Top AI Applications in Healthcare: 2025 Edition

Posted in AI Healthcare

Algorithms are not just processing data anymore, but actively changing the future of well-being. From early disease detection through data analysis, deep learning, and predictive modeling, to intelligent systems streamlining operations and personalizing treatments, the impact of AI on healthcare is no longer a promise but solid proof of its potential. According to precedence research, a $36.95 billion global market in 2025 is set to reach $613.78 billion by 2032. Let’s talk about the explosive growth, the top AI applications in healthcare, and their effect.

5+ Best Applications of AI in Healthcare

1. Diagnostics and Medical Imaging 

The application of AI in healthcare (medical imaging) leverages advanced machine learning models and AI systems. They can analyze medical images with a speed, consistency, and precision that often surpasses human capability, leading to earlier detection, more accurate diagnoses, and improved patient outcomes.

Current Advancements

Detection to Diagnosis: AI systems focus on flagging a potential issue for example a nodule on a chest X-ray. Today’s systems use deep learning algorithms, particularly Convolutional Neural Networks (CNNs), to not only detect abnormalities but also to classify, segment, and quantify them. 

AI in pathology can analyze vast digital slides of tissue biopsies to automatically identify cancerous cells and even grade the tumor’s aggressiveness, providing pathologists with a pre-analyzed report.

Multimodal Data Integration: AI can go beyond single-image analysis. Advanced systems now integrate data from multiple sources to provide a more holistic diagnosis. 

A neurology AI might analyze a patient’s CT scan, MRI, and electroencephalography (EEG) data simultaneously to more accurately diagnose a condition like a stroke. This fusion of imaging data with other clinical information, such as genomic data and patient history, is a cornerstone of modern precision medicine.

Real-Time and Portable Imaging: AI is no longer confined to the static environment of a radiology. With portable devices like handheld ultrasound and wireless X-ray systems becoming popular, AI algorithms are enabling diagnostics at the point of care. 

A recent study showed that AI-assisted point-of-care ultrasound improved the diagnostic accuracy of non-radiologists to levels comparable to radiologist-interpreted scans with high-quality images and provide real-time interpretation.

2. Drug Discovery and Development 

Artificial intelligence in healthcare is making rounds in the pharmaceutical sector by tackling the most significant challenges of drug development: the immense costs, long timelines, and low success rates. 

By automating and accelerating key processes, AI is ushering in a new era of precision drug design and rational discovery, moving away from traditional, labor-intensive trial-and-error methods.

Current Advancements

Accelerated Target Identification & Virtual Screening: AI algorithms can sift through and analyze vast, complex datasets to identify drug targets, the specific molecules in the body that a drug needs to interact with. 

This process that may take years for human researchers can be completed by AI in months. Once a target is identified, AI performs virtual screening, computationally sifting through billions of molecules to predict which ones are most likely to bind to the target and have the desired therapeutic effect. 

This drastically reduces the number of compounds that need to be physically synthesized and tested in a lab.

Generative AI for de novo Molecular Design: Instead of just screening existing molecules, generative AI (including models like Generative Adversarial Networks, or GANs) can design entirely new, unique chemical structures from scratch. These AI-generated molecules are “born” with desirable properties, such as a strong binding affinity to the target and a low risk of toxicity. 

This capability allows researchers to explore entirely new chemical spaces, potentially leading to breakthroughs for previously “undruggable” diseases.

Predicting Drug Properties and Clinical Outcomes: Before a new molecule is synthesized, AI can predict its properties, including its toxicity and how it will be absorbed, distributed, metabolized, and excreted by the body (known as ADME-Tox). 

This in silico (computer-based) testing helps researchers prioritize the most promising candidates, avoiding the cost and time of creating molecules that are likely to fail. AI is also being used to predict the likelihood of success in clinical trials by analyzing data from similar compounds and patient populations, helping to de-risk the entire development pipeline.

3. Virtual Health Assistants and AI-driven Telemedicine 

These AI tools have evolved from simple symptom checkers into sophisticated, round-the-clock health management platforms, playing a crucial role in expanding care access, especially in remote areas, and easing the burden on healthcare providers.

Current Advancements

Intelligent Chatbots: Modern virtual assistants use advanced Natural Language Processing (NLP) to engage in natural, human-like conversations. They can perform an initial symptom assessment, providing a preliminary diagnosis and recommending the next steps.

Companies like Ada Health and Buoy Health have created platforms that can assess a patient’s symptoms with high accuracy, serving as a first point of contact that reduces unnecessary in-person visits.

Continuous Remote Patient Monitoring (RPM): AI is a critical component of RPM platforms, which use wearable devices, home sensors, and patient-reported data to monitor chronic conditions like diabetes and heart failure. 

AI algorithms analyze this continuous stream of data in real time, looking for subtle changes or trends that may signal a worsening condition. They can automatically alert a healthcare provider to an impending health crisis, allowing for early intervention and potentially preventing a costly hospital stay.

Administrative Automation: AI assistants are not just for patients; they are also invaluable for providers. They can automate time-consuming administrative tasks, such as patient scheduling, appointment reminders, and billing. 

Ambient listening technology, from companies like Suki AI and DeepScribe, listens to a doctor-patient conversation and automatically generates clinical notes in the electronic health record (EHR), freeing doctors from the burden of documentation.

4. Predictive Analytics for Public Health 

By leveraging AI to analyze vast, complex datasets, public health officials can now anticipate disease outbreaks, forecast healthcare demand, and strategically allocate resources with a precision previously impossible.

Current Advancements

AI-Driven Early Warning Systems: The most critical application of predictive analytics is in the early detection of disease outbreaks. 

AI models pull from a diverse range of unstructured and non-traditional data sources that go far beyond conventional health records. This includes real-time information from social media (e.g., monitoring keywords related to symptoms), internet search queries (e.g., spikes in searches for “fever” or “cough”), news articles, travel patterns (e.g., airline ticket sales), and even satellite imagery to monitor environmental factors that could influence disease vectors, such as mosquito breeding grounds. 

Systems like BlueDot famously used this approach to provide an early warning of the COVID-19 outbreak in Wuhan, China, ahead of official alerts.

Resource Allocation and Strategic Planning: During a public health crisis, a major challenge is ensuring resources like hospital beds, ventilators, medical staff, and vaccines are deployed where they are needed most. 

AI models can simulate outbreak scenarios and predict where demand will surge. This allows optimization of supply chains and logistics. For example, by analyzing historical data from past flu seasons and combining it with real-time syndromic surveillance, an AI system can predict which regions will have the highest patient load.

Proactive Interventions and Policy Modeling: Predictive analytics enables public health officials to test the potential impact of different policies before they are implemented. 

AI models can simulate how a mask mandate, a new vaccine rollout strategy, or a school closure might affect the rate of infection and hospitalization. This data-driven approach helps policymakers make more informed decisions, leading to more effective and equitable public health strategies. 

It can also identify high-risk populations, allowing for targeted interventions to address specific needs and reduce health disparities.

5. Healthcare Administration 

AI’s impact on healthcare administration optimizes workflows, reduce costs, and overall improves experience for both staff and patients. By automating a wide range of administrative tasks, AI frees up human resources to focus on high-value, patient-facing work.

Current Advancements

AI-Driven Patient Flow Optimization: Managing patient flow is a complex logistical challenge. AI uses predictive analytics to forecast patient admissions and discharges, which helps administrators optimize bed assignments and manage capacity. 

By analyzing historical data and real-time patient census, AI can predict when a patient will be ready for discharge, ensuring that a bed is immediately available for the next admission. 

This reduces hiccups, shortens patient wait times in the ED, and improves overall hospital efficiency. Some hospitals have reported a 25% decrease in ED wait times after implementing AI-powered patient flow solutions.

Automated Revenue Cycle Management (RCM): AI is automating tasks like claims processing, coding, and denial management to let users can review claims before submission to identify coding errors, missing information, or inconsistencies that would lead to a denial. 

This approach increases the rate of first-pass claims, ensuring faster and more accurate payments. By analyzing past denials, AI can identify the root causes of rejections and provide actionable insights to prevent them in the future.

Intelligent Workforce and Scheduling Management: A persistent challenge in healthcare is staffing, which often accounts for the largest portion of a hospital’s budget. 

AI addresses this by using predictive analytics to forecast staffing needs based on factors like patient volume, acuity levels, and seasonal trends. The AI can then create optimized schedules that balance patient demand with staff availability, skills, and even personal preferences. 

This not only reduces costly overtime and reliance on agency staff but also improves employee satisfaction and reduces the risk of burnout. Case studies have shown that AI-driven scheduling can reduce nurse overtime by as much as 30% and significantly lower staff turnover.

6. Genomic Sequencing and Analysis

The human genome has billions of data pointsthat holds the key to personalized medicine, disease diagnosis, and a deeper understanding of human biology. 

However, interpreting this immense volume of data is a task far too complex for traditional methods. This is where AI, particularly machine learning and deep learning, has become an indispensable tool.

Current Advancements

Accelerated Variant Calling and Interpretation: Raw genomic data consists of short DNA fragments that must be computationally assembled and compared to a reference genome. 

AI algorithms, especially deep learning models, have dramatically improved the accuracy and speed of this process, known as variant calling. They can more effectively distinguish true genetic variations (mutations, indels, and structural variants) from sequencing errors. 

This allows for a more confident and faster diagnosis, which is crucial for conditions that require immediate intervention. Companies like Illumina and Google’s DeepVariant have developed AI tools that significantly enhance the precision of variant identification.

Decoding the “Dark Matter” of the Genome: The vast majority of the human genome (over 98%) is “non-coding,” meaning it does not contain instructions for creating proteins. For decades, this was considered “junk DNA.” However, we now know it contains crucial regulatory elements that control gene expression. 

AI predicts the function of these non-coding regions and understands how variants within them can contribute to disease. This has opened up new avenues for research into complex conditions like cancer, heart disease, and neurodegenerative disorders.

Phenotype-Driven Diagnosis: A major challenge in diagnosing rare genetic diseases is the sheer volume of possibilities. AI systems are now being used to integrate a patient’s phenotype (observable traits and symptoms) with their genomic data. 

For example, AI platforms like Face2Gene can analyze a patient’s facial features from a photo to suggest a list of possible genetic syndromes. This phenotypic data, when combined with an AI-driven analysis of their genome, significantly narrows down the list of potential diagnoses, shortening what was once a diagnostic odyssey that could take years.

Artificial Intelligence in Healthcare Market Size 2024 -2034

11 AI Apps in Healthcare: Free & Paid 

App Name	Free/Paid	Cost	Rating
Ada	Free (with Paid Options)	Enterprise: $4K–64K/year	App Store: 4.7 iOS / 4.7 Android
Aiddison	Paid	Not disclosed	No public ratings
BioMorph	Paid	Not disclosed	No public ratings
ChatGPT	Free (with Paid Options)	Plus $20/mo, Pro $200/mo, Team ~$25/user/mo	App Store: 4.9 iOS / 4.3 Android
Claude	Free (with Paid Options)	Pro $17/mo, Max $100/mo, Team $25/member/mo	App Store: 4.8 iOS
DAX Copilot (Nuance/athenahealth)	Paid	~$369/month per provider	KLAS Research: ~90/100 (Nuance Dragon rated “Best in KLAS” 2024)
Doximity GPT	Free/Paid	Starts ~$45/month	Capterra (Doximity platform): 4.6/5
Merative (ex-IBM Watson Health)	Paid	Contact vendor (pricing varies by solution)	RepVue: 3.5 / TrustRadius: 7.9/10
Moxi (hospital robot by Diligent Robotics)	Paid	Enterprise-only (pricing not public)	KLAS Research: Highly rated (exact score private, cited ~88/100)
Storyline AI	Paid	“Affordable” (not disclosed)	Capterra (Storyline Creator): 4.9/5
Glass Health	Free (with Paid Options)	Not disclosed	Currently unrated

1. Ada

Ada is a digital symptom checker and health assessment tool powered by a medical knowledge graph and natural language processing. It guides users through structured symptom input, analyzes the responses, and provides a set of possible conditions. Its adaptive design refines accuracy through ongoing interaction with users.

It suggests next steps such as self-monitoring, booking a routine check-up, or consulting a clinician urgently. The tool also supports progress tracking, giving patients and healthcare providers visibility into health changes over time.

• Patient knowledge before a consultation ensures visits are more focused and productive.
  
• Triage function for healthcare providers, separating urgent cases from manageable conditions.
  
• Reduction of unnecessary emergency department visits by directing patients appropriately.
  
• Clear explanations of symptoms help patients feel informed and reassured.
  
• Long-term engagement through continuous tracking and evolving recommendations.
  
• Valuable patient-reported data that supports personalized care planning.
  

2. Aiddison

Aiddison is a leading AI app in healthcare centered on drug discovery. It integrates ligand-based and structure-based drug design approaches to understand molecular interactions with biological targets. The system screens vast numbers of compounds efficiently compared to conventional high-throughput laboratory testing.

The significant advantage of Aiddison lies in accelerating early drug development by predicting which compounds are most likely safe and effective. This reduces the high rate of failure during clinical trials, lowers costs, and increases the pace of bringing new therapies to market.

• Molecular modeling capabilities that evaluate drug-target interactions in detail.
  
• Predictions of compound safety and efficacy before costly lab testing.
  
• Faster compound screening compared to traditional experimental workflows.
  
• Fewer failed candidates are progressing into expensive late-stage clinical trials.
  
• Inhibitor identification for enzymes linked to diseases such as cancer.
  
• Productivity gains in pharmaceutical R&D pipelines.
  
• Data-driven guidance for laboratory experiments, reducing unnecessary trials.
  
• Contributions to precision medicine through targeted compound design.
  

3. BioMorph

BioMorph specializes in predictive analytics applied to large biological datasets. It identifies how chemical compounds influence cellular behavior using advanced machine learning algorithms. 

The system plays a pivotal role in drug discovery by rapidly screening for promising candidates and flagging toxic compounds early. It helps researchers focus resources on molecules with the highest likelihood of success, shortening the path to clinical testing.

• Predictive modeling that highlights active or toxic compounds.
  
• Rapid prioritization of drug candidates based on biological activity.
  
• Strong support for experimental efficiency in research environments.
  
• Discovery of subtle relationships in complex biological data sets.
  
• Application in oncology, with predictions for compounds showing anti-cancer potential.
  
• Early elimination of unpromising molecules saves research time and resources.
  

4. ChatGPT

ChatGPT is a large language model applied in healthcare for administrative support and communication enhancement. It generates summaries, drafts patient communications, and assists with literature review tasks. While not designed for medical diagnosis, its utility in healthcare workflows is expanding.

By automating note creation, educational material preparation, and patient engagement tasks, ChatGPT reduces the administrative load on providers. Its integration into healthcare tools supports both clinician productivity and better-informed patients.

• Wide applicability in summarizing clinical notes and visit documentation.
  
• Patient-friendly educational materials were produced quickly and clearly.
  
• Administrative time savings that allow clinicians to focus more on care.
  
• Integration into healthcare platforms such as Doximity GPT.
  
• Opportunities for research support by sifting through large volumes of literature.
  
• Improved provider-patient communication through clear, accessible content.
  

5. Claude

Claude is another large language model recognized for strong natural language understanding and empathetic tone. It is well-suited for patient-facing interactions such as drafting responses or simplifying medical explanations. Its healthcare adaptations, like Hathr AI, ensure privacy and compliance.

In clinical environments, Claude’s clarity and warmth improve communication by translating complex information into accessible messages. Its contributions support both patient trust and adherence to care plans.

• Expressive and empathetic communication style suited for patient care.
  
• Simplification of complex medical terminology into accessible language.
  
• HIPAA-compliant platforms ensure safe deployment in healthcare settings.
  
• Patient education materials that encourage treatment adherence.
  
• Enhanced trust between patients and providers through supportive messaging.
  
• Practical use in follow-up communications, aftercare instructions, and reassurance.
  

6. Dax Copilot

Dax Copilot addresses clinician documentation burdens through ambient listening technology. It records natural consultations, then generates structured notes integrated into electronic health record (EHR) systems. 

By automating documentation, Dax Copilot reduces clinician burnout, improves communication across care teams, and enhances care quality with accurate records. Its adoption enables more patient-focused consultations.

• Automated note generation covering symptoms, diagnoses, and treatment plans.
  
• Seamless integration into EHR systems such as Epic.
  
• Time savings that allow clinicians to dedicate more energy to direct care.
  
• Improved accuracy of patient records compared to manual note-taking.
  
• Reduced clinician fatigue linked to heavy documentation requirements.
  
• Strengthened communication within care teams through standardized notes.
  

7. Doximity GPT

Doximity GPT brings the capabilities of large language models into a HIPAA-compliant environment. Its use cases include drafting clinical summaries, generating patient communications, and assisting in research workflows. The design ensures secure handling of sensitive health data.

Healthcare professionals benefit from productivity improvements without risking patient confidentiality. The tool bridges the gap between general AI power and healthcare-specific privacy needs.

• HIPAA-compliant safeguards are critical for clinical applications.
  
• Drafting of referral letters, clinical summaries, and communication notes.
  
• Professional and consistent communication style tailored for healthcare.
  
• Efficiency gains for physicians managing documentation and communication.
  
• Secure alternative to consumer-grade AI applications.
  
• Increased patient engagement through clear and timely communication.
  

8. Merative

Merative applies machine learning and statistical analysis to large healthcare datasets. It extracts insights related to clinical care, operations, and finances. 

Hospitals and health systems use Merative to monitor trends, identify risk factors, and design interventions at a population level. Its ability to uncover best practices and predict potential crises supports system-wide improvements.

• Analysis of clinical, operational, and financial healthcare data.
  
• Identification of risk factors and outcome predictors.
  
• Population-level insights that inform treatment optimization.
  
• System-wide efficiency improvements that lower costs.
  
• Prediction of potential health crises through trend monitoring.
  
• Support for healthcare administrators in resource allocation decisions.
  

9. Moxi

Moxi is a hospital robot designed to handle logistical tasks. With built-in AI and sensors, it autonomously navigates clinical environments, delivering supplies, collecting samples, and assisting staff. Its role reduces the load on nurses and clinical teams.

By offloading non-clinical tasks, Moxi improves staff efficiency and allows more time for direct patient care. Its contribution leads to smoother hospital operations and higher patient satisfaction.

• Autonomous navigation in complex hospital settings.
  
• Delivery of medications, linens, and equipment to patient areas.
  
• Collection and transport of lab samples safely and quickly.
  
• Relief of nursing staff from routine logistical responsibilities.
  
• Operational efficiency improvements across hospital workflows.
  
• More available time for clinicians to spend with patients.
  

10. Storyline AI

Storyline AI integrates telehealth functionality with AI-driven data analytics. It gathers patient information through digital interactions, then applies predictive models to assess risks and suggest interventions. 

The platform enhances remote management of chronic conditions, supports personalized care planning, and strengthens patient engagement. Its predictive capabilities enable proactive healthcare delivery at a distance.

• Telehealth integration with continuous patient monitoring features.
  
• Predictive risk assessments guiding proactive interventions.
  
• Personalized care recommendations based on real-time data.
  
• Strong patient engagement through accessible digital communication.
  
• Support for providers managing chronic disease populations remotely.
  
• Early alerts that connect patients with providers before crises develop.
  

11. PathAI

PathAI advances pathology by applying AI algorithms to digital slides of tissue samples. Its systems identify subtle patterns in histology images that are often difficult to detect manually. 

In oncology, PathAI improves cancer staging, grading, and biomarker identification. By augmenting pathologists’ expertise, it contributes to better-informed treatment strategies and reduced diagnostic errors.

• High-resolution slide analysis for subtle disease markers.
  
• Increased accuracy and consistency in pathology diagnoses.
  
• Faster turnaround times compared to manual review.
  
• Identification of prognostic markers critical for cancer care.
  
• Support for oncologists in treatment planning and precision medicine.
  
• Reduced risk of diagnostic error, improving patient outcomes.

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Closing Note 

AI is no longer a concept for the future; it’s business. From reducing operational inefficiencies to enabling faster and more accurate clinical decisions, AI solutions are helping organizations deliver better outcomes while optimizing their resources. For healthcare enterprises, startups, and digital health innovators, the opportunity to lead with intelligent, scalable technology has never been greater. If you’re looking to integrate AI into your healthcare ecosystem with compliance, interoperability, and long-term support in mind, Folio3 Digital Health is your trusted partner for building tailored solutions that drive real business value.

Frequently Asked Questions 

What is the use of AI applications in healthcare?

The best AI applications in healthcare enhance diagnostics, treatment personalization, patient engagement, and administrative efficiency.

What challenges do healthcare providers face when implementing AI solutions?

Challenges include data privacy, integration complexities, implementation costs, and adherence to regulatory compliance.

What are the ethical considerations for using AI in healthcare?

Ethical considerations involve ensuring data privacy, transparency in AI algorithms, addressing biases, obtaining informed consent, and establishing accountability.

What are the most common languages used for AI in healthcare?

The most common AI technologies include:

• Machine Learning (ML) – for pattern recognition and predictive analytics.
  
• Natural Language Processing (NLP) – for interpreting clinical notes and medical records.
  
• Computer Vision – for analyzing radiology images like X-rays, MRIs, and CT scans.
  
• Robotic Process Automation (RPA) – for automating administrative tasks.
  

What role does AI play in drug discovery and development?

AI accelerates drug discovery by identifying potential compounds, predicting their success rates, and even simulating clinical trials, which reduces the timeline from 10 to 15 years and lowers R&D costs, bringing life-saving drugs to market more quickly.

How does AI support medical imaging and diagnostics?

AI algorithms can detect anomalies in X-rays, CT scans, and MRIs with high precision, exceeding human-level accuracy. These systems assist radiologists by flagging potential issues early and streamlining workflow with automated image analysis.

Can AI help in mental health care?

Yes. AI-powered chatbots and mental health apps use natural language processing to detect emotional cues and provide support. Additionally, AI models analyze behavioral data to flag early signs of depression, anxiety, or PTSD, aiding in early intervention.

What are the 10 common applications of artificial intelligence in healthcare?

AI is transforming healthcare across multiple domains. The 11 most promising applications of AI in healthcare include:

1. Medical Imaging & Diagnostics: Analyze radiology scans with near-human or even superhuman accuracy, helping detect diseases like cancer or neurological disorders earlier.
   
2. Predictive Analytics: Forecast patient risks such as hospital readmission or disease progression, enabling proactive care.
   
3. Drug Discovery: Accelerates the process of identifying and testing new drugs, significantly reducing time and cost.
   
4. Personalized Medicine: Customizes treatment plans based on a patient’s genetics, lifestyle, and history, improving outcomes.
   
5. Virtual Health Assistants: Chatbots and tools offer 24/7 patient support, symptom triage, and mental health check-ins.
   
6. Robotic Surgery: AI-assisted robotic systems enhance surgical precision and allow for minimally invasive procedures with quicker recovery times.
   
7. Clinical Trial Optimization: Identifies eligible patients and improves trial design by analyzing health records and trial parameters.
   
8. AI-Powered Genomics: Processes complex genetic data to discover mutations and personalize gene-targeted treatments.
   
9. AI in Pathology: Analyzes pathology slides and tissue samples to detect cellular abnormalities with high accuracy.
   
10. Population Health Management: Monitors public health data to predict disease outbreaks and guide resource planning.
    
11. AI in Nutritional and Lifestyle Guidance: AI-driven wellness apps recommend personalized diets and routines based on individual health profiles.

What are the most innovative AI applications in healthcare today?

• AI-powered clinical documentation tools, like generative AI, assist doctors by transcribing and summarizing patient visits in real-time, reducing paperwork and saving time.
  
• Digital twins for personalized medicine create virtual models of patients to simulate treatments and predict outcomes, leading to highly personalized care plans.
  
• AI in drug discovery analyzes genetic and molecular data to speed up the discovery and development of new medications.
  
• Predictive patient monitoring predicts risks like hospital readmission or sepsis by continuously analyzing patient data, enabling early intervention.
  
• AI-Enhanced medical imaging helps radiologists detect diseases like cancer and neurological disorders more accurately and quickly through image analysis.
  

Which use cases show highest ROI in AI healthcare applications?

The most successful AI in healthcare use cases are: 

• Diagnostic imaging (90%+ accuracy)
• Sepsis prevention (18% mortality reduction)
• Generative ai healthcare for personalized treatments
• Administrative automation (60% time reduction)
• Remote monitoring (40% fewer hospitalizations)

What are the emerging apps of AI in healthcare?

• Chatbots and LLMs
• Electronic health records
• Medical imaging analysis
• Clinical decision support
• Diagnostic AI
• Early disease detection
• Enhanced diagnostic accuracy
• Health Informatics
• IoT framework
• Patient empowerment
• Patient health monitoring
• Radiology AI

What are the best AI applications in healthcare automation?

• Data Management & Cybersecurity: AI systems manage large volumes of healthcare data, allowing quick access to patient records, providing AI-powered cybersecurity to protect sensitive patient data. 
• Fraud Detection: AI algorithms are used to identify fraudulent activities and errors in medical billing and financial analysis. 

Examples of AI tools include: 

• Ada and Buoy Health: AI-powered symptom checkers and conversational AI tools to guide patients to care.
• PathAI and Aidoc: Tools used for pathology and radiology diagnostics.

What are some real-world applications of agentic AI in healthcare?

Real-world applications of agentic AI in healthcare include AI-driven diagnostics, patient monitoring with remote sensors, intelligent appointment scheduling, and a smooth drug discovery process.

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