Do you have difficulty balancing improved patient outcomes with operational efficiency in healthcare? Whether you’re a doctor or a healthcare administrator, artificial intelligence in healthcare can help you strike the right balance. 

It is a powerful tool that can improve the organization’s production, effectiveness, and efficiency. By evaluating huge amounts of data in real-time, AI can help to improve clinical and non-clinical decision-making, reduce medical unpredictability, and optimize staffing. Similarly, AI completes jobs that humans would normally execute in less time and at a lower cost.

In 2024, the AI market in the healthcare industry was estimated to be worth USD 15.4 billion. From 2023 to 2030, it is predicted to increase at a CAGR of 37.5%, totaling $188 billion. 

We produced this detailed guide to help you understand how AI is being implemented in healthcare. Let’s define AI in healthcare before we continue.

What is AI (artificial intelligence)?

AI technology in healthcare uses machines to analyze and act on medical data, typically to forecast a specific outcome. Using patient data and other information, AI can assist doctors and medical professionals in providing more accurate diagnoses and treatment recommendations. 

Different AI implementation options in healthcare come together to alter the industry. Let’s examine them down below.

Healthcare Solutions for Artificial Intelligence (AI) 

AI in healthcare comprises a variety of technologies, each with a distinct function. While diverse AI technologies have significant benefits for enterprises, the jobs and processes they support can differ. Let’s look at the many AI options in healthcare. Precision Medicine: Redefining Treatment Strategies.

Understanding The Concept

AI applications in healthcare have a big impact on genomes and genetics. The approach allows for the identification of patterns in massive data sets combining medical records and genetic information, aiding in the discovery of disease-mutation connections. AI can tell clinicians what happens in the cell when DNA is tweaked, either therapeutically or naturally. For example, they may discover previously unknown correlations between DNA defects and other disorders.

Benefits of Precision Medicine for Healthcare

• Customized Care: Precision medicine allows medical professionals to create treatment programs that are tailored to each patient’s unique genetic and biological characteristics.
• Early illness Detection: Genetic analysis and predictive modeling enable the early detection of risks, intervention, and illness prevention.
• Optimized Drug Selection: Genetic profiling can predict how patients will react to pharmaceuticals, resulting in better therapy selection and dosing.
• Reduced Healthcare Cost: Precision medicine lowers trial-and-error procedures, resulting in more effective resource use and lower healthcare costs.
• Treating uncommon Diseases: AI technology in healthcare aids in the early and precise detection of uncommon diseases by discovering links between genetic anomalies, patient symptoms, and disease processes.

AI technologies used in precision medicine

Machine Learning (ML): ML algorithms examine large datasets to uncover trends, connections, and predictors.

Deep Learning: Deep Learning algorithms examine complicated biological data, such as genetic sequences and protein structures, and help us comprehend disease causes.

Deep Learning: AI creates predictive models that forecast illness risks, development, and treatment outcomes based on an individual’s genetic profile and other pertinent information.

Practical Application: Precision Medicine Changing Lives

Prinses Máxima Centre for Paediatric Oncology in Utrecht, Netherlands.

It provides specialized care and does cancer research on youngsters. It uses cutting-edge genomic tools to detect genetic abnormalities in pediatric cancer patients. By personalizing treatment selections to each patient’s tumor’s unique genetic traits, the clinic intends to improve treatment efficacy, reduce side effects, and promote pediatric oncology research.

AI-enabled EHRs

Understanding the notion

It is no secret that working in the healthcare profession can be stressful, and many departments are understaffed. Electronic health records (EHRs) are the digital version of a patient’s whole medical record, including previous diagnoses, current treatments, ongoing prescriptions, test results, and so on. Its goal is to develop an encrypted and consistent platform for healthcare providers to store, retrieve, and exchange patient data.

Medical practitioners can focus more on patient care and other important parts of their employment if they spend less time on administrative tasks. AI can aid with duties such as record keeping, scan analysis, and data entry.

Benefits of Artificial Intelligence in EHR Systems

• Enhances Accessibility: EHR systems promote care coordination and continuity by allowing authorized healthcare staff to view patient information remotely.
• Improves Patient Care: Quick access to complete patient records leads to better clinical decision-making, which benefits patients by providing more targeted and specific care.
• Saves time: EHRs improve efficiency and productivity by removing or greatly lowering the need for paper records and manual data entry, giving healthcare practitioners more time to focus on direct patient care.
• Reduces Manual Errors: Using easily understandable and uniform digital data reduces the likelihood of writing typos, superfluous testing, and prescription errors.
• Easy Data Exchange: Electronic health records (EHRs) enable interoperability, or the exchange of data between healthcare organizations, which improves patient communication and treatment.

AI Technologies Applied in EHRs

• Natural Language Processing (NLP): Natural Language Processing (NLP) extracts useful information from unstructured clinical material, such as patient reports. NLP enables automated data entry, coding, and insight extraction from clinical narratives.
• Predictive Analytics: AI-powered predictive algorithms examine historical patient data to detect trends, risk factors, and potential health consequences. This allows for early illness identification, identifies high-risk patients, and influences treatment approaches.
• Machine Learning (ML): Machine Learning (ML) algorithms examine EHR data to identify patterns, connections, and abnormalities. They can use past data to anticipate disease development, patient outcomes, and therapy responses.

Practical Application: EHRs with AI Advantage (Digital Transformation)

National Health Service (UK)

The National Health Service in the United Kingdom adopted EHR technologies to modernize healthcare delivery and improve patient care. It facilitated efficient communication and collaboration among various healthcare departments. Furthermore, with less paperwork and manual data entry, healthcare workers will have more time to focus on patient care.

Remote Patient Monitoring

Understanding The Concept

Remote patient monitoring allows the patient to receive care from home. AI implementation in healthcare enables remote patient data transmission. In many isolated places where healthcare services are far and difficult to reach, remote patient monitoring with AI comes to the rescue. 

Advantages of Remote Patient Monitoring in Healthcare

• Reducing costs: Remote patient monitoring can result in fewer hospital admissions and unnecessary visits, lowering healthcare expenses.
• Personalized Care: AI-driven RPM optimizes treatment methods by enabling customized interventions based on patient-specific data. 
• Chronic Disease Management: RPM helps control chronic illnesses by encouraging regular patient monitoring and appropriate intervention.
• Universal Access to Healthcare: The hospital can give necessary care with reasonable ease to those who live in remote places and need assistance. 

AI Technologies for Remote Patient Monitoring

Machine Learning (ML): Machine Learning (ML) algorithms look at patient data to identify patterns and anticipate health trends.

Predictive Analytics: Predictive analytics uses previous patient data and real-time inputs to forecast prospective health risks.

Pattern Recognition: Artificial intelligence in healthcare analyzes problematic patterns in patient data and sends notifications to healthcare providers.

Natural Language Processing (NLP): NLP can process and interpret text or speech data supplied by patients, providing information about their symptoms and feelings.

Practical Application: Reimagining Healthcare with Remote Patient Monitoring,

University Hospital, Heidelberg, Germany

The hospital’s stroke section has implemented a telemedicine service that allows for remote monitoring of stroke patients after they depart. Patients are given wearable devices equipped with sensors that continuously capture data about their vital signs, movements, and activities. The collected data is transmitted to the hospital’s stroke unit via a secure digital platform.

AI enables patient diagnosis

Understanding the notion

Artificial intelligence in healthcare improves medical decision-making, management, automation, administration, and processes. It can be used to diagnose cancer, prioritize crucial discoveries in medical imaging, identify acute abnormalities, help radiologists prioritize life-threatening situations, diagnose cardiac arrhythmias, predict stroke outcomes, and aid in chronic disease management.

Benefits of AI-enabled patient diagnosis

Speedier Diagnosis: Diagnostic AI-powered systems can analyze and interpret data quickly, resulting in speedier diagnosis and treatment initiation.

Healthcare Resource Allocation: AI can help prioritize critical cases and streamline the diagnostic process, allowing for more efficient resource allocation in the healthcare industry.

Risk Assessment: Artificial intelligence can compute and evaluate illness risk factors, allowing doctors to provide better patient treatment and follow-up decisions.

Multimodal Analysis: Artificial intelligence may combine data from several sources (clinical, imaging, and genetic) to provide a thorough diagnostic evaluation, allowing for an all-encompassing assessment of the patient’s health.

AI Technologies for Patient Diagnosis

• Machine Learning (ML): Machine Learning (ML) algorithms examine patient data to uncover patterns, correlations, and trends that lead to correct diagnoses.
• Deep Learning: Deep neural networks use complicated data, such as medical imaging, to extract features and detect subtle anomalies.
• Natural Language Processing (NLP): NLP examines clinical notes, patient histories, and other textual data to help diagnose conditions and extract information.

Practical Application: AI-Assisted Coronary Artery Disease Diagnosis.

Heart Flow

Heart Flow’s AI-driven technology analyzes cardiac CT data to create customized 3D models of patients’ coronary arteries. It helps cardiologists diagnose coronary artery disease and determine the most effective therapy options.

AI Chatbots: Your 24/7 Health Assistants.

Understanding The Concept

With AI in healthcare, patients cannot receive individualized therapy recommendations at home. Patients have improved hospital access when essential, and AI chatbots help them even more. If the issues are minor, patients are automatically instructed to take the necessary medication. If a medical visit is required, it is recommended.

Benefits of AI-powered Chatbots in Healthcare

• 24/7 Accessibility: AI chatbots give patients access to healthcare information and support 24 hours a day, seven days a week, allowing them to request assistance at any time.
• Appointment Scheduling: Chatbots can help patients arrange and remember their appointments, enhancing patient engagement and decreasing no-show rates.
• Medication Reminders: Chatbots can remind patients to take their medications and help them manage their treatment plans, hence improving medication adherence and patient outcomes.
• Symptom Evaluation: AI chatbots can help patients assess their symptoms by delivering preliminary assessments and recommendations for the next steps.
• Language Accessibility: Chatbots’ capacity to converse in multiple languages makes them more accessible to non-native speakers and meets the needs of a diverse variety of patient groups.
• Cost Savings: AI chatbots help healthcare organizations and providers save money by automating repetitive activities and questions.
• Patient Engagement: Chatbots can promote healthy habits and patient engagement by connecting with them through interactive chats, quizzes, and surveys.

AI Technologies Used in ChatBots

• Natural Language Processing (NLP): Natural Language Processing (NLP) enables chatbots to understand and respond to user inputs and queries by interpreting and processing human language. NLP allows chatbots to extract meaning, context, and intent from text or voice.
• Machine Learning (ML):  Machine Learning (ML) enables the chatbot to learn from data and improve its response over time. They can identify trends in user behavior and adjust their behavior in response to the information provided.
• Robotic-Process Automation (RPA): RPA can automate administrative activities such as appointment scheduling, data entry, and insurance verification, allowing healthcare personnel to focus on more complex tasks.

Practical Application: Virtual Healthcare with AI-Driven Chatbots.

Thuisarts

Thuisarts is an example of artificial intelligence used in healthcare. In the Netherlands, it is a platform that provides online consultations. Thuisarts offers an AI-powered chatbot solution for virtual consultations that interacts with users to better understand their challenges.

Medical Image Analysis: AI for Enhanced Diagnostics.

Understanding The Concept

Artificial intelligence can now be used in healthcare to detect problems in medical pictures such as CT and radiography imaging. Doctors use image recognition to diagnose malignancies, kidney and liver infections, and enhance cancer prognosis.

Advantages of AI-powered Medical Image Analysis in Healthcare

• Accurate diagnostic: AI-powered image analysis improves disease identification and diagnostic accuracy, lowering human error and improving patient outcomes.
• Early discovery: Recognizing mild abnormalities early allows for illness discovery and action, which may improve the prognosis.
• Makes it Efficient: Using AI in healthcare accelerates picture analysis, allowing for faster diagnosis and shorter patient wait times.
• AI Technologies Used in Medical Image Analysis.
• Deep Learning: CNNs and other deep learning architectures are commonly used to identify patterns in medical pictures. 
• Transfer Learning: It uses pre-trained models from general image datasets to improve the performance of AI models on medical images in the absence of labeled data.
• Natural Language Processing (NLP): Techniques for analyzing textual information associated with medical pictures, such as radiology reports or clinical notes. This can give context when understanding an image.

Practical Application: The Impact of InnerEye Technology

Addenbrooke Hospital, Cambridge.

Microsoft’s InnerEye technology automatically examines patients with prostate cancer at Addenbrooke Hospital in Cambridge. The technique uses a picture to diagnose prostate problems, identify malignancies, and generate reports. This has accelerated the treatment of prostate cancer, and the institution is exploring employing the same method for patients with brain tumors.

Clinical Trial Solutions: Improving Research with AI

Understanding the notion.

Using artificial intelligence in healthcare accelerates certain aspects of the clinical study process. It collects data in real-time and allows researchers to identify patterns and create correlations. This information assists researchers in determining what to conduct and speeds up the investigation.

Advantages of AI-Enabled Clinical Trial Solutions

• Accelerated Recruitment: By reviewing vast datasets, electronic medical records, and genetic data, AI in healthcare aids in identifying qualified patient candidates for clinical trials, speeding up the participant recruitment process.
• Trial Stage Development: Using AI to design trials with optimized processes, sample sizes, and outcomes helps raise trial success rates while lowering costs.
• Data analysis: AI systems search for patterns, trends, and treatment responses in complex trial data, assisting decision-making and revealing valuable information.
• Tailored Treatment: AI enables the identification of patient subgroups that react differently to medicines, allowing for more individualized therapy.
• Drug Discovery: AI-driven simulations and modeling that predict the potential efficacy and safety of novel compounds accelerate drug development.
• Risk Prediction: AI examines potential dangers and poor results in clinical investigations, allowing for proactive mitigation actions.
• AI Technologies Used in Clinical Trial Solutions.
• Machine Learning (ML): To predict patient reactions and enhance trial design, ML systems analyze patient data, trial results, and historical data.
• Natural language processing (NLP): Natural language processing (NLP) retrieves useful information from unstructured text data such as patient records, study procedures, and medical literature.
• Predictive Analytics: AI-powered algorithms for estimating trial results, patient enrollment rates, and potential bottlenecks.
• Image Analysis: AI in healthcare uses biomarkers and medical pictures to aid in patient selection, sickness monitoring, and therapy evaluation.
• Practical Application: An AI-Powered Solution for Zika Virus Infections

Sanofi & The Fraunhofer Institute for Molecular Biology and Applied Ecology

Sanofi and Germany’s Fraunhofer Institute for Molecular Biology and Applied Ecology collaborated to find a solution for Zika-related disorders. Researchers used artificial intelligence in healthcare to search a database of licensed drugs for a solution. The AI study focused on the drug “dyclonine,” which is commonly used as a local anesthetic.

If you’ve decided on the type of AI-enabled healthcare product you want to create, you should read our blog post about why Flutter is the best tech stack for developing medical applications.

What Are the Challenges of Using AI in Healthcare?

While AI healthcare applications have many advantages, they must also overcome a number of challenges to be adopted successfully. The main obstacles are as follows:

Data Security and Privacy:

Healthcare data is sensitive, so protecting patient privacy is critical. To protect patient data, AI 

Implementation in healthcare applications must adhere to stringent security protocols.

Lack of Standardization:

Healthcare data is not currently standardized, making it difficult to integrate AI into multiple systems. Furthermore, the lack of consistency makes comparing data from various sources problematic.

Ethical issues:

When applying AI to healthcare applications, there are ethical concerns to be aware of. For example, there may be difficulties with algorithmic bias or the impact on patient autonomy.

Integrating with existing systems:

Integrating AI into existing healthcare systems may take time. Healthcare providers may need to invest in additional infrastructure and train their employees to use new technology.

These problems underline the importance of rigorous planning when integrating AI into healthcare applications. You must strike a compromise between technical challenges like standardization and system integration and ethical ones like patient data privacy.

These problems show that you should not underestimate the value of Healthcare IT consulting when integrating AI into your healthcare operations. You must strike a compromise between technical challenges like standardization and system integration and ethical ones like patient data privacy.

How can Velan help?

This is only the beginning of a wonderful future. AI in healthcare improves efficiency and patient focus.

Some issues make it difficult to implement AI use cases in healthcare in a beneficial way. Nonetheless, by collaborating with a company like Velan, which has extensive experience developing AI solutions, you may swiftly overcome these challenges and produce more accurate diagnostics, medication discoveries, and treatment plans. 

With a talented team of professionals, we can create new AI-powered solutions that match the needs and overcome the obstacles of the healthcare industry. Working with us allows you to fully leverage AI in healthcare.