Healthcare is evolving rapidly. Healthcare innovation is driven by the need to meet growing consumer expectations of care quality amid global health emergencies, world population aging, the rise of chronic diseases, and workforce shortages. Technologies like artificial intelligence (AI), virtual care, telehealth, wearable health tech, and large language models (LLMs) are transforming patient care and boosting operational efficiency.
For example, AI algorithms enhance medical imaging by helping radiologists detect abnormalities faster and more accurately. AI is also capable of simplifying diagnoses — especially critical for early detection of conditions like cancer. In administrative functions, AI-powered automation optimizes tasks like patient scheduling, billing, and workforce management, which means a reduced manual workload, decreased costs, and boosted efficiency. It doesn’t stop there – AI also improves clinical data management by automating data entry, retrieval, and record-keeping. Together, these technologies lead to cost savings, reduced manual errors, improved efficiency, and enhanced data accuracy.
Akvelon’s extensive work in healthcare technology has granted us deep insights into the opportunities and challenges that come with these technologies. In this article, we’re going to share five key healthcare technology trends for 2025, detailing how these innovations work in practice, their benefits, and the challenges healthcare organizations must navigate for their successful implementation.
Trend 1: AI-Powered Automation in Clinical Data Management
AI-powered automation is transforming clinical data management by reducing the administrative burden on healthcare professionals and enhancing data accuracy. By automating tasks like patient record management, documentation, and data retrieval, healthcare providers can dedicate more time to patient care.
A compelling example of this trend is Mercy Health’s implementation of an AI-driven nursing workforce management system, which automates tasks like shift filling and scheduling. This solution cut the time spent on staffing by 20% and increased fill rates from 83% to 86%. In 2023 alone, Mercy Health reported over $30 million in savings through this system. Beyond operational efficiency, the platform provides data-driven insights that optimize care delivery and manage clinical labor effectively.
By quickly analyzing large volumes of data, AI accelerates automation while introducing advanced capabilities that streamline operations and improve outcomes for both providers and patients.
For instance, our AI-powered tools automate the process of de-identifying protected health information (PHI), allowing healthcare organizations to maintain compliance with data protection regulations while reducing manual workload. Solutions like these enable healthcare professionals to spend less time on repetitive tasks and more on direct patient care.
By addressing challenges like “pajama time” — the after-hours documentation clinicians often complete at home — an AI-driven approach offers a way to improve work-life balance and operational efficiency. This enables healthcare professionals to engage more meaningfully with patients while minimizing the risk of human error in data management. Our experience with real-time speech-to-text software positions us to reduce the time healthcare providers spend on manual data entry, ensuring accurate and timely documentation. In fact, the research from the British Journal of Healthcare Management shows that speech recognition software can be up to 1.7x faster than typing, making it an essential tool for improving workflow efficiency.
Beyond documentation, AI-powered tools contribute to a range of critical areas in healthcare operations. For example, we have conceptualized an LLM-powered medical paperwork search system based on our expertise with similar technologies. This concept strives to allow patients, clinicians, and providers to quickly locate authorized records, improving information retrieval and operational efficiency. Additionally, LLM-powered solutions can be leveraged for creating a document data extraction tool that automates data entry from various formats, aimed at reducing manual errors and streamlining processes.
Overcoming Key Challenges in Healthcare Automation
Healthcare automation offers significant benefits, but it also presents challenges that require thoughtful consideration to ensure effective implementation:
- Potential Integration Difficulties: Integrating new automation tools into existing healthcare systems can be technically challenging. Seamless integration is vital to avoid disruptions in established clinical workflows and ensure that new tools complement, rather than complicate, daily operations.
- Ongoing Maintenance and Support: Automation systems require regular maintenance and updates to stay reliable. Without continuous support, these mission-critical systems may degrade over time, negatively affecting patient care and operational efficiency.
As data management demands grow, AI-powered automation has the potential to play a key role in streamlining operations. However, integrating AI tools into established systems can disrupt workflows if not carefully managed. Ongoing maintenance is also critical to prevent these tools from becoming outdated and unreliable.
Addressing these challenges has been central to Akvelon’s approach to deploying healthcare automation solutions. For more information on how AI is transforming healthcare, visit our Healthcare AI Consulting and Development Services page and explore our innovative solutions.
Trend 2: Virtual Care Solutions
Virtual care solutions have become an essential component of modern healthcare, enabling patients to access medical services remotely and on-demand.
This rapid growth is fueled by advancements in digital health technologies and the rising demand for more convenient healthcare options. Virtual care, from telemedicine to remote patient monitoring, is transforming how healthcare is provided, making it easier for patients to get the care they need quickly, no matter where they are.
Remote patient monitoring leverages wearables and medical devices to track vital signs in real-time, transmitting data to healthcare providers. This makes timely interventions possible, which is especially critical for managing chronic diseases. For instance, our remote health data monitoring and analytics system captures patients' vital signs using medical devices, wearables, and health apps. This data is transmitted in real-time to healthcare providers, supporting timely interventions and improving outcomes, particularly for chronic condition management.
Virtual platforms now also support real-time translation, allowing non-English speakers or patients in remote areas with limited access to doctors to receive equitable care. Akvelon has contributed to solutions that improve inclusivity, such as real-time translation services for virtual care, ensuring that language barriers do not impede access. Additionally, we developed virtual ultrasound diagnostic capabilities, allowing patients to undergo procedures from their homes with real-time data securely transmitted to healthcare providers for accurate analysis.
These examples highlight how virtual care solutions can significantly enhance the quality of healthcare delivery, even in remote or underserved areas.
Overcoming Key Challenges in Virtual Care
While virtual care offers significant advantages, overcoming its challenges requires a thoughtful and strategic approach — one that our extensive experience has shaped.
- Risk of Data Security Breaches: Remote care platforms involve transmitting sensitive health data, increasing the risk of cybersecurity threats. Ensuring that all communication and data transfers are secure is paramount.
- Limited Interoperability with Health Systems: Virtual care tools must integrate seamlessly with existing health systems such as electronic health records (EHRs). Inadequate interoperability can lead to fragmented care and data silos.
Virtual care has the potential to expand in 2025, with a focus on addressing access and security challenges to make healthcare more inclusive and adaptable to diverse patient needs. It’s worth noting that virtual care tools present data security risks due to the transmission of sensitive health information. Additionally, technology disparities may create a digital divide, limiting access for underserved populations
Learn more about our telehealth expertise and the innovative solutions we offer on our Telemedicine Software Development and Engineering Services page.
Trend 3: LLM-Powered Assistance in Healthcare Operations
LLM-powered tools are becoming essential in healthcare, supporting data analysis, decision-making, and workflow optimization. Leveraging large language models (LLMs), a subset of generative AI (GenAI), enables healthcare providers to streamline operations, improve patient outcomes, and facilitate a wide range of activities, from research and patient management to administrative tasks.
AI assistance in diagnostics and medical research is poised to become a valuable tool, supporting healthcare providers with data analysis and insights that will streamline operations and improve outcomes. By analyzing large and complex datasets, AI algorithms can uncover patterns that may go unnoticed by human researchers, offering deeper insights and more efficient data processing.
However, it’s important to recognize that the use of AI in direct diagnostics is still developing. This area presents complex ethical and legal considerations, making it essential for AI to play a supportive, rather than decisive, role in the diagnostic process.
The market growth of generative AI, which includes LLMs and other advanced AI models, demonstrates the increasing importance of these technologies in healthcare. As GenAI continues to evolve, healthcare organizations must navigate the opportunities and challenges it brings to fully leverage its potential.
As healthcare providers embrace AI, the potential to enhance patient outcomes and streamline medical processes becomes clear.
Here are some examples illustrating how LLM-powered solutions address critical challenges in healthcare, from improving data management to supporting clinical decision-making.
As part of our GenAI services, our PTSD Chatbot Accelerator provides real-time support to individuals with post traumatic stress disorder (PTSD) by offering early intervention insights based on patient responses.
Additionally, healthcare professionals may benefit from tools that help streamline research, such as our LLM-powered medical literature tools. These tools assist healthcare professionals in sifting through vast medical databases, extracting key insights, and summarizing findings. While we haven’t yet fully implemented these tools, we discovered that they could significantly reduce the time spent on research, enabling clinicians to focus more on patient care. AI-powered search solutions also facilitate faster retrieval of patient records and medical documents, improving accuracy and efficiency in clinical workflows.
Key Challenges in LLM-Powered Assistance in Healthcare Operations
LLM-powered assistance in healthcare operations brings unprecedented opportunities, but seamless implementation presents its own set of challenges. We’ve found that these are the key barriers that healthcare providers must address to fully realize the potential of these technologies.
- Inconsistent Data Quality: GenAI models rely heavily on high-quality and consistent data to deliver accurate results. Any issues with data quality could lead to incorrect suggestions and pose challenges. In Akvelon, we devote efforts towards accumulating data that mirrors actual-world instances. Our team has vast experience that can shed light on data necessities and assist in formulating top-tier data collections.
- Risk of Biased Outputs: Due to limited context awareness in specific industry domains, GenAI solutions can sometimes produce misleading or biased content. To combat this, we integrate domain-specific databases and use real-time feedback loops to correct biases and improve context awareness.
- Risk of Data Breaches: Safeguarding sensitive healthcare information is crucial when deploying LLM-powered solutions, as any vulnerabilities in data security can result in breaches or non-compliance with regulations. We implement state-of-the-art encryption for all user data, ensuring their safety during storage and transmission. We also conduct regular security audits and compliance checks to ensure adherence to international standards, including HIPAA. Our advanced LLM Security Testing Framework helps healthcare providers maintain a secure environment while deploying these technologies.
While these advancements are transforming the healthcare sector, our GenAI services offer a broader scope of solutions across multiple industries. To discover how Akvelon's GenAI capabilities can help your organization to optimize operations, generate insights, and enhance decision-making across various fields, explore our comprehensive expertise and services in our dedicated GenAI page.
Trend 4: AI-Driven Medical Imaging
AI-driven medical imaging is advancing rapidly, particularly in X-ray fracture analysis. By utilizing computer vision, AI-powered tools can quickly and accurately detect fractures, significantly speeding up diagnosis and improving patient outcomes.
AI models utilizing augmented MRI-scanned images achieved an accuracy of 98.56% in brain tumor classification, as detailed in the study published in Scientific Reports. This indicates a significant enhancement in diagnostic precision and the potential to reduce the time to diagnosis for patients.
This remarkable growth highlights the increasing reliance on AI to deliver precise and efficient diagnostic results.
Despite the benefits of AI-driven medical imaging brings, navigating its challenges requires careful consideration:
- Risk of Inconsistent Data Quality: AI-driven imaging models depend on high-quality images for accurate diagnostics. Inconsistent image quality across different sources can reduce model accuracy. Our approach includes using advanced image augmentation techniques to improve model generalization and accuracy across various image types.
- Challenges in Regulatory Compliance: AI tools in medical imaging must comply with strict regulatory standards for security, data protection, and patient safety to ensure they are safe and accurate. Failure to adhere to these regulations can result in legal risks and compromised patient safety. Akvelon’s deep expertise in regulatory compliance ensures that all AI imaging tools meet legal and ethical standards.
Through addressing these challenges, Akvelon has developed AI solutions that enhance the efficiency and precision of fracture diagnostics. For instance, our team created an AI-powered web service that allows physicians to quickly and efficiently diagnose distal radius fractures using X-ray images. This application, powered by machine learning models, achieves approximately 97% accuracy in identifying fractures and can also provide the precise coordinates of the fracture within the image. Our solution accelerates the diagnostic process, enabling physicians to deliver immediate care to their patients.
We recommend this approach because AI models can analyze X-ray images to detect fractures with high accuracy, reducing diagnosis time and allowing faster patient treatment.
Though the success of AI in medical imaging depends on consistent data quality and regulatory compliance. Any deviations in image quality can compromise model accuracy, and compliance with strict healthcare standards is essential to avoid legal issues. By using large datasets to train AI models, healthcare providers can achieve more consistent results across varied imaging conditions and sources.
Trend 5: AI-Powered Insurance Claim Processing
AI is transforming the insurance claim process by automating labor-intensive steps.
Traditional claim processing is prone to delays and errors due to manual data entry, which often slows payments and increases operational costs. AI-driven tools streamline the claims lifecycle — from submission to approval — reducing human error and speeding up reimbursements for healthcare providers. For example, a Nordic insurance company successfully automated its claims processing, with 70% of claims documents being correctly extracted and interpreted automatically, showcasing the efficiency and accuracy that AI can bring to claims management.
AI tools can flag incomplete claims, auto-populate data fields, and detect suspicious patterns, reducing the need for manual intervention and improving turnaround times.
AI models trained on diverse datasets can detect anomalies in claim submissions, helping healthcare providers and insurers combat fraud while processing legitimate claims efficiently.
Overcoming Key Challenges in AI-Powered Claim Processing
AI-driven claim processing enhances efficiency, yet addressing the related challenges is essential for smooth operations. In our experience, there are several key issues that healthcare organizations need to resolve to maximize the benefits of these technologies, including:
- Risk of Inaccurate Data Entry: Inaccurate or incomplete data can delay claim processing and lead to rejections. Even minor errors during data entry can result in time-consuming reviews and rework, delaying the entire process.
- Fraud Detection Limitations: While AI can detect fraud patterns, there is a risk of false positives or undetected fraudulent claims if the model is not properly trained on diverse datasets. This can lead to unnecessary investigations or overlooked fraudulent activity, both of which slow down the approval process.
- Challenges in Adapting to Multiple Payer Systems: Healthcare providers often work with various insurance payers, each with their own requirements. AI tools must adapt to these differences in order to streamline claims processing effectively.
In response to these challenges, Akvelon leveraged our expertise in AI-driven claim processing to explore potential solutions. We developed approaches for AI-driven fraud detection systems that identify anomalies in claims data, enabling healthcare providers and insurers to detect fraud while expediting legitimate claim approvals. By automating these processes, these tools improve accuracy, reduce manual review time, and ensure compliance with regulatory standards, ultimately allowing faster reimbursements.
AI-powered claims processing accelerates the revenue cycle, provided these tools remain adaptable to payer-specific demands and advances in fraud detection capabilities. Though AI-driven claims processing tools must manage variations in payer requirements across different insurance providers, incomplete or inaccurate data can lead to costly rework. Additionally, effective fraud detection requires continuous training of AI models to identify evolving fraudulent behaviors.
Moving Healthcare Forward
Looking ahead, the future of healthcare will be shaped by continued advancements in AI, personalized medicine, and the integration of AI with wearable devices. Akvelon is already at the forefront of these innovations, driving better patient outcomes and improving operational workflows. This is why we are already a trusted partner for healthcare organizations that are ready to leverage these technologies and stay ahead of the curve.
Successfully implementing AI in healthcare requires a strategic focus on data security, privacy, and regulatory compliance. At Akvelon, we prioritize these elements to ensure our solutions align with HIPAA and other healthcare regulations. Our security framework leverages encryption, risk assessments, and monitoring to protect patient data, enabling healthcare providers to adopt AI safely and responsibly, minimizing risks while maximizing the benefits.