’s scheduling rules to book, reschedule, or cancel appointments. The system directly updates the clinic's calendar and electronic health record (EHR) without requiring human intervention for standard requests.

Switchboard acts as an intelligent middleware layer between patient communication channels (portal messages, phone calls) and the clinical inbox. Instead of messages landing chronologically in a general pool, the AI analyzes the content to determine intent (e.g., clinical symptom vs. billing question vs. scheduling). It then automatically routes the message to the correct department queue and, for clinical queries, pre-drafts a response or tees up orders for physician review.

Elaborate acts as an automated medical assistant that pre-processes incoming clinical messages before a doctor sees them. Instead of a blank reply box, the clinician opens a message to find a pre-drafted, clinically relevant response such as a normal lab result letter or a prescription refill approval ready for signature. It filters out low-value noise and cues up high-value decisions, effectively allowing the physician to "edit and sign" rather than "read, research, and write."

Opmed acts as a central tool for hospital resources. It uses the same mathematical engine originally built for the OR to solve scheduling in other departments. Whether it is fitting a 4-hour surgery into an OR block, aligning a physical therapy session with a patient’s pain medication window, or predicting how many nurses are needed on the medical floor next Tuesday, the tool ingests historical data to predict actual demand and automates the schedule to maximize utilization and regulatory compliance.

The Full Brain Solution runs in the background, automatically analyzing non-contrast CT and CTA head/neck images immediately after acquisition. If the AI detects a potential acute abnormality such as a large vessel occlusion or intracranial hemorrhage, it flags the study as "high priority" in the radiologist’s worklist and sends an alert to the relevant care team (e.g., neuro-interventionalists) via a mobile app. It functions as a retrospective "safety net" and prioritization engine, ensuring critical cases are read first rather than sitting in a queue.

Lunit INSIGHT CXR analyzes frontal chest radiographs in near real-time to identify potential abnormalities. In its primary workflow, it generates an "abnormality score" (0–100%) and a secondary image (heatmap) that highlights the exact location of suspicious findings like nodules, consolidation, or pneumothorax. For US-based users utilizing the FDA-cleared "Triage" module, the system functions as a background "traffic controller," automatically flagging studies with critical findings (e.g., pneumothorax) to move them to the top of the radiologist’s worklist for immediate review.

Lunit INSIGHT MMG analyzes standard 2D mammography images to identify suspicious lesions such as masses, calcifications, asymmetries, and architectural distortions. It assigns an "Abnormality Score" (a percentage probability of malignancy) to each case and generates visual heatmaps that pinpoint the exact location of suspicious areas. The system functions primarily as a "second reader," allowing radiologists to review the case unaided first, then toggle the AI overlay to ensure no subtle findings were missed before finalizing the report.

Cohere Health replaces the traditional "fax-and-wait" prior authorization model with a digital intake process that sits between the provider and the health plan. When a clinician orders a service, the tool analyzes clinical data (often extracted directly from the EHR) against the payer’s policy guidelines in real-time. Instead of issuing a flat denial or requiring a manual review days later, the system attempts to "green-light" the request instantly or provides immediate "nudges" to guide the provider toward a pre-approved, evidence-based care path.

Myndshft is a background infrastructure tool that automates the "investigative" phase of patient intake and ordering. When a clinician orders a service or specialty drug, the software instantly queries payers to determine (1) if the patient is covered, (2) if a prior authorization is required, and (3) whether that authorization falls under the patient’s medical benefit or pharmacy benefit. If an authorization is required, it autopopulates the necessary forms with clinical data from your system and submits them directly to the payer, bypassing the need for staff to log into individual insurance portals.