AI in healthcare: Uses, examples, and benefits

You might not realize it, but artificial intelligence already shapes much of our healthcare experience. In a recent NVIDIA survey on the state of AI in the healthcare and life sciences industries, two-thirds of over 600 healthcare professionals reported actively using AI solutions across capacities ranging from drug discovery to imaging, diagnostics, and more.

In hospitals, radiologists are using AI models to analyze scans and detect abnormalities that even experienced human eyes might miss. Similarly, clinicians are employing AI to analyze patient histories in seconds, flag early warning signs, improve diagnostic accuracy, and suggest personalized treatment recommendations. For healthcare providers, that means improved efficiency, allowing a greater focus on patient care. And for patients, it promises more precise care and improved quality of life.

In this guide, we’ll examine the impact and benefits of AI applications in healthcare, along with the challenges and ethical considerations to keep in mind.

What is AI in healthcare?

AI in healthcare means the use of artificial intelligence technologies, such as machine learning (ML), predictive analysis, and natural language processing (NLP), to enhance medical decision-making, improve patient outcomes, and optimize hospital operations. It involves using AI models and tools across healthcare segments, from pharmaceuticals and biotech to laboratory testing, medical equipment, and even administrative tasks like research and patient intake.

When deployed in healthcare settings, artificial intelligence can enhance the overall effectiveness of medical systems. For instance, AI algorithms already integrate with electronic health records (EHRs) to extract patient medical history, analyze vast amounts of data, and help physicians identify patient risks. AI-powered tools, such as wearable devices, monitor sleep patterns, stress levels, respiration rates, and other parameters to help doctors manage patients, detect abnormalities, and adapt treatment recommendations over time. And in telemedicine, AI applications like chatbots and virtual assistants triage patient inquiries, streamline scheduling, and support remote monitoring. All together, these applications make healthcare more personalized and effective for patients and providers alike.

Common uses of AI in healthcare  

One of the biggest misconceptions about using AI in healthcare is that AI will make the role of healthcare providers obsolete. With artificial intelligence visibly handling routine diagnostics and recommending first-line treatments, the fear of technology is valid. But despite the widespread adoption of AI, humans will always be relevant in healthcare for several reasons, including empathy and the nonlinear methods of clinical diagnostics.

In fact, the Royal College of Surgeons of England believes that adopting AI in healthcare has the potential to create new jobs that did not previously exist. Instead of replacing people, AI healthcare tools will repurpose roles and equip those physicians who embrace them to make better decisions and improve patient relationships.

The following section outlines some of the most common applications of AI in healthcare.

Medical imaging analysis

AI algorithms can assist radiologists in detecting tumors, lesions, bone fractures, and other abnormalities from CT scans, X-rays, and MRIs. Using deep learning models, these tools can enhance images, analyze data, and identify patterns that enable the early detection of cancers as well as cardiovascular and neurological diseases. A study by the University of Hawaii Cancer Center found that deep learning AI models can even distinguish between the mammograms of women more likely to develop breast cancer someday and those who are not.

Disease detection and diagnosis

AI tools using technologies like machine learning and predictive analysis can accurately pick up biomarkers in individuals that are highly predictive of developing diseases such as Alzheimer’s or kidney disease, among others.  

ML research tool MILTON, an AI algorithm developed by pharmaceutical giant AstraZeneca, has the capability to predict individuals at risk of certain diseases based on blood and urine tests, blood pressure, and respiratory function years before a patient is ever diagnosed. With AI, healthcare providers can focus more on preventive care, improving patient outcomes, and quality of life.

Drug discovery and development

Artificial intelligence is aiding drug research by analyzing datasets more quickly and predicting molecular interactions to streamline various stages of the drug development process. In this way, AI helps reduce the cost and time of clinical trials.

Personalized treatment 

Physicians can use AI to tailor preventive care, medical treatment, and interventions to individual patients based on their genetic makeup, lifestyle, and medical history. This is known as precision medicine. In cases like diabetes treatment, for example, AI can provide insights for developing personalized dietary plans and exercise regimens to help patients manage blood sugar levels. Personalized treatments can also include AI-powered wearable devices for monitoring and adjusting interventions based on real-time data.

Administrative tasks and workflow automation 

In a recent American Medical Association survey, 57 percent of physicians reported using AI to address administrative burdens. From data entry and note-taking during appointments to medical coding and claims processing afterward, AI can automate most of the routine tasks that slow down medical care.

AI-powered virtual assistants and chatbots can help with appointment scheduling, patient triage, and communication. They answer questions, make preliminary diagnoses, and automatically sync data to the patients’ EHRs. Specialized large language models (LLMs) like Med-PaLM can also help providers and administrative personnel with research and patient education.

Benefits of AI in healthcare

With so many applications in healthcare already, it’s clear that AI is delivering measurable benefits across the board for patients, clinicians, and the broader healthcare ecosystem. Together, these advances are leading to more proactive, data-driven, and human-centered care. Here are some of the most significant benefits of AI in medicine today:

• Reduced cost of care
• Better decision-making
• Improved operational efficiency
• Better doctor-patient relationship
• Earlier and more accurate diagnoses
• Reduced human error and clinical burnout
• Enhanced accessibility and continuity of care

Challenges and ethical considerations of AI in healthcare

Despite the substantial benefits AI brings to healthcare, its use also raises serious concerns about data privacy, accuracy, bias, accountability, and even cost. According to NVIDIA’s report, these challenges often differ based on the size of the healthcare organization. Small and medium-sized organizations may struggle with the financial challenges of implementing AI, while larger companies face greater data-privacy and regulatory complexities.

Either way, the healthcare industry is highly sensitive, and even minor missteps in AI deployment can have major consequences, ranging from legal liabilities to ethical lapses that erode public trust. As a result, it’s important that you understand the challenges and ethical considerations surrounding AI in healthcare to ensure its safe and responsible implementation.

Data privacy and security issues

From electronic health records to imaging scans, AI systems rely on massive volumes of sensitive patient data to function effectively. This dependence increases the risk of data leaks and privacy breaches, especially if patient information is mishandled or used without consent. Consider, for instance, the case of Medtronic. In 2023, the Minneapolis-based medical device manufacturer faced a class action lawsuit, alleging that its InPen diabetes management app used tracking technologies like Google Analytics, Firebase, and Crashlytics without adequate user consent or protection.

Cyberattacks on healthcare databases have also become increasingly common. Such incidents can compromise patient care, expose confidential records, and lead to identity theft or financial fraud. And as more healthcare systems integrate AI tools, the potential attack surface grows even larger.

To mitigate these risks, healthcare organizations should strengthen data governance practices and adopt comprehensive security practices. This includes robust encryption of patient data, strict access controls, regular audits, and the use of secure cloud environments. Compliance with regulatory requirements such as the Health Insurance Portability and Accountability Act (HIPAA) is equally essential to protect patient trust and organizational integrity.

Algorithmic biases

Bias is a critical concern when implementing artificial intelligence for predictive decision-making. If the training data for an AI model underrepresents certain patient groups — based on race, gender, age, or socioeconomic status — the model can produce biased predictions, leading to misdiagnoses or unequal treatment outcomes. To avoid this risk, healthcare organizations must prioritize diversity in AI training data and continuously evaluate model performance across different patient groups. Regular bias testing is also important, along with transparent reporting and human oversight to ensure your AI technology serves all patients fairly.

High costs of AI implementation

Building and deploying AI systems in healthcare is expensive. From infrastructure upgrades to data management and computing costs, staff training, and ongoing maintenance, the financial burden can be a challenge for smaller organizations.

Employing a phased approach to implementation that targets specific use cases first, such as scheduling automation or medical imaging, can help manage costs before expanding system-wide. And over time, the efficiency gains from AI often offset initial investments.

Ethical concerns about decision-making in patient care

One of the biggest ethical questions surrounding AI in healthcare involves responsibility. Who is ultimately responsible when AI influences patient care? If an algorithm recommends a treatment plan or flags a diagnosis, should clinicians follow it, or can they override it?

The truth is, there’s always a risk of overreliance when AI is implemented into any system, including healthcare. And because AI can sometimes generate inaccurate or fabricated information, relying on its output without verification can lead to misdiagnoses. On the other hand, dismissing its insights entirely might mean missing valuable opportunities for better care in certain contexts.

The wisest solution is to treat AI healthcare tools as a decision-support system rather than a replacement for clinical expertise. Transparency and explainability are critical, and clinicians should always understand why AI reaches a particular decision before acting on it. Finally, regular training and human-in-the-loop (HITL) processes must be put in place to maintain accountability, strengthen trust, and keep patient care at the center of every decision.

Bring AI into your healthcare system with Jotform

If you’re ready to deploy AI in your healthcare organization, Jotform AI Agents offer a simple way to get started right now.

Our Healthcare & Wellness AI Agents make it easy to bring the power of artificial intelligence into your operations with AI-powered forms. These forms remove administrative burden from healthcare providers, helping with everything from patient intake and appointment scheduling to hospice care coordination, medical history collection, and even mental health assessment.

Powered by NLP, Jotform AI Agents turn your traditional information-gathering workflow into a dynamic, conversational experience, guiding patients through step-by-step processes to answer common questions and securely collect information. The AI Agents integrate with your existing systems to sync patient information into your records for easy accessibility. This gives you a more efficient workflow, makes patient interactions more engaging, and allows your clinical staff to focus on delivering more personalized care rather than handling administrative work.

Our no-code AI Agents are easy to set up. You can build them from scratch, start with a form, clone yourself, or use one of our ready-to-go templates designed for various healthcare needs. Jotform gives you lots of customization and training options — from practice policies to patient education resources and common procedures — and you can train your AI Agents to enhance their knowledge base and response accuracy.

With Jotform AI Agents on your team, you can offer your patients a more convenient way to communicate their needs while improving your process with efficient data collection.

The future of AI in healthcare

AI’s role in healthcare may still be in its early stages, but there’s no doubt it’s  on an upward trajectory toward wider adoption and impact. According to Fortune Business Insights, the global AI in healthcare market size is projected to grow from $39.25 billion in 2025 to $504.17 billion by 2032. One of the biggest frontiers driving this growth is predictive medicine, which will help clinicians identify health risks long before a patient’s symptoms appear. By analyzing genetic information and lifestyle data, AI systems promise to predict who’s more likely to develop chronic conditions, empowering doctors to intervene early.

AI-powered wearable devices are also exploding in popularity. As smartwatches, biosensors, and fitness trackers become more advanced, AI will turn the constant flow of biometric data into meaningful insights that can alert users and physicians in real time when early signs of illness or irregularities emerge. Additionally, AI-driven patient care has the potential to expand access to quality care in underserved regions. For instance, AI-powered telemedicine tools and virtual assistants can help fill gaps where doctors are scarce, bringing expert-level guidance to low-resource environments.

On a global scale, AI could also play a central role in epidemic tracking and disease management. ML models and NLP tools can analyze public health data, news reports, and social media activity in real time to help predict outbreaks and make future pandemics far more manageable.

Deliver more efficient care with AI

If you want to keep pace with AI in healthcare, a tool like Jotform AI Agents gives you a simple, scalable way in. Start by designing intelligent data-collection workflows and training the agents on your data, then deploy conversational interfaces that engage patients and streamline staff processes. Get started today or get in touch for more details.