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Average price: 137 products listed
137 Listings in Healthcare AI Available
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$14.99–$119/mo
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70 tools
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What is Upheal? Upheal is a therapy notes AI agent offering an AI assistant for therapists that writes progress notes and gives session insights. Upheal helps therapists and group practices automate therapy notes work and get results faster. Key capabilities of Upheal Automatic progress notes Treatment plan drafts Session analytics Telehealth and in-person capture Progress note templates How Upheal works Upheal takes audio as input and produces text and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as SimplePractice, TherapyNotes, Zoom and Google Meet, so the agent works inside existing workflows. Who uses Upheal? Upheal is built for therapists and group practices. It suits teams that want automatic progress notes and treatment plan drafts without adding headcount, while keeping people in control of review and final decisions. Upheal vs Mentalyc Upheal is often compared with Mentalyc. Upheal stands out for automatic progress notes and session analytics. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Anumana? Anumana develops ECG-AI algorithms that read standard electrocardiograms to detect hidden heart disease. It was founded by nference in partnership with Mayo Clinic, and its ECG-AI LEF algorithm received FDA 510(k) clearance in October 2023. Key capabilities of Anumana ECG-AI LEF: detects low ejection fraction in patients at risk of heart failure Pulmonary hypertension: breakthrough-designated algorithm in the pipeline Cardiac amyloidosis: breakthrough-designated algorithm in the pipeline Hyperkalemia: breakthrough-designated algorithm in the pipeline Standard ECG input: works from ECGs already collected in care Clinical validation: tested in a multi-site study of 16,000 patients How Anumana works The algorithm analyzes a standard ECG and flags patients who may have low ejection fraction, so clinicians can follow up with confirmatory imaging. In the multi-site validation study, ECG-AI LEF reached 84.5% sensitivity and 83.6% specificity with an AUROC of 0.932. Clinicians make the diagnosis. Who uses Anumana? Health systems and clinicians screening patients at risk of heart failure. A Mayo Clinic trial of 22,641 adults found a 31% improvement in LEF diagnosis versus standard of care. Anumana pricing Anumana does not publish pricing. Quotes come from the vendor. Anumana alternatives Us2.ai automates echocardiogram analysis, Eko Health pairs digital stethoscopes with AI murmur and ECG detection, and Ultromics analyzes echo for heart failure. Anumana works from the ECG.
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What is Kheiron Medical? Kheiron Medical is a breast screening AI agent offering Mia, an AI system that supports radiologists in reading screening mammograms. Founded in 2016 and based in London, United Kingdom, Kheiron Medical helps breast screening services automate AI breast screening work and get results faster. Key capabilities of Kheiron Medical Independent second reader Cancer detection Workload reduction NHS screening support Regulatory-cleared algorithms Worklist prioritization How Kheiron Medical works Kheiron Medical takes image as input and produces insights. It is powered by Mia (in-house) models, with the vendor managing prompts, models and updates. It connects to tools such as PACS, Epic, DICOM and HL7 FHIR, so the agent works inside existing workflows. Who uses Kheiron Medical? Kheiron Medical is built for breast screening services. It suits teams that want independent second reader and cancer detection without adding headcount, while keeping people in control of review and final decisions. Kheiron Medical vs Vara Kheiron Medical is often compared with Vara. Kheiron Medical stands out for independent second reader and workload reduction. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Within3? Within3 is an insights management AI agent offering an insights management platform with AI for life sciences advisory boards and medical insights. Founded in 2008 and based in Columbus, Ohio, USA, Within3 helps medical affairs and marketing teams automate insights management work and get results faster. Key capabilities of Within3 Virtual advisory boards AI insight synthesis Medical affairs insights Compliance controls Evidence traceability Compliance-ready outputs How Within3 works Within3 takes text and audio as input and produces insights and reports. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Veeva CRM, Salesforce, Medidata and Snowflake, so the agent works inside existing workflows. Who uses Within3? Within3 is built for medical affairs and marketing teams. It suits teams that want virtual advisory boards and AI insight synthesis without adding headcount, while keeping people in control of review and final decisions. Within3 vs Veeva AI Within3 is often compared with Veeva AI. Within3 stands out for virtual advisory boards and medical affairs insights. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Laura? Laura is a patient deterioration AI agent offering a Brazilian clinical AI platform that monitors hospital patients and alerts teams to early signs of deterioration such as sepsis. Based in Curitiba, Brazil, Laura helps hospitals in Brazil automate patient deterioration work and get results faster. Key capabilities of Laura Sepsis early warning Patient risk scores Care team alerts Hospital analytics Early deterioration alerts Risk scoring How Laura works Laura takes clinical data as input and produces alerts and scores. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Hospital EMR, HL7, Tasy and MV Soul, so the agent works inside existing workflows. Who uses Laura? Laura is built for hospitals in Brazil. It suits teams that want sepsis early warning and patient risk scores without adding headcount, while keeping people in control of review and final decisions. Laura vs Epic Sepsis Model Laura is often compared with Epic Sepsis Model. Laura stands out for sepsis early warning and care team alerts. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Denti.AI? Denti.AI is a dental imaging and charting AI agent offering dental AI for radiograph analysis, voice perio charting and automated clinical notes. Founded in 2018 and based in Toronto, Ontario, Canada, Denti.AI helps dental practices and DSOs automate dental imaging and charting work and get results faster. Key capabilities of Denti.AI X-ray pathology detection Voice perio charting Auto clinical notes Patient communication Practice management integration Clinician review How Denti.AI works Denti.AI takes image and audio as input and produces insights and text. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Dentrix, Open Dental, Eaglesoft and ezyVet, so the agent works inside existing workflows. Who uses Denti.AI? Denti.AI is built for dental practices and DSOs. It suits teams that want X-ray pathology detection and voice perio charting without adding headcount, while keeping people in control of review and final decisions. Denti.AI vs Overjet Denti.AI is often compared with Overjet. Denti.AI stands out for X-ray pathology detection and auto clinical notes. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Brainomix? Brainomix is a stroke imaging AI AI agent offering AI imaging software for stroke and lung fibrosis that supports faster treatment decisions. Founded in 2010 and based in Oxford, United Kingdom, Brainomix helps stroke networks and hospitals automate stroke imaging AI work and get results faster. Key capabilities of Brainomix E-Stroke CT analysis ASPECTS scoring Lung fibrosis quantification Care network sharing Regulatory-cleared algorithms Worklist prioritization How Brainomix works Brainomix takes image as input and produces insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as PACS, DICOM, Epic and HL7 FHIR, so the agent works inside existing workflows. Who uses Brainomix? Brainomix is built for stroke networks and hospitals. It suits teams that want e-Stroke CT analysis and ASPECTS scoring without adding headcount, while keeping people in control of review and final decisions. Brainomix vs RapidAI Brainomix is often compared with RapidAI. Brainomix stands out for e-Stroke CT analysis and lung fibrosis quantification. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Ubie? Ubie is an AI symptom checker AI agent offering an AI symptom checker and medical AI that guides patients to appropriate care. Founded in 2017 and based in Tokyo, Japan, Ubie helps patients and hospitals in Japan and the US automate AI symptom checker work and get results faster. Key capabilities of Ubie AI symptom checker Disease information Hospital AI questionnaire Pharma partnerships Regulatory approvals Clinical workflow integration How Ubie works Ubie takes text as input and produces text and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as PACS, EHR systems, DICOM and HL7 FHIR, so the agent works inside existing workflows. Who uses Ubie? Ubie is built for patients and hospitals in Japan and the US. It suits teams that want AI symptom checker and disease information without adding headcount, while keeping people in control of review and final decisions. Ubie vs Ada Health Ubie is often compared with Ada Health. Ubie stands out for AI symptom checker and hospital AI questionnaire. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is 5C Network? 5C Network is an AI-assisted teleradiology platform that reports radiology scans for hospitals and diagnostic centers. The company describes itself as operating India's largest teleradiology platform and combines its AI products with a network of radiologists who sign off on reports. Key capabilities of 5C Network Bionic Vision: AI system that analyzes CT, MRI, X-ray and PET-CT scans Bionic Flow: Voice-to-report dictation tool for radiologists Bionic Inside: AI deployment on hospital hardware SecondRead: Independent second-opinion service for patients Multi-modality reporting: Covers CT, MRI, X-ray, PET-CT and nuclear medicine PACS/RIS integration: Connects through DICOM, with live deployment in about 72 hours and no hardware needed How 5C Network works A facility sends studies from its PACS or RIS over DICOM. The AI pre-analyzes the images, and a radiologist from the 400+ strong network reviews and issues the report, with the company citing a 30-minute average turnaround against a 24 to 48 hour industry norm. The company says its AI was trained on more than 4 billion clinical images and that it has reported over 20 million scans, with 20,000+ scans reported daily. Who uses 5C Network? Hospitals and diagnostic centers that lack round-the-clock radiologists use 5C Network to outsource reporting. The company says it serves 2,000+ healthcare facilities. Individual patients can also use SecondRead for an independent opinion on existing scans. 5C Network pricing 5C Network does not publish pricing on its site. It offers 10 free test cases so a facility can evaluate report quality and turnaround before committing, and commercial terms are arranged directly with the company. 5C Network alternatives Alternatives in AI radiology and teleradiology include Aidoc for AI triage alerts, Qure.ai for AI chest and head imaging analysis, and Rad AI for report generation, each covering a different slice of the reading workflow.
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What is Mentalyc? Mentalyc is an AI note-taking tool for therapists and mental health clinicians. It drafts progress notes, treatment plans and related documentation from sessions. Key capabilities of Mentalyc Progress notes: SOAP and DAP notes drafted from sessions AI treatment plans: Available from the Basic plan Alliance scoring and goal tracking: Tracks therapeutic progress EHR transfers: Works with major EHRs such as SimplePractice and TherapyNotes Automatic CPT codes: Included on Pro and above Group therapy notes: Individual member summaries on the Super plan Risk detection: Safety planning support Client worksheets: Matched to sessions How Mentalyc works A clinician records or enters a session and Mentalyc produces a structured progress note in the chosen format. Higher plans add treatment plans, medical necessity documentation, CPT codes and EMDR templates. Notes can then be transferred to the clinician EHR for review. Who uses Mentalyc? Individual therapists and group practices. Mentalyc reports 30,000+ clinicians and a 4.8 out of 5 rating on G2. Mentalyc pricing On annual billing, Mini is $14.99 per month for 40 notes, Basic $29.99 for 100 notes, Pro $59.99 for 160 notes and Super $99.99 for 330 notes. Team is $49.99 per seat per month with unlimited notes. Mentalyc alternatives Alternatives include Nuance DAX Copilot for clinical documentation, Suki AI for voice-based notes and Nabla for ambient scribing.
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What is Regard? Regard is an EHR-integrated clinical AI platform that reviews all of a patient data to recommend diagnoses and generate documentation at the point of care. It also targets missed revenue opportunities. Key capabilities of Regard Clinical notes: Diagnostic support with full clinical context. Mid-revenue cycle: Catches missed diagnoses before denials and queries. HCC capture: Finds risk adjustment opportunities across populations. Screening: Flags patients eligible for high-value interventions. Deep EHR data integration: Maps millions of data points to clinical concepts. How Regard works Regard reads the full chart inside the EHR using proprietary, clinically validated algorithms that map data points to clinical concepts. It then recommends diagnoses and drafts documentation inside the clinician workflow. Clinicians accept or reject each recommendation. Who uses Regard? Regard is used by hospitals and health systems. The vendor cites Sentara Health with a 17 percent increase in CC and MCC capture and Penn Highlands Healthcare with a 20 percent query reduction. Regard pricing Regard does not disclose pricing on its website. It is sold to health systems by contract. Regard alternatives Alternatives include Abridge, which focuses on ambient documentation, Nuance DAX Copilot, which is Microsoft ambient documentation, and Viz.ai, which handles imaging triage and care coordination.
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What is Sunoh.ai? Sunoh.ai is an AI medical scribe that listens during patient visits and converts the conversation into clinical notes automatically. It comes from the eClinicalWorks family and the vendor claims it saves providers 2+ hours daily on documentation. Key capabilities of Sunoh.ai Real-time transcription: Speech-to-text with medical terminology support. EHR-ready notes: Automatic clinical notes in compatible format. Order entry assistance: Labs, imaging, procedures and medications. Multilingual support: Accent recognition. Specialty templates: Customizable by specialty. Mobile and desktop: iOS, Android and desktop access. How Sunoh.ai works Sunoh listens during the encounter, transcribes it with medical terminology support, and drafts a clinical note in an EHR-compatible format. It can also assist with entering labs, imaging, procedures and medication orders. A browser extension covers EHRs without a direct integration, and the vendor states a HIPAA-compliant setup with Business Associate Agreements. Who uses Sunoh.ai? Physicians and practices using EHRs such as Epic, athenahealth, eClinicalWorks, Cerner, Allscripts, Amazing Charts, Meditech, ModMed, NextGen, Practice Fusion and Tebra. Sunoh.ai pricing Exact pricing is not disclosed on the page; users sign up or contact sales for details. Sunoh.ai alternatives Related tools include Abridge, Nuance DAX Copilot, Twofold, Scribeberry and ScribeHealth, which also draft notes from encounters.
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Mentalyc
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Healthcare AI applies machine learning and generative models to clinical and operational work, documentation, patient engagement, scheduling, and administrative automation, with safety, privacy, and compliance as the defining concerns. This guide explains what healthcare AI is, how it works, what matters, and how to choose one.
Healthcare AI applies machine learning and generative models to clinical and operational work, documentation, patient engagement, scheduling, and administrative automation, with safety, privacy, and compliance as the defining concerns. This guide explains what healthcare AI is, how it works, what matters, and how to choose one.
Healthcare AI covers tools that assist clinical and administrative tasks: ambient clinical documentation (AI scribes), patient engagement and triage chatbots, scheduling and intake automation, claims and revenue-cycle automation, and clinical decision support.
Tech stacks
See where healthcare ai fits in a complete stack, with the other software, AI agents and services each business needs.
Most marketplace-relevant healthcare AI focuses on operational and administrative use cases, documentation, communication, and workflow, rather than autonomous diagnosis, which is heavily regulated.
The category is defined by stringent requirements: HIPAA and data privacy, clinical safety, accuracy, and regulatory compliance. Buyers weigh these alongside integration with EHR systems and measurable time or cost savings.
Depending on the use case, AI listens to and documents clinical encounters, answers patient questions and triages, automates scheduling and intake, or processes claims, surfacing outputs for clinician or staff review within compliant workflows.
Platforms combine speech and language models, EHR integration, knowledge grounding, and strict security and compliance controls, with human review for clinical content.
Healthcare organizations configure workflows, integrate with the EHR, and maintain oversight and compliance; AI handles documentation and routine tasks while clinicians and staff verify and decide.
AI scribes capture clinician-patient conversations and draft structured notes for review.
Chatbots answer questions, triage, and guide patients while protecting sensitive data.
Automate appointment scheduling, reminders, and intake to reduce administrative load.
Automate coding, claims, and billing tasks to reduce errors and denials.
Integrate with electronic health record systems so AI fits clinical workflows.
Encryption, access controls, BAAs, and compliance for protected health information.
AI documentation cuts charting time so clinicians focus on patients, not paperwork.
Automating scheduling, intake, and claims reduces staff workload and errors.
24/7 engagement and faster scheduling improve patient experience and access.
Automation reduces documentation and billing mistakes when properly reviewed.
Streamlined workflows free capacity across clinical and administrative teams.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| Ambient AI scribes | Clinical documentation | Practices to health systems | Cuts charting time | Clinician review required |
| Patient engagement AI | Chat, triage, communication | Any | Access and deflection | Safety and privacy critical |
| Administrative automation | Scheduling, intake, claims | Any | Reduces admin load | EHR integration effort |
| Clinical decision support | Evidence and risk surfacing | Health systems | Supports clinicians | Regulatory scrutiny; oversight |
Hospitals & Health Systems: Reduce clinician documentation burden and streamline operations at scale.
Physician Practices: Cut charting time and automate scheduling and intake.
Telehealth: Power patient engagement, triage, and virtual-visit documentation.
Behavioral Health: Ease documentation while protecting sensitive patient data.
Health Insurance / Payers: Automate claims, prior authorization, and member engagement.
Pharmacy: Automate communication, refills, and administrative workflows.
This is non-negotiable. Confirm HIPAA compliance, a signed BAA, and certifications for protected health information.
Verify accuracy and that clinicians review AI-generated clinical content; demand evidence and oversight.
Confirm integration with your EHR so AI fits clinical workflows rather than adding steps.
Check data handling, residency, retention, and whether data trains shared models.
Look for credible evidence of time or cost savings in settings like yours.
Understand per-clinician, per-visit, or volume pricing and how it scales.
Ambient documentation is becoming standard, materially reducing clinician charting burden.
Agentic administrative automation is streamlining scheduling, intake, and revenue cycle end to end.
Regulatory frameworks for clinical AI are maturing, clarifying safe deployment.
Buyers should prioritize HIPAA compliance, clinical safety and oversight, EHR integration, and credible evidence above all.
Healthcare AI applies machine learning and generative models to clinical and administrative work, ambient clinical documentation (AI scribes), patient engagement and triage chatbots, scheduling and intake automation, revenue-cycle and claims automation, and clinical decision support. Most practical deployments focus on operational and documentation tasks rather than autonomous diagnosis, which is heavily regulated.
It can and must be for handling protected health information. Compliant vendors implement encryption, access controls, audit logs, and will sign a Business Associate Agreement (BAA). HIPAA compliance and a BAA are non-negotiable requirements, never use a tool that won't sign a BAA for PHI, and confirm data handling and residency before adopting.
Autonomous diagnosis is heavily regulated and not how most healthcare AI is used. Clinical decision support tools can surface evidence and flag risks to assist clinicians, but a licensed clinician makes the diagnosis and decisions. Any clinical AI should keep humans in the loop and comply with applicable regulatory requirements.
Ambient AI scribes listen to the clinician-patient conversation (with consent) and generate structured clinical notes that the clinician reviews and signs. They aim to reduce documentation burden and burnout. Accuracy and clinician review are essential, and the tool must handle the conversation as protected health information under HIPAA.
It must be, given the sensitivity and regulation of health data. Confirm HIPAA compliance, a BAA, encryption, access controls, data residency, retention policies, and whether data trains shared models. Strong security, privacy, and compliance should outweigh other factors when evaluating healthcare AI.
Leading tools integrate with major EHR systems so documentation and workflows fit clinical practice rather than adding steps. Integration depth varies and can be complex, so confirm support for your specific EHR and how deeply the tool reads from and writes to it.
Common models are per-clinician (PEPM), per-visit/encounter, or volume-based, sometimes as add-ons within EHR or practice-management systems. Estimate your clinician count or visit volume, and weigh compliance, EHR integration, and evidence of savings alongside cost.
Make HIPAA compliance and a BAA, clinical safety and human oversight, and EHR integration your top criteria, then evaluate data privacy and residency, credible evidence of time or cost savings, and pricing. Pilot in a real clinical or operational setting and verify compliance and accuracy before scaling.