AI in Healthcare: What It Means for Patients and Providers

“AI in healthcare can reduce inefficiencies in revenue cycles, leading to immediate gains.”

Healthcare systems worldwide continue to face challenges posed by rising demand, staff shortages, and funding constraints. To solve these challenges, advanced technologies like AI in healthcare may provide innovative and cost-effective solutions.

AI is revolutionizing how we diagnose, treat, and handle administrative tasks in healthcare. There is a lot AI can do for the healthcare industry, whether it’s personalizing treatment recommendations, predicting outcomes, or discovering new drugs. 

According to KPMG, 53% of executives believe healthcare is leading the adoption of AI. The use of artificial intelligence in healthcare can literally improve patients’ lives by improving diagnostics and treatment and assisting them in making informed medical decisions easily. The global healthcare market is expected to grow to $31.02 billion by 2025 from $2.4 billion in 2019.

Let’s explore AI’s potential in healthcare and everything you need to know.

What is AI in Healthcare?

The use of machine learning (ML) algorithms and other cognitive technologies in healthcare is referred to as AI in healthcare. In simple terms, artificial intelligence is the ability of computers to mimic human cognition and to learn, think, make decisions, and take action.

In healthcare, artificial intelligence refers to the use of machines to analyze and predict outcomes based on medical data. The use of patient data and other information can provide doctors and other healthcare professionals with a more accurate diagnosis and treatment recommendation.

A few examples of AI in healthcare include enhancing diagnosis, disease detection, health risk assessment, drug development, administration, and communication, as well as speeding up and improving these processes.

Why AI in healthcare?

AI in healthcare is so important because it can completely change the way patients are handled and how operations are conducted. By examining extensive data sets at exceptionally high speeds, AI enhances diagnostic precision to allow earlier detection of diseases than ever before.

Additionally, AI has improved therapeutic outcomes by making it possible for treatment plans to be tailored to individual patient information gathered from sources like genetic makeup or historical medical records, among others.

In addition, AI makes human resources available for direct patient care by simplifying administrative tasks as well as clinical workflow. This kind of automation not only boosts efficiency but also ensures real-time data and predictive analytics that influence medical decision-making.

Moreover, telemedicine systems and remote monitoring tools supported by AI enhance access to medical services, especially for populations that may be underserved or those living in remote areas. Therefore, AI serves as a significant catalyst for changing how healthcare is delivered around the world, leading to more efficient care and personalized medicine.

Besides enabling telemedicine and supporting remote monitoring solutions specifically beneficial to underprivileged communities and rural areas, AI also enhances health service accessibility. In the end, however, when implemented in the healthcare industry, artificial intelligence will move towards an effective healthcare delivery system with affordable personalized care, thus promising better health outcomes globally, both at individual and population levels.

How is AI being used in healthcare?

Applications of AI in healthcare are varied and widespread, ranging from medical imaging to virtual assistants and chatbots. The following are some use cases of AI in healthcare:

1. Use of AI in Patient Treatment

• AI-powered chatbots for mental health treatment

Healthcare organizations are using AI-powered chatbots and other tools to diagnose and treat mental health conditions like depression and anxiety. AI-powered chatbots and other tools are of great help in the healthcare sector. 

• Surgery assisted by AI (Robotic Surgery)

Surgeons are increasingly using artificial intelligence to perform complex procedures more accurately and precisely. Consequently, hospital stays can be shortened, recovery times can be sped up, and patient outcomes can be greatly improved.

• Treating patients with personalized medicine

With AI algorithms, doctors can analyze large amounts of patient data and make more personalized treatment decisions. As a result, healthcare costs can be reduced, and outcomes can be improved.

• Easy and faster drug development with the help of AI

Drug development is being accelerated through the use of artificial intelligence. Previously difficult-to-treat diseases are now able to be treated easily with the latest and most effective drug development. 

• Monitoring patients remotely 

After the pandemic, the trend of remotely monitoring patients has increased. The use of artificial intelligence reduces hospital readmissions and improves patient outcomes by monitoring patients remotely with the help of AI. 

2. Use of AI in Healthcare Administration

•  To reduce cost 

As healthcare costs are rising, it has become a major concern for both patients and healthcare providers. The use of artificial intelligence can improve efficiency, reduce waste, and identify opportunities for cost reduction. A few examples of how AI can be used are for optimizing staffing levels, reducing supply chain costs, and improving patient flow.

• Appointment scheduling

A scheduling system powered by artificial intelligence can reduce wait times and optimize patient appointments. It is possible to create an optimal schedule using artificial intelligence by taking into account factors such as wait times, patient preferences, and provider availability.

• Medical billing and coding

Billing and coding medical services can be time-consuming and error-prone. Automating the process can reduce errors and improve accuracy. Using artificial intelligence, healthcare providers can identify billing errors and fraud, reducing costs and protecting patients. 

• AI to detect fraud

Using AI algorithms, healthcare providers can detect fraudulent billing and claims, reducing healthcare costs and protecting patients. Analyzing large amounts of data with artificial intelligence can lead to the identification of patterns and anomalies that may indicate fraud.

• AI in medical research and data analysis

AI can assist in analyzing large amounts of medical data, such as patient records, clinical trial results, and medical imaging data. Ultimately, this can improve patient outcomes by providing new insights and discoveries. AI can also help identify patients eligible for clinical trials, which can result in improved research processes.

3. Use of AI in Diagnosis

• AI applications for imaging diagnosis

Diagnoses are increasingly made faster and more accurate by using artificial intelligence in diagnostic imaging. Medical images such as X-rays, CT scans, and MRIs can be analyzed using AI technology. As a result, radiologists can make more accurate diagnoses, detect conditions earlier, and reduce the need for unnecessary tests and procedures.

• AI for testing and analysis in laboratories

Using artificial intelligence, laboratories can analyze laboratory data, such as blood test results, to identify patterns and anomalies that could indicate certain conditions. As a result, doctors will be able to make better decisions about their treatment and monitor how well interventions are working over time.

• AI and telemedicine 

Telemedicine refers to the use of technology to provide healthcare services remotely. As part of telemedicine, AI can provide remote consultations with healthcare professionals, personalized medical advice, and assist in triaging patients. As a result, healthcare can be made more accessible in areas with limited resources or where it may be difficult to meet in person.

• AI-based analysis of medical records 

Using AI algorithms, for example, one can identify patients with a high risk of developing certain diseases based on their medical history. AI can help doctors make more informed decisions about diagnosis and treatment by analyzing electronic medical records (EMRs) to identify patterns and trends.

• AI on diagnosis accuracy

According to a study published in the Lancet Digital Health, an AI algorithm can diagnose certain eye diseases as accurately as experienced ophthalmologists. A reliance on AI, however, may also lead to overdiagnosis or misdiagnosis if the technology is not used appropriately or algorithms are not validated. Therefore, it is important to continue monitoring the impact of AI on diagnosis accuracy and make sure that it is used responsibly.

Types of AI in healthcare

An artificial intelligence system is more of a collection of technologies than a single one. Many of these technologies are relevant to the healthcare industry, but their specific roles and functions vary. The following are some specific AI technologies that are highly relevant to health care.

Machine Learning

One of the types of AI, Machine Learning, has fundamentally reshaped healthcare by enhancing medical diagnosis and treatment. The algorithms in this AI can identify patterns in vast amounts of clinical data and predict medical outcomes with unprecedented accuracy.

Healthcare professionals can use this technology to analyze patient records & medical imaging, and predicting effective treatment procedures through supervised learning is one of the most common applications of precision medicine.

A subset of AI, deep learning, is also used in healthcare for speech recognition. As deep learning advances and becomes more prevalent, healthcare professionals will become increasingly dependent on it.

Natural Language Processing

Natural language processing (NLP) is another type of AI in healthcare that interprets and uses human language. This form of AI is reshaping the healthcare industry. The use of NLP in healthcare has improved diagnosis accuracy, streamlined clinical processes, and enhanced patient care.

The use of natural language processing in the context of medical records may help to diagnose illnesses accurately by extracting useful information from health records. Also, based on past health data it can be used to identify treatments and medications appropriate for each patient. A powerful feature of NLP is also its ability to provide clinicians with powerful tools for managing large amounts of complex data – a process that normally takes a lot of time.

Healthcare AI is rapidly becoming a must-have in the modern healthcare industry, with many more applications and sophistication expected in the future. 

Rule-based Expert Systems

Today, rule-based expert systems are widely used in healthcare to provide clinical decision support. In the 1980s and 1990s, expert systems based on ‘if-then’ rules dominated AI and were widely used commercially. An expert system requires human experts to construct a series of rules in a particular area of knowledge. To a certain extent, they work well and are easy to understand. However, when there are a lot of rules (usually several thousand), and the rules conflict with each other, they tend to break down.

Changing the rules can also be time-consuming and difficult if the knowledge domain changes. Increasingly, data-driven and machine learning-based approaches are replacing them in healthcare.

What are the benefits of AI in healthcare?

In the past few decades, medical innovation has completely transformed the way we provide medical care. New diagnostic procedures allow healthcare professionals to diagnose a wide variety of ailments more precisely and timely, allowing them to treat patients appropriately. Here are some of the benefits of AI in healthcare software development. 

Better Diagnosis

Moreover, artificial intelligence can capture a huge hoard of medical data, which includes images, laboratory findings, and past history, thereby closing all the gaps that humans could have missed. For instance, it enables early detection of diseases such as cancers and heart diseases, leading to better patient outcomes.

Personalized Treatment

Furthermore, AI facilitates individualized treatment plans based on each patient’s unique needs. By using genetics, lifestyle information, and response to treatment, AI can identify the most favorable treatments for each individual, thus enhancing medical intervention.

Remote Healthcare Facilities 

In addition to offering online medical services, AI can also offer telemedicine services, remote monitoring facilities, etc. This is how AI can prevent healthcare gaps in rural areas and unserved populations. This computer system is crucial because even if one lives far from a well-trained doctor, there’s still a need to get medical care of any kind.

Automate Administrative Processes 

Artificial intelligence can aid in the automation and optimization of numerous administrative and clinical operations, such as patient scheduling, billing, and claims processing. With this method, health workers are able to provide more services for the patients hence improving operational efficiency as well as reducing administrative load.

Assists in Faster Drug Development

Key among them is that artificial intelligence (AI) speeds up and improves drug discovery by analyzing biological data, predicting molecular interactions, and identifying potential drug candidates. For these reasons, AI assists in accelerating research, lowering costs, and introducing new remedies quickly.

Saves Time, Cost & Resources

The use of artificial intelligence (AI) for automating routine tasks in healthcare delivery has been shown to help healthcare providers save time, reduce expenditures, and optimize resource utilization by increasing diagnostic accuracy rates while optimizing treatment plans accordingly. It promotes better resource management practices that lead to sustainable healthcare.

AI in healthcare examples

We have compiled some examples and use cases of AI in healthcare to help you better understand this rapidly evolving field.

1. Google Health’s AI for Diabetic Retinopathy

Google Health has created an artificial intelligence machine that diagnoses diabetic retinopathy, a complication of diabetes that can lead to loss of sight. This AI interprets retinal images and gives diagnostic results that are as accurate as ophthalmologists. Google’s AI system helps prevent millions of people from losing sight by enabling early detection and treatment, illustrating how AI could revolutionize medical imaging and diagnostics.

2. Viz.ai in Stroke Management

Viz.ai employs artificial intelligence to provide real-time analysis of medical imaging for efficient stroke care coordination. Their main product, Viz LVO, identifies stroke indicators from image data sources, prioritizes cases, and alerts experts to intervene in time. It has significantly cut down the door-in-to-treatment time, enhancing patients’ outcomes by speeding up critical clinical decisions with accuracy.

3. DeepScribe Clinical Documentation

DeepScribe offers an artificial intelligence-powered platform for real-time transcription of patient-physician conversations during the clinical documentation process. Its electronic health records (EHR) integration allows doctors to pay more attention to patients instead of doing paperwork. The streamlining of the administration process by DeepScribe reduces administrative work, improves accuracy, and enhances overall healthcare efficiency.

Success stories of AI implementations in healthcare

The healthcare industry has long struggled with complex challenges: accurate diagnosis, limited resources, and the ever-present pressure to improve patient outcomes. Tech giants like IBM, Microsoft, and others saw an opportunity to revolutionize this landscape with a powerful tool – Artificial Intelligence.

IBM

IBM envisioned an AI future where doctors wouldn’t face diagnostic puzzles alone. IBM introduced Watson, a super-intelligent AI trained on a staggering amount of medical data. Watson would not replace doctors but rather become their trusted companion, analyzing patient information and providing diagnoses and treatment options based on relevant research.

This wasn’t the only front on which IBM planned to deploy AI. They also aimed to bring intelligent assistance into the operating room by developing AI surgical assistants. These robotic marvels would analyze real-time data during surgery, offering surgeons a virtual “extra set of eyes” to navigate complex procedures with unmatched precision.

The vision: They wanted AI to assist doctors in diagnosis and surgery.

A significant outcome: IBM created Watson, an AI that analyzes vast amounts of medical data to suggest diagnoses and treatment options. They also developed AI surgical assistants that guide surgeons during operations.

Microsoft

Microsoft, on the other hand, focused on making AI accessible to everyone, especially in underserved areas. Their vision was to utilize AI for early disease detection. Project InnerEye, their brainchild, aimed to be the hero in this story. This AI system would analyze medical images, like mammograms and retinal scans, to identify signs of diseases like cancer at an earlier stage, potentially saving countless lives.

But early detection is only half the battle. Microsoft also understood the importance of data management. Azure, Microsoft’s secure cloud platform, allows healthcare institutions to store and analyze huge amounts of patient data securely. This data could then be used for research, personalized medicine, and population health management, ultimately leading to better preventative care strategies.

The vision: They wanted AI to help detect diseases earlier, especially in underserved areas.

A significant outcome: Microsoft built Project InnerEye, an AI that analyzes medical images to detect signs of disease. They also offer a secure cloud platform for storing and analyzing vast amounts of patient data.

OpenXcell 

Cardixa, a healthcare organization dedicated to improving patient experiences, pinpointed a critical hurdle in their patient journey: the traditional pen-and-paper medical forms. These seemingly mundane documents posed a significant obstacle for senior citizens and Parkinson’s patients, leading to frustration and potentially inaccurate information. OpenXcell knew they had to create an AI solution that wasn’t just efficient but also empathetic.

OpenXcell’s ingenious response was a user-friendly, AI-powered voice assistant. This innovative system transformed the form-filling process from a chore into a conversation. Patients could simply speak their answers, and the voice assistant would seamlessly record and enter the details into the form. Gone were the days of struggling with handwriting or fumbling for the right words. The voice assistant acted as a patient advocate, ensuring accuracy and reducing the stress associated with paperwork.

The vision: OpenXcell wanted senior citizens and Parkinson’s patients to fill out medical forms with Voice Assistant AI Solution as they speak.
A significant outcome: Elderly patients and those battling Parkinson’s disease found a convenient and empowering way to manage their medical information. The voice assistant not only streamlined the form-filling process but also fostered a sense of independence and control over their healthcare journey.

Top 5 emerging trends in AI in healthcare to look for in 2025

1. Telehealth

AI technology is being used in telemedicine to enhance provider efficiency, maintain patient connectivity with wearables and other tools for remote monitoring, and even bring specialty care to previously unreachable remote areas. Disease diagnosis, health monitoring, and triaging are automated using chatbots and virtual health assistants powered by NLP.

2. Medical robots

Startups are building medical robots to deliver care in rural locations or carry out intricate procedures with more accuracy. Robotic surgery improves success rates and decreases problems, while telepresence robots replicate physical presence, and medical transport robots and drones with AI routing may deliver medical supplies and equipment autonomously.

3. Clinic trials

AI improves clinical trial efficiency by analyzing medical data and forecasting results. Additionally, it helps researchers find viable therapies and medications and optimize trial design. Startups are employing natural language processing (NLP) to create a holistic picture of patient health, allowing researchers to spot trends and negative events.

4. Personalized healthcare

Healthcare organizations are creating ML and deep learning models to obtain insights about variations in a person’s genetic makeup, way of life, and medical background. This makes it possible for medical facilities to provide specialized and personalized care. Additionally, startups offer platforms for testing and diagnostics that can identify diseases early, predict their risk, and evaluate the effectiveness of treatment. 

5. Hospital management 

In hospitals, AI automates administrative duties, addresses staffing issues like staffing shortages and staffing allocation, and improves operations and resource use. To maximize resource use and hospital operations, startups use patient flow management and AI-based predictive modeling. Software for supply chain optimization and hospital management powered by AI offers targeted interventions and policies to raise the standard of care.

Challenges and limitations of AI in healthcare 

Despite the significant potential, there are limited AI-enabled solution adoptions in medical practice. In addition to privacy issues, AI technology also has methodological and technical flaws. The following are the primary challenges affecting AI in healthcare: 

1. Lack of quality data 

Clinicians require high-quality datasets to validate AI models on a clinical and technical level. However, collecting patient information and images to test AI algorithms is challenging due to the fragmentation of medical data across several EHRs and software platforms.

2. Might create social biases 

AI algorithms can be biased if they are trained on unrepresentative data or when they contain biases by design. Unfair treatment of certain patient groups can compromise patient safety.

3. Possible security risk 

The primary and direct drawback of AI in healthcare is the potential for data privacy security breaches. Since it develops and expands based on information obtained, it is vulnerable to misuse and abuse. Hospitals that invest in AI will simply need to spend more for data protection, no matter how initially cost-effective the investment was.  

4. Cost can be a barrier

The cost of implementing AI technology may require healthcare organizations to invest in new infrastructure, software, and experts. Small organizations with limited resources may find it difficult to adopt the technology, which will create a barrier for them.

5. Technical difficulty

It takes a high level of technical expertise to develop and implement AI technology. Implementing and maintaining AI systems can be a challenge for healthcare organizations due to a lack of an expert AI team. You can opt for the best AI software development services to easily implement AI technology in your organization. 

6. Ethical issues

The use of AI in healthcare raises ethical concerns, including the possibility that AI systems can make decisions without human guidance. To ensure ethical and patient-centered AI use in healthcare, healthcare professionals and organizations must adhere to ethical guidelines.

Hence, implementing AI in healthcare is challenging and limited by a number of issues. Health professionals, AI experts, and policymakers must collaborate to make AI development and implementation effective and responsible.

How to implement AI in healthcare? 

Implementing AI in healthcare involves a structured approach to ensure effective integration and beneficial outcomes. Here’s a streamlined four-step process:

 1. Automate business processes

Process automation has transformed industries like manufacturing and finance, and healthcare is poised to reap similar benefits. AI in healthcare can analyze population data to uncover trends and inform policy decisions. However, its potential extends to clinical decision support. 

For AI to aid physicians, it must understand clinical processes. For example, a pregnant patient follows a 41-week care plan known to specialists, but AI must be trained on this plan to be effective. Healthcare systems are digitizing clinical guidelines into visual process models that AI can use to support step-by-step care for conditions like heart disease or diabetes. This automation is particularly valuable for clinicians in training or those in remote areas, enhancing decision-making speed and quality while the clinician remains the final decision-maker.

 2. Choose the right IT partner 

AI solutions often start with health informaticists, who are experts in applying digital tools to improve care. However, they aren’t the ones who will implement, use, or govern AI systems. 

Collaboration with an AI development company, clinicians, and compliance officers is crucial. IT professionals ensure the solution is scalable, cost-efficient, and secure. Clinicians verify that the AI system supports their workflow and improves patient outcomes. Compliance officers ensure the system meets safety and regulatory standards. Their input is essential throughout the design and implementation process.

 3. Invest in the right technologies

AI projects should not be treated as one-offs. For scalability, AI tools must integrate with existing IT infrastructure and comply with data privacy and security regulations like HIPAA. 

Open-source solutions are becoming popular due to their cost-effectiveness, flexibility, and strong security posture from diverse inputs. Many machine learning models and large language models like GPT-3 and GPT-4 use open-source approaches. Additionally, cloud-agnostic platforms enable AI models to be deployed where needed, such as in ambulances or bedside, without vendor lock-in or network dependency.

 4. Implement the AI solution

To ensure AI projects are successful, healthcare organizations must begin with the end goal in mind. Understanding clinical workflows is essential for smooth integration. AI models need to be trained and validated with robust, high-quality datasets.

The AI solution must integrate with the existing technology stack and operate at scale using current IT resources unless additional computing power is budgeted. It should also connect with sensitive data across relevant departments.

By following these steps, healthcare organizations can leverage AI to improve operations and patient outcomes. The rapid advancement of AI technology presents exciting opportunities for the healthcare sector to enhance efficiency and care quality. 

Future of AI in healthcare

There is still more work to be done in AI, but the future of AI in the healthcare sector appears bright, achievable, and exciting. In the future, AI-powered solutions will play a crucial role in how healthcare systems operate, enhance clinical resources, and ensure patient well-being.

AI-based treatments are more expensive, but once they are ready, they will provide a sophisticated, rapid, and effective diagnosis. Moreover, this will make training medical students easier and more interesting with realistic simulations that simulate real-time situations, which even computer-driven algorithms can’t provide.

Human clinicians will not be replaced by AI systems on a large scale; rather, their efforts will be augmented. Human clinicians may eventually switch to tasks and job designs that require empathy, persuasion, and the ability to integrate big-picture thinking. Artificial intelligence is likely to replace only those healthcare providers who refuse to implement the latest trends in artificial intelligence.

Custom AI solutions for healthcare providers 

Although AI in healthcare is still in its early stages, more and more providers are beginning to realize how crucial this technology will soon be. To achieve a competitive edge, we advise healthcare providers to incorporate AI as soon as possible. 

OpenXcell has helped many healthcare companies create AI solutions that meet their unique needs. As an AI-driven company, we develop tools that enhance patient care, streamline operations, and improve clinical outcomes by combining advanced technology and industry expertise. 

Our bespoke AI solutions help healthcare providers improve diagnostic accuracy, optimize treatment plans, or automate administrative tasks. Trust us to be your partner in driving innovation and transforming healthcare through the power of AI.