Healthcare AI Agents Playbook

Healthcare AI Agents Playbook

• 1. A guide to AI assistants for healthcare delivery AI Agents in Healthcare

• 2. AI Agents in healthcare ii Table of Contents Executive summary 4 The AI revolution in healthcare: why now? 4 ROI snapshot: cost savings and efficiency gains 5 1 What makes AI assistants in healthcare tick? 7 Challenges in implementing Gen AI in clinical settings 8 Specialty-specific implementation hurdles 8 3 Specialty-specific AI assistants 10 AI agents for cardiology 11 AI agents for oncology and hematology 12 AI agents for gastroenterology 13 AI agents for neurology and neurosurgery 14 AI agents for internal medicine 15 AI agents for endocrinology 16 AI agents for dermatology 17 AI agents for emergency medicine 18 AI agents for critical care physicians 19 AI agents for pathologists 20 AI agents for physical medicine and rehabilitation 21 4 A decision framework for Gen AI implementations 22 Assessment matrix 23 Build/Buy: Key evaluation criteria for vendors 23 Vendor selection checklist 23 5 Gen AI playbook for healthcare providers 24 Phase 1: Initial assessment and planning (weeks 1 2) 25 Current state analysis 25 Use case prioritization 25 Technical planning 25 6 The business case for AI assistants in clinical settings 6 2

• 3. AI Agents in healthcare iii Phase 2: Pilot program design (weeks 3-6) Phase 3: Full-scale implementation (weeks 7-8) How we support your AI journey Success metrics framework Next steps Funding support for Gen AI 34 34 Solution development Organization-wide rollout Discovery workshop process Clinical outcomes Training and change management Performance monitoring Proof of concept development Operational efficiency Controlled deployment Continuous optimization Partnership and support model Financial impact 26 27 28 29 26 27 28 29 26 27 28 29 26 27 28 29 Ready to transform your healthcare practice? Featured case study: Case study 2: Case study 3: 30 31 32 33 Max healthcare longitudinal patient data revolution Atria Healthcare intelligent patient profiling Next-gen retinal imaging innovation 7

• 4. AI Agents in healthcare 4 1 Executive summary The AI revolution in healthcare: why now? Healthcare is at a tipping point. With deep domain expertise in healthcare, life sciences, and financial services, we help clients build generative AI pilots in just 8 weeks, leveraging enterprisegrade LLM boilerplates. The convergence of advanced AI, cloud computing, and the explosion of structured and unstructured healthcare data has created unprecedented opportunities to reimagine patient care delivery, clinician workflows, and operational efficiency. This eBook is your practical guide to navigating this transformation. It not only outlines why AI adoption is urgent but also how to go about it, step by step. Inside, you'll find:   Real-world business cases and RšI analysis for hospital administrators and clinical decision-makers™   Key use cases and solution architectures across departments, clinical documentation, diagnostics, patient triage, and operations™   Implementation frameworks and best practices for piloting and scaling GenAI within your healthcare organiŽatio   Case studies from smart hospitals already realiŽing significant improvements in cost, quality, and care coordinatio   Technical deep dives into how AI assistants work, including multimodal input handling, NLP, integration with EHRs, and realtime learning

• 5. AI Agents in healthcare 5 Whether you’re a CMO, CIO, or department head, this eBook will help you evaluate the impact, prioritize use cases, and build a compelling business case for AI adoption within your organization. Financial impact: Z Average annual savings of $2.3M per 300-bed hospitalX Z Reduced malpractice insurance premiums through improved diagnostic accuracyX Z Decreased length of stay through optimized treatment protocolsX Z Enhanced revenue capture through improved documentation and coding Clinical impact: Z Earlier disease detection and interventiot Z Personalized treatment recommendations based on comprehensive data analysisX Z Reduced medication errors and adverse drug eventsX Z Improved care coordination across departments and specialties Operational impact: Z ¯treamlined workflows and reduced redundant processesX Z Optimized resource allocation and staff schedulingX Z Enhanced capacity management and patient flo› Z Improved supply chain management and inventory control ROI snapshot: cost savings and efficiency gains

• 6. AI Agents in healthcare 6 2 The business case for AI assistants in clinical settings Some of the main challenges in healthcare have remained the same over many years. The daily life of many doctors and the operational nature of provider networks has not changed dramatically despite many tech advancements. These are the primary reasons why you should consider AI assistants and copilots in your healthcare system. AI assistants and copilots reduce documentation, support decisionmaking, and free clinicians to focus on what matters most: patient care. It is our point of view that the time to wait and watch is over. There is now ample evidence (from our own customer implementations and other documented stories) that gen AI is not a fad and is here to stay. We believe that adopting AI now will give advantages that compound over time. Late adopters will struggle to catch up. Ž The–brnot–crisis–: In a recent AMIA survey, 74% of physicians said the time spent on documentation impeded patient care: Administrative burden is crushing clinical excellenceˆ Ž Rising– costs,– shrinking– margins– : Healthcare costs continue climbing while reimbursements decline.• Ž Quality under pre‰‰ure : With increasing patient volumes and staff shortages, maintaining consistent care quality becomes increasingly challenging without increasing capacity.

• 7. AI Agents in healthcare 7 3 What makes AI assistants in healthcare tick? Think of AI agents as your most reliable residents, available 24/7, never tired, constantly learning, and backed by the latest medical research. They process vast amounts of data instantly and provide evidence-based recommendations: j Multi-modal kroce]]ing: Combines text, images, lab results, and sensor data\ j Contextual under]tanding: Interprets medical terminology and clinical context\ j Real-time learning: Continuously improves from new data and outcomes\ j Seamle]] integration: Works within existing workflows without disruption.

• 8. AI Agents in healthcare 8 Gen AI assistants and agents integrate smoothly with EHRs, PACS, laboratory systems, and monitoring devices. In many cases, there is no ripand-replace required, just enhanced capabilities on top of your existing systems. However, be aware of potential challenges: Challenges in implementing Gen AI in clinical settings K Data integLation complexities: EHR fragmentation remains the biggest hurdle. Success requires robust data standardization and real-time synchronization capabilities. K Staff tLaining and adoption: Change management is critical. The key is seamless workflow integration that enhances rather than disrupts existing processes. Clinical buy-in early will offset a lot of frustration. K RegulatoLy compliance LequiLements: HIPAAgrade compliance, FDA pathways, and audit trails are non-negotiable. Choose partners with proven healthcare compliance expertise. K Human-in-the-loop (HITL): Always ask about HITL paths. In clinical settings, the doctor owns the final decisions. Specialty-specific implementation hurdles Clinical environments are complex. As many technology providers have learnt over the years, there is no one-size-fits-all. In our experience, here are some unique challenges you may encounter while implementing AI agents for different specialties: K Ca]diology challenges: ECG standardization across devices, real-time processing demands K ‹mergency medicine challenges: K Critical care challenges: K Neurology challenges: Zerotolerance for delays, 24/7 reliability requirementsª Life-critical decisions with no room for errorª Complex neuroimaging processing, surgical integration needs. K InteÀnal medicine and subspecialties challenges: Managing complex multi-system diseases, coordinating comprehensive care across multiple conditions K InteÀnal medicine and subspecialties challenges: K EndocÀinology challenges: K DeÀmatology challenges: K EmeÀgency medicine challenges: Managing complex multi-system diseases, coordinating comprehensive care across multiple conditionsª Balancing delicate hormonal systems, managing lifelong chronic metabolic disorders requiring precise titrationª Distinguishing between thousands of similar-appearing conditions, addressing both cosmetic and lifethreatening diseasesª Zerotolerance for delays, 24/7 reliability requirements demanding immediate critical decision-making under pressure

• 9. AI Agents in healthcare 9 ; Critical care medicine challenges: ; Laboratory medicine and pathology challenges: ; Orthopedics challenges: ; Urology challenges: ; Psychiatry and psychology challenges: ; Pulmonology challenges: ; Nephrology challenges: ; Rheumatology challenges: a Managinga unstableapatientsarequiringaconstantamonitoring,a makinga life-or-deat[a decisionsa wit[a incompletea information` a Ensuringa absolutea accuracya ina diagnostica testing,a interpretinga complexa resultsa affectinga patientaoutcomes` a Combininga surgicala precisiona wit[a biomec[anicala expertise,a managinga bot[a acutea traumaa anda degenerativea conditions` a Addressinga sensitivea intimatea [ealt[a issues,a performinga delicatea proceduresainaanatomicallyac[allengingalocations` a Treatinga invisiblea illnessesa wit[a subjectivea symptoms,amanagingapatientasafetyaandasocietala stigmZ a Managinga lifet[reateninga respiratorya emergencies,a treatinga progressivea diseasesa wit[a limiteda reversiblea treatmentaoptions` a <andlinga irreversiblea kidneya damage,a managinga complexa dialysisa sc[edulesa anda transplanta coordinationa requirements` Diagnosinga elusivea autoimmunea conditions,a balancinga immunosuppressiona benefitsa againsta infectiona risksaandacomplicationsa ; Radiation oncology challenges: ; Œnesthesiology challenges: ; Otolaryngology (ENT) challenges: ; Ophthalmology challenges: ; Radiology challenges: ; Pathology challenges: ; Physical medicine and rehabilitation challenges: Delivering preci e cancer treatment, balancing tumor de truction with healthy ti  ue pre ervation  trategie ` En uring patient  afety during uncon ciou ne  , managing unpredictable reaction  and maintaining phy iological  tability` Operating in confined anatomical  pace , managing` Performing micro copic  urgery on irreplaceable  en ory organ , preventing permanent vi ion lo   complication ` Interpreting  ubtle imaging finding  accurately, managing highvolume  tudie  while maintaining diagno tic preci ion  tandard ` Providing definitive diagno e  from ti  ue  ample , bearing re pon ibility for cancer  taging and treatment deci ionŸ Re toring function after deva tating injurie , managing complex di abilitie  requiring long-term coordination.

• 10. AI Agents in healthcare 10 4Specialty-specific
 AI assistants Consider this chapter as a handy ‘art of the possible’ for discussions with your executives, boards, CMOs, doctors, and technologists. We have put together this list of AI assistants and agents based on our own implementations, conversations with healthcare professionals, and an assessment of what is possible with the gen AI technology we already have. There is a bigger list of AI assistants and copilots (more than 150) in our internal research and we will be pleased to share it with you. Ask us and we will email it. For now, we have focused on 50 use cases that we believe are great to pilot and prove RoI.

• 11. AI Agents in healthcare 11 AI agents for cardiology This AI agent analyzes ECGs to detect critical conditions like arrhythmias, STEMI, AFIB, heart blocks, and ischemic changes. It flags patterns instantly with high accuracy and speed. Standardized reports ensure faster, safer clinical decisions without missing key cardiac events. Eliminates the burden of manual ECG evaluation, reduces diagnostic errors from fatigue or distraction, and ensures no life-threatening arrhythmias are missed during critical care periods. This agent tracks symptoms, vitals, and behavior to predict heart failure decompensation. It builds a personalized baseline and alerts doctors about potential acute events. Enables proactive care adjustments and reduces emergency interventions. Transforms reactive emergency care into proactive management, reduces urgent after-hours calls from deteriorating patients, and provides data-driven insights for optimizing heart failure medications and care plans. This copilot reviews imaging to suggest catheter paths, wire choices, and stent sizing. It simulates procedures, predicts complications, and optimizes contrast use. The copilot helps cardiologists reduce planning time and improve procedural precision. Reduces procedural planning time, minimizes trialand-error during complex interventions, and provides confidence in approach selection, leading to shorter procedure times and improved patient safety outcomes. Automated ECG analysis agent Heart failure monitoring agent Coronary angiography planning copilot This copilot reviews imaging to suggest catheter paths, wire choices, and stent sizing. It simulates procedures, predicts complications, and optimizes contrast use. The copilot helps cardiologists reduce planning time and improve procedural precision. Reduces procedural planning time, minimizes trialand-error during complex interventions, and provides confidence in approach selection, leading to shorter procedure times and improved patient safety outcomes. This assistant auto-analyzes 2D/3D echo images to extract key cardiac measurements. It generates standardized reports, highlights anomalies, and ensures consistency in reports across cases. This can accelerate echo interpretation from 30–45 minutes to just minutes. Dramatically reduces time spent on routine measurements, eliminates inter-observer variability in readings, and allows doctors to focus on clinical interpretation rather than technical analysis tasks. Cardiac risk stratification AI copilot Echocardiogram interpretation AI assistant

• 12. AI Agents in healthcare 12 AI agents for oncology and hematology This agent analyzes imaging (CT/MRI), pathology reports, and biomarker profiles to determine accurate TNM staging. It aligns with current oncology guidelines and incorporates molecular markers to enhance prognosis accuracy. The system produces standardized reports with confidence intervals, ensuring staging uniformity across providers. Ensures staging consistency, saves time on manual assessments, and provides reliable survival data for informed decisions and trial selection. This agent tracks changes in tumor markers, imaging, labs, and clinical symptoms across treatment cycles. It detects early signs of response or resistance before they manifest clinically, allowing proactive intervention. Enables timely intervention, reduces treatment delays, and improves response tracking in complex oncology cases. The copilot analyzes HLA typing, minor antigens, and clinical compatibility factors to rank optimal donor-recipient pairs. It assesses risks like graftversus-host disease and calculates transplant success probabilities. Patient-specific and centerlevel outcomes data enhance match recommendations. Improves match accuracy, minimizes GVHD risk, and expands the usable donor pool, enhancing transplant success. This AI advisor integrates tumor genomics, pharmacogenomics, and clinical guidelines to suggest personalized therapies. It highlights actionable mutations, predicts drug response/ resistance, and prioritizes treatments based on molecular profiling. The engine updates continuously as new evidence and trial data emerge. Boosts use of precision therapies, avoids ineffective treatments, and enhances outcomes via genomics-guided care. Cancer staging and prognosis agent Treatment response monitoring agent Bone marrow transplant matching copilot Precision oncology advisor The copilot dynamically recommends chemotherapy dosing by monitoring pharmacokinetics, organ function, blood counts, and toxicity patterns. It predicts adverse reactions before onset and recommends safe dose modifications based on real-time patient data. The system continuously learns from patient-specific trends to fine-tune regimens. Reduces toxicity-related hospitalizations, supports confident dosing, and improves treatment adherence and patient safety. Chemotherapy dosing copilot Blood smear analysis assistant Using AI -driven microscopy, this assistant analyzes digitized smears to identify abnormal morphology, blasts, dysplasia, or parasites. It delivers rapid differential counts and highlights urgent abnormalities with annotated visuals. Quality control metrics ensure consistent, high-confidence interpretations. Delivers faster diagnostics, reduces interpretive variability, and enables timely treatment for critical hematologic cases.

• 13. AI Agents in healthcare 13 This AI agent analyzes real-time colonoscopy footage using computer vision to automatically identify and classify polyps during procedures. It distinguishes between adenomatous and hyperplastic polyps while alerting physicians to subtle lesions. Significantly increases adenoma detection rates, reduces interval cancer risk, and standardizes polyp identification across skill levels for improved screening outcomes. This AI copilot continuously analyzes inflammatory bowel disease biomarkers, imaging data, and patient symptoms to assess disease activity. It tracks inflammatory markers and predicts flare-ups while providing personalized treatment recommendations. Optimizes treatment timing through predictive analytics, prevents disease flare-ups via early intervention, and improves long-term outcomes through personalized monitoring protocols. Colonoscopy polyp detection agent IBD activity monitoring copilot AI agents for gastroenterology This AI assistant analyzes non-invasive imaging, including elastography and MRI to accurately stage liver fibrosis without tissue samples. It combines multiple assessment tools and provides METAVIRequivalent staging with cirrhosis risk stratification. Reduces dependency on invasive liver biopsies, enables early intervention strategies, and facilitates continuous monitoring of fibrosis progression in chronic liver disease patients. Liver fibrosis assessment assistant This AI agent performs automated analysis of endoscopic images to detect precancerous lesions and early malignancies throughout the digestive tract. It identifies dysplastic changes and provides real-time alerts with detailed morphological analysis during procedures. Improves early cancer detection rates through enhanced sensitivity, standardizes screening protocols across providers, and reduces diagnostic variability in endoscopic interpretation. Endoscopic image analysis agent This AI optimizer creates personalized dietary recommendations by analyzing digestive conditions, symptom patterns, and food intolerances. It considers inflammatory markers and microbiome composition to design optimal meal plans and adjust recommendations based on treatment response. Improves symptom management through evidence-based nutrition plans, enhances treatment compliance via personalized approaches, and optimizes therapeutic outcomes in functional digestive disorders. Nutritional therapy optimizer This AI predictor integrates multiple risk factors including family history, genetic markers, and imaging findings to identify high-risk pancreatic cancer patients. It analyzes trends and calculates personalized risk scores for appropriate screening recommendations. Enables early detection through risk-stratified screening, improves screening protocol effectiveness via personalized approaches, and identifies candidates for intensive surveillance programs.  Pancreatic cancer risk predictor

• 14. AI Agents in healthcare 14 AI agents for neurology and neurosurgery This AI agent performs rapid analysis of brain imaging including CT and MRI to identify acute stroke within minutes of acquisition. It differentiates ischemic from hemorrhagic strokes and calculates severity scores for immediate triage prioritization. Reduces door-to-needle time through accelerated diagnosis, improves functional recovery outcomes via early intervention, and optimizes emergency stroke care protocols. Stroke detection and triage agent This AI copilot continuously monitors EEG patterns and physiological signals to predict seizure onset before clinical manifestation. It analyzes brainwave anomalies and patient-specific triggers to provide early warning alerts for preventive interventions. Reduces seizure frequency through predictive intervention, improves quality of life via proactive management, and enables personalized epilepsy treatment strategies. Epilepsy seizure prediction copilot This AI assistant automatically detects brain abnormalities including tumors, lesions, and structural changes from MRI and CT scans. It provides detailed annotations and differential diagnoses while flagging urgent findings for immediate attention. Accelerates diagnosis through automated screening, reduces radiologist workload significantly, and improves detection accuracy for subtle neurological abnormalities. Neuroimaging analysis assistant This AI agent can continuously monitor tremor patterns, gait abnormalities, and movement characteristics when wearable sensors and video are available. It quantifies symptom severity and tracks medication response in real-time for Parkinson 's and related disorders. Optimizes medication timing through objective monitoring, tracks disease progression accurately, and enables data-driven treatment adjustments for movement disorders. Movement disorder assessment agent This AI copilot creates detailed 3D brain mapping and surgical navigation plans using advanced imaging and anatomical modeling. It identifies critical structures, predicts surgical risks, and optimizes approach routes for maximum safety and efficacy. Reduces surgical complications through enhanced planning, preserves critical neurological function via precise navigation, and improves surgical outcomes through risk stratification. This AI agent performs longitudinal analysis of cognitive function using neuropsychological tests, biomarkers, observations, and imaging data to track dementia progression. It integrates multiple assessment modalities to predict decline trajectories and treatment responses. Enables early intervention through predictive analytics, optimizes personalized care planning, and improves dementia management through comprehensive monitoring protocols. Cognitive decline monitoring agent Surgical planning copilot

• 15. AI Agents in healthcare 15 AI agents for internal medicine This AI agent processes comprehensive patient history, laboratory results, and imaging data to create detailed treatment roadmaps. It identifies key medical patterns, comorbidities, and risk factors while generating prioritized clinical recommendations.  Fast-tracks medical history interpretation through automated analysis, improves diagnostic accuracy via comprehensive profiling, and streamlines clinical decision-making for complex patients. Automated patient profiler This AI agent provides real-time analysis of patient data to suggest differential diagnoses and evidence-based treatment options. It integrates clinical guidelines, patient-specific factors, and recent medical literature for comprehensive decision support. Reduces diagnostic errors through systematic analysis, standardizes care quality across providers, and enhances clinical reasoning with evidence-based recommendations. This AI copilot automatically reviews patient medication lists to identify dangerous drug interactions, contraindications, and dosing errors. It cross-references patient allergies, kidney function, and concurrent medications for safety optimization. Prevents adverse drug events through comprehensive screening, reduces medication errors significantly, and ensures safe polypharmacy management in complex patients. Clinical decision support agent Medication reconciliation copilot This AI assistant continuously monitors chronic disease indicators and automatically adjusts personalized care plans based on patient progress. It tracks guideline adherence and recommends timely interventions for optimal disease control. Improves guideline adherence through automated monitoring, reduces disease-related complications via proactive management, and optimizes chronic care delivery for better outcomes.  Chronic disease management assistant This AI agent integrates multiple predictive models to calculate personalized risk scores for various health conditions. It analyzes patient demographics, biomarkers, and clinical history to stratify risk and recommend appropriate interventions. Optimizes preventive care strategies through risk stratification, reduces unnecessary testing via targeted screening, and enables personalized prevention protocols. Risk assessment agent This AI copilot automates discharge planning by coordinating follow-up appointments, medication reconciliation, and care instructions. It ensures seamless transitions between care settings and providers while optimizing post-discharge monitoring. Reduces readmissions through systematic transition planning, ensures continuity of care across settings, and improves patient safety during care transitions. Care transition coordinator

• 16. AI Agents in healthcare 16 AI agents for endocrinology This AI copilot analyzes continuous glucose monitoring data, meal intake, and activity patterns to provide real-time personalized insulin dosing recommendations. It learns individual response patterns and adjusts recommendations based on lifestyle factors. Improves glycemic control through personalized dosing algorithms, reduces hypoglycemic events via predictive monitoring, and optimizes diabetes management for better patient outcomes. This AI agent automatically analyzes thyroid ultrasound images to assess nodule characteristics and malignancy risk. It applies standardized scoring systems and determines appropriate biopsy recommendations based on established guidelines. Reduces unnecessary biopsies through accurate risk assessment, improves cancer detection rates, and standardizes thyroid nodule evaluation across providers. Diabetes management copilot Thyroid nodule risk stratification agent This AI assistant personalizes hormone therapy recommendations by analyzing patient symptoms, laboratory values, and individual risk factors. It adjusts dosing and formulations based on treatment response and side effect profiles. Improves symptom relief through personalized therapy optimization, minimizes treatment side effects via individualized dosing, and enhances hormone replacement therapy outcomes. Hormone replacement optimization assistant This AI agent performs automated analysis of retinal photography to detect diabetic eye disease and grade severity levels. It identifies hemorrhages, exudates, and neovascularization while providing referral recommendations. Increases screening compliance through accessible automated analysis, prevents vision loss complications via early detection, and improves diabetic care coordination. This AI agent integrates multiple metabolic markers, including glucose levels, lipid profiles, and blood pressure, to predict metabolic syndrome development. It calculates personalized risk scores and recommends preventive interventions. Enables early intervention through predictive risk assessment, prevents progression to diabetes via targeted prevention, and optimizes metabolic health management strategies. This AI copilot continuously monitors cortisol levels and stress indicators to optimize hormone replacement therapy dosing. It predicts adrenal crisis risk and provides real-time dosing adjustments for various stress situations. Prevents life-threatening adrenal crises through predictive monitoring, improves quality of life via optimized hormone replacement, and enhances adrenal insufficiency management.  Diabetic retinopathy screening agent Metabolic syndrome prediction assistant Adrenal insufficiency monitoring copilot

• 17. AI Agents in healthcare 17 AI agents for dermatology This AI agent performs automated dermoscopy analysis to identify melanoma and suspicious skin lesions. It analyzes color patterns, asymmetry, and border characteristics while providing malignancy risk scores. Increases early cancer detection through enhanced screening accuracy, reduces unnecessary biopsies via improved risk stratification, and standardizes skin cancer screening protocols. This AI copilot provides standardized image analysis to grade acne severity and track treatment progress over time. It counts lesions, assesses inflammation levels, and monitors therapeutic response objectively. Optimizes treatment selection through objective severity assessment, monitors progress objectively via standardized grading, and improves acne management outcomes. Skin cancer detection agent Acne severity assessment copilot Psoriasis activity monitoring assistant This AI assistant continuously assesses psoriasis lesion severity using patient-submitted It tracks disease activity, identifies flare-ups early, and recommends treatment adjustments based on progression patterns. Improves treatment timing through continuous monitoring, reduces disease flare-ups via early intervention, and optimizes psoriasis management through objective assessment. Drug eruption identifier This AI agent uses pattern recognition to identify medication-induced skin reactions from clinical photographs and patient history. It correlates timing patterns and reaction characteristics to identify causative medications. Accelerates causative drug identification through systematic analysis, prevents severe reactions via early recognition, and improves adverse drug reaction management. This AI assistant automatically measures wound dimensions and healing progress from smartphone photographs. It tracks healing rates, identifies complications early, and recommends appropriate wound care protocol adjustments. Optimizes wound care protocols through objective monitoring, reduces healing time via early intervention, and improves wound management outcomes. Wound healing tracker This AI copilot performs 3D facial analysis to optimize aesthetic treatment planning and predict outcomes. It simulates procedure results and recommends optimal treatment approaches based on individual facial anatomy. Improves patient satisfaction through realistic outcome prediction, optimizes cosmetic treatment outcomes via precise planning, and enhances aesthetic procedure success rates. Cosmetic procedure planner

• 18. AI Agents in healthcare 18 AI agents for emergency medicine This AI agent automatically prioritizes patients using vital signs, symptoms, and clinical presentation data. It calculates urgency scores and optimizes patient flow through emergency departments for efficient care delivery. Reduces waiting times through fact-based prioritization protocols and optimizes emergency department workflow for better patient handling. This AI agent continuously monitors vital signs, laboratory values, and clinical indicators to predict sepsis onset before clinical deterioration. It provides early warning alerts for immediate intervention. Reduces sepsis mortality through early detection, enables early intervention via predictive monitoring, and improves sepsis management outcomes significantly. Triage severity predictor Sepsis early warning agent This AI agent uses pattern recognition to identify substances causing overdose symptoms from clinical presentation and available testing. It recommends appropriate antidotes and treatment protocols. Accelerates antidote administration through rapid identification, improves overdose survival outcomes, and enhances toxicological emergency management. Drug overdose identification assistant This AI copilot provides automated injury scoring and resource allocation recommendations for trauma patients. It prioritizes treatment interventions and optimizes operating room scheduling based on injury severity. Improves trauma outcomes through systematic assessment, optimizes operating room scheduling via intelligent prioritization, and enhances trauma care coordination. This AI assistant provides real-time step-by-step guidance for emergency procedures, including intubation, central line placement, and resuscitation protocols. It offers visual aids and timing recommendations. Improves procedure success rates through guided assistance, reduces procedural complications via standardized protocols, and enhances emergency procedure performance. Trauma assessment copilot This AI agent rapidly analyzes chest pain symptoms, ECG findings, and laboratory results to determine cardiac risk levels. It provides immediate risk assessment and guides appropriate care pathways.  Optimizes resource utilization through accurate risk assessment, reduces unnecessary hospital admissions, and improves chest pain evaluation efficiency. Chest pain risk stratification agent Emergency procedure assistant

• 19. This AI copilot continuously monitors risk factors, including sedation levels, sleep patterns, and medication effects, to enable early delirium detection. It provides personalized prevention strategies and intervention recommendations. Lessening ICU delirium through predictive risk assessment improves cognitive outcomes via early intervention and enhances neurological recovery in critical patients. AI Agents in healthcare 19 AI agents for critical care physicians This AI agent continuously monitors patient lung mechanics and adjusts ventilator settings in realtime based on respiratory compliance, oxygenation, and CO2 levels. It personalizes ventilation strategies to minimize lung injury. Reduces ventilator-associated lung injury through personalized settings, accelerates the weaning process via optimized protocols, and improves mechanical ventilation outcomes significantly. This AI agent provides real-time cardiac output analysis using multiple hemodynamic parameters to optimize fluid resuscitation and vasoactive medication dosing. It integrates pressure measurements with clinical indicators. Optimizes fluid management through precise hemodynamic assessment, reduces multiple organ dysfunction via targeted interventions, and improves cardiovascular support strategies. Mechanical ventilation optimization agent Hemodynamic monitoring agent Delirium prevention copilot This AI assistant automatically detects healthcareassociated infections by analyzing laboratory data, vital signs, and clinical indicators. It identifies infection patterns and provides antimicrobial stewardship recommendations. Lower infection rates through early detection, optimize antibiotic stewardship programs, and improve infection control measures in intensive care settings. This AI agent analyzes vital signs, laboratory values, and clinical trends to predict cardiac arrest risk before clinical deterioration. It provides early warning alerts for preventive interventions. Reduces cardiac arrests through predictive monitoring, improves patient survival rates via early intervention, and enhances critical care safety protocols. This AI agent uses predictive modeling to optimize bed utilization, staffing requirements, and equipment allocation based on patient acuity and census forecasting. It improves operational efficiency. Optimizes bed utilization through predictive analytics, improves patient throughput efficiency, and enhances ICU operational management for better resource allocation. Infection surveillance assistant Code blue prediction agent ICU resource allocation agent

• 20. AI Agents in healthcare 20 AI agents for pathologists This AI assistant provides intelligent analysis of complex laboratory panels with clinical correlation and trend analysis. It identifies critical values and suggests appropriate clinical actions. Accelerates diagnosis through automated interpretation, reduces laboratory interpretation errors, and improves clinical correlation of complex laboratory data. This AI assistant performs automated cancer detection in tissue specimens using advanced imaging analysis. It identifies malignant cells, grades tumors, and provides diagnostic recommendations. Standardizes pathology reporting through consistent analysis, reduces diagnostic turnaround time, and improves cancer detection accuracy in tissue specimens. This AI agent continuously monitors analytical processes to detect systematic errors, instrument malfunctions, and quality control failures. It provides real-time alerts for corrective actions. Improves test accuracy through continuous monitoring, reduces false laboratory results, and enhances laboratory quality assurance programs significantly.  Automated lab result interpretation assistant Digital pathology assistant Laboratory quality control agent This AI copilot integrates genomic, proteomic, and clinical data to identify novel disease biomarkers and validate diagnostic applications. It accelerates biomarker research and development. Accelerates biomarker development through integrated analysis, improves diagnostic test accuracy, and enhances precision medicine capabilities for personalized patient care. This AI assistant provides automated pathogen identification and antimicrobial susceptibility testing using advanced pattern recognition. It correlates clinical presentation with microbiological findings. Bring down pathogen identification time through automated analysis, optimize antibiotic selection, and improve microbiological diagnostic accuracy for better patient outcomes. This AI agent analyzes blood compatibility, manages inventory optimization, and predicts transfusion requirements. It ensures safe blood product allocation and reduces wastage through predictive modeling. Reduces transfusion reactions through enhanced compatibility analysis, optimizes blood bank operational efficiency, and improves transfusion safety protocols. Biomarker discovery copilot Microbiology identification assistant Transfusion medicine agent

• 21. AI Agents in healthcare 21 AI agents for physical medicine and rehabilitation This AI agent analyzes injury severity, patient demographics, and baseline functional status to predict rehabilitation potential and recovery trajectories. It integrates multiple prognostic factors for comprehensive outcome forecasting. Optimizes treatment planning through evidencebased predictions, sets realistic recovery goals for patients, and improves rehabilitation resource allocation for better outcomes. This AI assistant continuously monitors walking patterns using wearable sensors to analyze stride length, cadence, and balance parameters. It provides real-time feedback and personalized gait training recommendations. Personalizes rehabilitation protocols through objective gait assessment, improves mobility functional outcomes, and enhances walking recovery in neurological and orthopedic patients. This AI assistant provides guidance for prosthetic selection and fitting based on residual limb anatomy, activity level, and functional goals. It optimizes prosthetic alignment and interface design. Improves prosthetic function through personalized fitting algorithms, enhances amputee quality of life, and optimizes prosthetic prescription for individual patient needs. Functional outcome prediction agent Gait analysis assistant Prosthetic fitting assistant This AI copilot performs automated spasticity assessment using sensor-based measurements and provides personalized treatment recommendations including medication dosing and therapy interventions for optimal spasticity control. Optimizes medication timing through objective spasticity monitoring, improves motor function recovery, and enhances spasticity management in neurological rehabilitation patients. This AI assistant continuously monitors exercise intensity, heart rate response, and cardiac parameters during rehabilitation sessions. It provides real-time safety alerts and personalized exercise prescription adjustments. Personalizes exercise prescriptions through cardiac monitoring, prevents cardiac rehabilitation events, and optimizes cardiovascular recovery in cardiac rehabilitation programs. This AI agent optimizes brain stimulation protocols based on neuroimaging data and functional assessments. It personalizes stimulation parameters to maximize neuroplasticity and accelerate neural recovery. Accelerates recovery through optimized brain stimulation, maximizes brain plasticity rehabilitation potential, and enhances neurological rehabilitation outcomes in stroke patients. Spasticity management copilot Cardiac rehabilitation monitoring assistant Neuroplasticity enhancement agent

• 22. AI Agents in healthcare 22 5 A decision framework for Gen AI implementations When implementing AI copilots for your healthcare organization, the abundance of available solutions can make the decision process overwhelming. A structured evaluation framework helps hospital leaders assess which AI copilot aligns best with their specific clinical workflows, technical infrastructure, and strategic objectives. The right choice requires balancing immediate functionality needs with long-term scalability and integration capabilities.

• 23. AI Agents in healthcare 23 We propose a simplified assessment framework for pilots and proof of concepts, which combines outcomes, technology, and vendor: 1 Clinical accu2acy: 1 Integ2ation capabilities: 1 Scalability: 1 Suppo2t st2uctu2e: 1 Vendo2: g Evidence-basedg validation,g peer-reviewedgstudies] g EHRg compatibility,g workflowgseamlessness.f Growt^g accommodation,g multi-siteg deployment] g 24/7g availability,g clinicalg expertise] g Compre^ensiveg vendorg selectiong exercise. Assessment matrix 1 Proven track record: 1 Regulatory compliance: 1 Implementation speed: 1 Ongoing support: 1 Partnerships: Real-world case studies and outcomes] FDA approvals, HIPAA certification] Time to value, minimal disruption] Continuous updates, clinical consultation.f Deep expertise and partnerships with Gen AI ecosystem. The biggest factor that can make or break your pilots is the vendor. In all likelihood, you would need a Gen AI consulting and development partner to recommend the right approach to testing AI within your settings. Look for: Build/Buy: Key evaluation criteria for vendors To summarize, an ideal AI consulting and development partner must have: Vendor selection checklist ✓ ✓ ✓ ✓ ✓ Healthcare-specific expertise and domain knowledge. Enterprise-grade security and compliance frameworks. Rapid deployment capabilities for pilots. Measurable ROI and outcome tracking frameworks. 24/7 support and continuous optimization.

• 24. AI Agents in healthcare 24 6 Gen AI playbook for healthcare providers GoML is a leading AI consulting and development partner for healthcare providers around the world. Based on our own implementations, we have built a comprehensive framework that will take you from your current state to Gen AI ready very quickly.

• 25. AI Agents in healthcare 25 Initial assessment and planning (weeks 1-2) Phase 1: ; Conduct compre4ensi<e workflow assessment across departments3 ; Identify top 3-5 pain points impacting clinical efficiency3 ; Document existing tec4nology infrastructure and integration capabilities3 ; Assess staff readiness and c4ange management requirements. ; Rank opportunities by clinical impact and ROI potential3 ; Focus on 4ig4-<olume, repetiti<e tasks wit4 clear success metrics3 ; Consider regulatory compliance requirements for eac4 use case3 ; Align priorities wit4 organizational strategic objecti<es. Current state analysts Use case prtortttzatton Techntcal planntng ; Define integration requirements wit4 existing EHR systems3 ; Establis4 data go<ernance and security protocols3 ; Plan infrastructure needs for AI deployment3 ; Set up project go<ernance and stake4older communication.

• 26. AI Agents in healthcare 26 Pilot program design (weeks 3-6) Phase 2: P Select the right enterprise-grade LLM boilerplate for rapid deploy6ent5 P Custo6ize AI copilot functionality for specific clinical workflows5 P Configure natural language processing for healthcare ter6inology5 P Develop real-ti6e data integration capabilities. P Create user training progra6s for clinical staff5 P Establish feedback loops for continuous i6prove6ent5 P Design adoption 6etrics and success tracking syste6s5 P Plan co66unication strategy for organizationwide rollout. P Deploy pilot in li6ited clinical environ6ent5 P Test with real patient data under strict security protocols5 P Validate clinical outco6es and operational efficiency gains5 P Gather user feedback and iterate on solution design5 P Acceptance and RoI analysis. Solution development Training and change management Controlled deployment

• 27. AI Agents in healthcare 27 Full-scale implementation (weeks 7-8) Phase 3: N E=ecute phased deployment across all departments< N Monitor system performance and user adoption rates< N Provide real-time support and troubleshooting< N Optimize workflows based on initial deployment learnings. N Track key metrics: documentation time, diagnostic accuracy, decision speed< N Measure cost reduction and operational efficiency improvements< N Monitor user satisfaction and adoption rates< N Oocument clinical outcome improvements. N Implement feedback-driven enhancements< N Scale successful use cases to additional departments< N Plan for future AI capabilities and feature additions< N Establish long-term partnership and support structure. Organization-wide rollout Performance monitoring Continuous optimization

• 28. AI Agents in healthcare 28 How we support your AI journey B Workflow analysis: identify highest-impact opport$nities within yo$r organization# B Technical assessment: eval$ate integration req$irements and infrastr$ct$re needs# B Use case design: create tailored sol$tions for yo$r specific clinical challenges# B ROI modeling: calc$late tangible benefits and investment ret$rns. B B$ild working prototypes with real data integration# B Demonstrate meas$rable val$e thro$gh workflow sim$lation# B Provide o$tcome meas$rement and s$ccess validation# B Ens$re compliance with healthcare reg$lations and standards. B Ongoing optimization and feat$re $pdates.t B Clinical cons$ltation for s$stained val$e delivery# B Performance monitoring and o$tcome tracking.t B Contin$o$s improvement and scaling s$pport. Discovery worksho} }rocess Proof of conce}t develo}ment Partnershi} and su}}ort model

• 29. AI Agents in healthcare 29 Success metrics framework # Diagnostic accuracy impro$ements4 # Clinical decision-making speed4 # Patient safety en5ancements4 # Quality of care indicators. # Documentation time reduction4 # Workflow optimization gains4 # Staff producti$ity impro$ements4 # Resource utilization optimization. # Operational cost reduction4 # Re$enue cycle impro$ements4 # Error pre$ention sa$ings4 # ROI ac5ie$ement and sustainability. Clinical outcomes Operational efficiency Financial impact

• 30. AI Agents in healthcare 30 7 Ready to transform your healthcare practice? GoML has worked with providers around the world to solve the problems of physician burnout and clinical workflow efficiency. We share some of our customer stories below:

• 31. AI Agents in healthcare 31 Max healthcare—longitudinal patient data revolution Featured case study: Challenge: Solution: Results: Impact: Clinicians and analysts struggled to extract insights from vast volumes of patient data stored across multiple sources. Accessing clinical findings from longitudinal patient data often required backend intervention, leading to long delays and fragmented workflows. GoML designed and built a generative AI copilot, leveraging Claude 3.5 on AWS Bedrock for natural language understanding and reasoning. Real-time clinical decision-making, proactive chronic condition management GoML helped Max shift from reactive to proactive care, giving doctors instant access to the data that matters, and transforming how they treat patients.

• 32. AI Agents in healthcare 32 Atria Healthcare—intelligent patient profiling Case study 2: Challenge: Solution: Results: Impact: Patient onboarding times were as high as 3 – 4 months, which was a major roadblock towards scaling their subscription based proactive care approach. Atria was reaching a limit to the number of patients they could effectively treat and predict risks for. AI-powered multi-agent framework that efficiently processes decades of patient history to assist healthcare providers in real-time. Processes 20–30 years of patient history within seconds, creating comprehensive patient summaries.  Helped Atria save a 9 year old's life, with historical data analysis and insights generation within seconds, to identify life threatening condition. 

• 33. AI Agents in healthcare 33 Next-gen retinal imaging innovation Case study 3: Challenge: Solution: Results: Impact: Current systems at the hospital failed to provide early insights required for faster clinical decision-making. These limitations meant every retinal scan required a doctor’s review, making it impossible to scale. AI-powered retinal imaging analysis that significantly speeded up diagnosis of potential conditions like glaucoma, marking high risk scans for doctors’ review, along with vital information. Improved diagnostic speed, accuracy, and treatment outcomes for diabetic retinopathy and other retinal conditions. Scalable retinal screening with faster clinical decision-making and escalation workflows. This is just a sample of the work that has happened around the healthcare world with Gen AI. GoML has a strong suite of case studies, boilerplates, and playbooks to help you on your clinical copilot journey.

• 34. AI Agents in healthcare 34 Next steps 5 Schedule discovery workshop: 5 Get custom ROI analysis: 5 Start 8-week pilot: 5 Plan full implementation: Assess your organization's AI readinessI Understand your speJifiJ investment returnsI Begin with Jontrolled deployment and validationI 6Jale suJJessful pilots organizationwide. Funding support for Gen AI GoML understands the ground reality of piloting and sJaling Gen AI for enterprises very well. That’s why we foJus on building systems that work inside your enterprise workflows. It is neJessary for enterprises that Jan’t afford to waste another quarter in POC limbo. By partnering with us, you Jan gain aJJess to speJial funding programs that are speJifiJally designed for healthJare Gen AI use Jases. You Jan run lean pilots, validate your JonJept in weeks and get enterprise-grade infrastruJture without the upfront Jost. OnJe this is Jomplete, and you wish to sJale, we will equip you with the blueprint for the arJhiteJture, partnerships and support that will take you where you need to go. If you are serious about Gen AI, GoML offers a Jlear path to impaJt, with aJJess to funding programs and a strategy to turn that investment into real results.

• 35. Schedule a free executive AI Briefing We provide complimentary discovery consultations to identify your highest-impact AI opportunities and create a customized implementation roadmap for your organization. We will also answer questions about AI for your executives. Schedule a call now. Schedule: goml.io/demo Website: www.goml.io Contact information: