{"id":29874,"date":"2026-08-21T16:07:45","date_gmt":"2026-08-21T13:07:45","guid":{"rendered":"https:\/\/www.intellectsoft.net\/blog\/?p=29874"},"modified":"2026-08-21T16:07:45","modified_gmt":"2026-08-21T13:07:45","slug":"ai-in-healthcare","status":"publish","type":"post","link":"https:\/\/www.intellectsoft.net\/blog\/ai-in-healthcare\/","title":{"rendered":"AI in Healthcare: How It Helps and What Challenges It Brings"},"content":{"rendered":"

AI in healthcare<\/span> provokes two questions. One is about the rate of AI adoption in clinical settings. The other is about the role and use cases of <\/span>artificial intelligence in healthcare.\u00a0<\/span><\/p>\n

In their 2026 Physician Survey on Augmented Intelligence, the<\/span> American Medical Association<\/span><\/a> shares that 81% of physicians now use AI in their work. By contrast, in 2023, only 38% of doctors relied on AI <\/span>healthcare solutions<\/span><\/a>. That\u2019s less than half of today\u2019s figure.<\/span><\/p>\n

In 2025, the number of licensed AI-based medical devices was 1,300, according to the <\/span>FDA report. <\/span><\/a>See how fast AI is becoming a part of health care.\u00a0<\/span><\/p>\n

How is AI applied in health systems, and what does it bring to the table? First, it supports both clinical and administrative work. Second, algorithms process medical data and records, shifting the paperwork away from doctors. Finally, AI automates mundane tasks, so care delivery is faster and more helpful. But these are not the only benefits you can expect from artificial intelligence.\u00a0<\/span><\/p>\n

Of course, these opportunities also come with challenges. And that\u2019s the reason why we want to give you a full picture of<\/span> AI in healthcare<\/span> \u2014 its applications, advantages, and risks.<\/span><\/p>\n

What Is AI in Healthcare?<\/span><\/h2>\n

Artificial intelligence in healthcare<\/span> is the use of algorithms to analyze medical data, detect patterns, and simplify healthcare tasks and decisions. Trained on data and records, AI models help care providers digitize processes and operations. So this leads to faster diagnosis, less manual work, and better patient care.\u00a0<\/span><\/p>\n

According to a review published in the <\/span>Journal of Medical Internet Research,<\/span><\/a> the goal of AI-augmented healthcare systems is not to replace human interaction between a doctor and patient but to make it more fruitful.\u00a0<\/span><\/p>\n

Care providers produce tons of data \u2014 medical records, lab results, images, and patient information. But how to process this in time for better health outcomes? They have two options: load medical staff down with paperwork or hire extra employees. Both of them are costly. Organizations either get exhausted teams who leave soon, or they invest more to scale their team.\u00a0<\/span><\/p>\n

Certainly, they can avoid both scenarios with <\/span>AI solutions<\/span><\/a> that help teams process data faster, get rid of manual work, and use their resources more efficiently.<\/span><\/p>\n

The technologies behind AI in healthcare<\/span><\/h3>\n

The scope of AI technologies health clinics use is quite broad, as they perform different tasks and functions.\u00a0<\/span><\/p>\n

Machine learning <\/b>and <\/span>deep learning<\/b> analyze data and find patterns and trends \u2014 so insights are extracted swiftly. <\/span>Natural language processing <\/b>works with clinical texts. And this leaves space and time for quality care delivery and shifts focus from admin tasks. <\/span>Computer vision <\/b>handles images and video, contributing to disease diagnosis.<\/span> Generative AI<\/b> and <\/span>agentic AI <\/b>based on AI models help generate reports and complete multi-step tasks.<\/span><\/p>\n

\"The<\/p>\n

Why Healthcare Is Adopting AI Now<\/span><\/h2>\n

Healthcare organizations are dealing with more patient data than ever. And it\u2019s not bad, as they can extract insights from it and use them to enhance care delivery. But the main dilemma: data is scattered across different healthcare systems. And instead of fast access when needed, medical staff have to look through many sources to find what they need.<\/span><\/p>\n

That\u2019s why <\/span>AI in healthcare industry <\/span>has become a panacea for messy data and hard-to-reach information. In a 2024 survey published in<\/span> PubMed,<\/span><\/a> 97% of U.S. healthcare executives reported gaps in patient data management as data volume grows. So it\u2019s hard to bring clinical information together and use it to benefit healthcare.<\/span><\/p>\n

The workforce is another factor driving AI adoption. According to<\/span> the AAMC\u2019s report,<\/span><\/a> in the U.S., the number of physicians could shrink by up to 86,000 by 2036. Of course, such forecasts encourage healthcare organizations to look for ways to support clinicians and lighten their workload.<\/span><\/p>\n

Finally, clinics are integrating artificial intelligence because of cost pressure.<\/span> Kaufman Hall\u2019s 2024 Physician Flash Report<\/span><\/a> pinpoints that labor expenses cover 94% of total expenses medical teams spend. It\u2019s a large cost item, so it’s not surprising that care providers are looking for solutions that don\u2019t involve hiring more staff.<\/span><\/p>\n

How AI Is Used in Healthcare: Key Applications<\/span><\/h2>\n

AI in healthcare has many use cases in clinical care, documentation, research, patient engagement, and admin workflows. Some AI usage scenarios can go into production at once, while with others, clinicians move from PoC to a working solution. Let\u2019s go through all AI in healthcare applications!<\/span><\/p>\n

Medical imaging and diagnostics<\/span><\/h3>\n

Medical imaging and diagnostics belong to areas where AI isn\u2019t a novice. AI tools process X-rays, CT scans, MRIs, and other healthcare images. So radiologists can spot possible problems faster, as well as review particular cases. Plus, AI instruments support healthcare specialists with more specific tasks \u2014\u00a0 measuring organs or tumors, defining urgent cases, detecting abnormal conditions, and more.\u00a0<\/span><\/p>\n

Good news: there are already many<\/span> FDA-authorized AI medical devices<\/span><\/a> for radiology. For example, <\/span>Aidoc\u2019s BriefCase-Triage<\/b> can help flag urgent findings on CT scans, while <\/span>Viz Subdural+<\/b> helps analyze images for possible subdural hematomas.<\/span><\/p>\n

Clinical documentation and ambient scribing<\/span><\/h3>\n

Generative AI takes pride of place in clinical documentation. Ambient AI tools can listen to dialogues between a clinician and patient, turn it into a draft note, and send it for review. This <\/span>AI use in healthcare <\/span>saves physicians\u2019 time and lets them commit to patients, without thinking about recording. Add here <\/span>EHR integration<\/span><\/a> with AI, and doctors will work with documents and medical histories faster with more insights from records.\u00a0\u00a0<\/span><\/p>\n

In 2025, <\/span>Cleveland Clinic<\/span><\/a> rolled out Ambience Healthcare’s platform for documentation, integrity, and point-of-care coding. AI makes draft notes, and the task of clinicians is to review and approve them before adding them to electronic health records.<\/span><\/p>\n

Predictive analytics and risk stratification<\/span><\/h3>\n

Predictive analytics helps identify patients who are at risk. Relying on clinical data, it notices patterns and behaviors and turns them into insights for care providers. With<\/span> predictive analytics in healthcare<\/span><\/a>, physicians can start prevention measures to stop disease development and more serious health consequences.<\/span><\/p>\n

For example, Cleveland Clinic uses<\/span> Bayesian Health’s AI platform<\/span><\/a> to help clinicians diagnose patients at risk of sepsis \u2014 all this is done through analysis of lab results, vital signs, and clinical notes in real time. Healthcare organizations opt for <\/span>predictive analytics services<\/span><\/a> to get more insights from their data, minimize risks, and be aware of major trends.<\/span><\/p>\n

Drug discovery and development<\/span><\/h3>\n

Let\u2019s place AI on the pedestal of drug discovery and development. The reason: algorithms help scientists process biological data, set goals, and build molecules. Plus, with AI, they can speed up picking new candidates for research and testing.<\/span><\/p>\n

For example, researchers <\/span>trained a deep neural network <\/span><\/a>to predict antibacterial activity. They relied on AI to screen more than 100 million molecules. If people did this, it would take too much time. But the model didn\u2019t just complete it swiftly but also identified halicin, a new antibiotic candidate against several bacteria, including Mycobacterium tuberculosis.<\/span><\/p>\n

Virtual assistants and patient engagement<\/span><\/h3>\n

The number of patients is booming. The same goes for their requests. Fortunately, virtual assistants can lighten the work for healthcare specialists. They answer routine questions, talk with patients, and help with simple processes. <\/span>Medical chatbots<\/span><\/a> can support both patients and nurses, and that\u2019s the reason why they are in demand among our clients.<\/span><\/p>\n

For instance,<\/span> Mass General Brigham<\/span><\/a> researchers tested GPT-4 for responding to patient messages. They found <\/span>AI and machine learning in healthcare <\/span>could save physicians\u2019 time and provide patients with more detailed responses.<\/span><\/p>\n

Administrative and revenue cycle automation<\/span><\/h3>\n

Parts of medical coding, claims processing, prior authorization, and other admin work \u2014 all these can be digitized with AI. <\/span>AI automation in healthcare<\/span><\/a> can cut mundane paperwork, speed up workflows, and free staff to focus on patients.<\/span><\/p>\n

Let\u2019s take a look at <\/span>recent research<\/span><\/a> on the potential of AI for coding. Scientists trained an XLNet model on 922 surgical notes to automatically generate CPT billing codes. The model achieved up to 88% class-by-class accuracy. And that\u2019s a good point that proves it\u2019s possible to standardize the process and get rid of manual coding work.<\/span><\/p>\n

Remote patient monitoring and wearables<\/span><\/h3>\n

What if doctors could monitor patients between visits, not just during an appointment? Indeed, it\u2019s possible.<\/span> AI used in healthcare<\/span> analyzes data from wearables, sensors, and connected devices. It can detect changes in health data and flag patients who may need more attention.\u00a0<\/span><\/p>\n

In their research, <\/span>Scientific Reports<\/span><\/a> studied 500 patients after hospital discharge who were using smartphones or wearable devices. Researchers found that predictions of 30-day hospital readmission could be more precise if remotely collected activity data were added. And the outputs were better when devices were combined with ML models.\u00a0<\/span><\/p>\n

Robot-assisted surgery<\/span><\/h3>\n

Robot-assisted surgery involves software, cameras, and robotic instruments \u2014 everything to assist surgeons during an operation. But can the robot work on its own? No. The surgeon has to control the instruments through a computer system.<\/span><\/p>\n

According to the <\/span>FDA,<\/span><\/a> these intelligent systems can help surgeons with certain tasks. First, with algorithms, they can perform minimally invasive procedures. Second, they can work in small or hard-to-reach areas. Third, they can observe the surgical field in 3D. Finally, surgeons have precise control of surgical instruments. But a robotic system cannot perform surgery without direct human control. Anyway such <\/span>use of AI in healthcare <\/span>helps surgeons perform operations faster and more efficiently.\u00a0<\/span><\/p>\n

\"How<\/p>\n

Benefits of AI in Healthcare<\/span><\/h2>\n

When discussing the pros and cons of AI in healthcare, it\u2019s logical to start with the advantages of algorithms for clinicians. And today, the number of benefits outweighs risks thanks to advances that AI keeps undergoing.<\/span><\/p>\n

Earlier detection and better diagnosis<\/span><\/h3>\n

AI can make diagnosis faster and easier. It can check medical images, test results, and patient records in seconds. It can recognize patterns that are easy to miss. So doctors find some conditions earlier. AI also helps them focus on patients who are more vulnerable and need more attention. These <\/span>benefits of using AI in healthcare<\/span> encourage care providers to integrate algorithms into their health systems to support both physicians and patients.\u00a0<\/span><\/p>\n

More time for clinicians<\/span><\/h3>\n

Intelligent systems can take on some routine tasks that physicians must perform. As a result, physicians deal with less paperwork and can spend more time with patients. For example, AI scribes can listen to interactions between doctors and patients and create notes of these visits in real time. According to the <\/span>AMA, <\/span><\/a>in 2026, 81% of physicians use AI in their practice \u2014 mostly for documentation.\u00a0<\/span><\/p>\n

Lower costs and faster operations<\/span><\/h3>\n

Repetitive administrative work, such as coding, claims processing, and prior authorization, distracts doctors from their main responsibilities. Luckily, with AI, all this can be automated. The result? Less manual work, more tasks completed with the same resources. The biggest gain is that AI is integrated into existing workflows, speeding up operations.<\/span><\/p>\n

Wider access to care<\/span><\/h3>\n

With AI technologies, healthcare support goes beyond traditional clinical settings. Virtual assistants can answer routine questions, while remote monitoring tools can analyze patient data between visits. So patients have more ways to access basic support. And clinicians can monitor larger patient populations.<\/span><\/p>\n

Faster medical research<\/span><\/h3>\n

With AI, scientists initiate and conduct more research. Algorithms speed up processing large volumes of biological and clinical data. So researchers can easily find targets for testing, screen molecules, and bring more advances into healthcare.\u00a0<\/span><\/p>\n

Challenges and Risks of AI in Healthcare<\/span><\/h2>\n

While AI in healthcare brings to the table, its adoption can also be risky and come with technical, clinical, and regulatory challenges. On one hand, healthcare organizations strive to become more fruitful and efficient. On the other hand, they face the dilemma of how to avoid security issues, biased and false results, and poor governance. Let\u2019s see what else can await them on their pathway!<\/span><\/p>\n

\"Challenges<\/p>\n

Data privacy and security<\/span><\/h3>\n

What are cons of AI in healthcare? <\/span><\/i>The answer is related, first, to <\/span>cybersecurity challenges in healthcare. <\/span><\/a>Medical data contains sensitive patient information that should be safeguarded. That\u2019s why AI systems should protect patient data at every stage\u2014with encryption, access controls, and audit logs.\u00a0<\/span><\/p>\n

Data quality and interoperability<\/span><\/h3>\n

Data quality determines how AI systems operate. Don\u2019t expect high AI model performance if you train it on messy, chaotic, and raw data. Indeed, data should be prepared before development and follow standards such as FHIR to connect electronic health records and other platforms.<\/span><\/p>\n

Algorithmic bias<\/span><\/h3>\n

If training data is missing or poorly represents certain patient groups, AI can repeat these biases in results. As a result, it may work less accurately. It\u2019s vital to use diverse data and test AI across different patient groups. This is how it\u2019s possible to detect issues early and deliver fair care.<\/span><\/p>\n

Sad to say, AI can deliver incorrect or deceptive results. The result: serious clinical consequences from false predictions or diagnoses. So it\u2019s a bad idea to rely on an AI output as a final answer. AI health systems need to be tested. Important clinical decisions should be made after human review of the results.\u00a0<\/span><\/p>\n

Explainability and clinician trust<\/span><\/h3>\n

Clinicians should understand why AI generates specific outputs before they can trust it. If an AI system provides answers without arguments, doctors may feel uncertain about relying on it. AI should show the key data, evidence, or confidence behind its outcomes when needed. A doctor should always make the final decision.<\/span><\/p>\n

Regulatory approval and compliance<\/span><\/h3>\n

AI in healthcare must follow strict rules. In the U.S., the <\/span>FDA<\/b> regulates how to develop and use AI-enabled medical devices. Plus, clinicians must also protect patient data under <\/span>HIPAA<\/b>, where applicable.<\/span><\/p>\n

In the EU, <\/span>GDPR<\/b> protects personal and health data. The <\/span>EU AI Act<\/b> also sets rules for high-risk AI systems used in areas such as medical devices. These rules cover areas such as risk management, data quality, and human oversight. The rules for AI built into regulated medical products are currently scheduled to apply from <\/span>August 2, 2028<\/b>. Other high-risk rules are scheduled to apply from <\/span>December 2, 2027<\/b>.<\/span><\/p>\n

Workflow integration<\/span><\/h3>\n

AI should fit into the tools healthcare teams already use. Why? If AI tools create extra work, you shouldn\u2019t implement them to put a burden on physicians.\u00a0<\/span><\/p>\n

AI should link with clinical workflows, EHRs, and other healthcare systems. This is a great backbone for reducing manual tasks, saving time, and making AI simpler for medical staff to use.<\/span><\/p>\n

How to Implement AI in a Healthcare Organization<\/span><\/h2>\n

Many healthcare institutions believe that picking an AI tool is the most vital step in implementing artificial intelligence. We keep debunking this myth by sharing a step-by-step approach that helps clinicians avoid mistakes.<\/span><\/p>\n

\"How<\/p>\n

Step 1. Define the use case and success metric<\/span><\/h3>\n

Healthcare institutions should start by defining their pain point. The team should define which workflow to enhance, decide who uses AI tools, and know what outputs they can receive. It\u2019s possible to measure AI success with the following metrics:\u00a0<\/span><\/p>\n