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What is Agentic AI?
Artificial Intelligence has been acting as a game changer in every industry and various advancements in the same have been significantly beneficial for healthcare and its subsets such as the medical technology sector. The need for accuracy and scalability is on the rise due to annual inefficiency losses, and thus agentic frameworks can play a vital role in increasing the same. Agentic AI is an artificially intelligent system that has the capacity to act autonomously in cases where a set of parameters and proper guidance to understand its environment and objectives is provided. When compared to traditional AI models that react to inputs, agentic AI models can interact with the environment, make decisions and perform actions with the ability to adapt through time and enhance their outcomes.
They function in dynamic and uncertain environments of the MedTech solutions field which is characterized by changing variables and limited to the domain of human intelligence. They can be seen operating autonomously in medical diagnostics, medical imaging, robotic surgeries, personalized treatment planning and more. In this context, they analyze data, make informed decisions, execute tasks, learn and adapt with the environment to improve their performance. Their market size is approximately valued at US $7.28 billion as of 2025, and is expected to reach around US $41.32 billion by 2030, surging at a CAGR of 41.48% during this forecast period. Let us delve into the details of these frameworks, related trends, innovations, challenges as well as how healthcare-related issues can be addressed using agentic AI for improved patient care and operational excellence in this blog.
Increasing market size of agentic frameworks during the forecast period 2024 to 2034
Key Agentic AI Frameworks & Models
Given below are a few AI models and frameworks that are used for the development of agentic systems in the MedTech industry –
1. Reinforcement Learning: RL involves training agents to develop sequences of decisions through a reward-based system for actions that lead to desirable outcomes. It is used for developing agents such as agentic RAG or for robotic surgery through precise and effective learning of procedures over time.
2. Transformers: These include bidirectional encoder representations (BERT) and generative pre-trained transformers (GPT) that have revolutionized natural language processing (NLP) in Medtech. They basically allow AI systems to understand, process and generate human-like text for supporting clinical trial data analysis and related decisions, generate patient communications and health reminders, automate medical transcriptions, manage medical records, discharge instructions, devise personalized treatment plans etc.
3. Deep Learning: These include techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) etc. for medical imaging and diagnostics. CNNs can efficiently detect tumors in X-rays, CT scans, MRIs for neurodegenerative diseases or AI breast cancer detection etc., while RNNs can handle sequential data via agentic RAG for monitoring vital signs and predicting patient health deterioration and disease progression.
4. Federated Learning: This is a distributed machine learning framework in which AI models are trained across multiple decentralized devices as well as data sources, without the need for sharing sensitive patient data. This allows compliance with the Health Insurance Portability and Accountability Act (HIPAA).
Applications of Agentic AI in MedTech
These systems have their foundations based on large language models (LLMs), large multimodal models (LMMs) etc. that power ChatGPT and other solutions. These enable AI to decode as well as generate human-like text for enabling interaction between patients, healthcare professionals and artificially intelligent systems. Furthermore, multimodal systems facilitate text, audio, video and images for detailed understanding of healthcare scenarios. Let us now explore the various transformative applications of agentic systems in the Medtech industry in particular-
Accurate Diagnosis
AI-based agentic systems can increase accuracy while reducing processing time of medical diagnosis. These systems analyze huge medical datasets and images. medical history, interactions, electronic health records research papers for suggesting informed diagnostics quickly to domain experts, leading to patient satisfaction and improved treatment adherence.
Personalized Treatment
Treatment plans specific to individual patients can be tailored as per their genetic makeup, lifestyle choices and patient histories. As compared to traditional treatments, these plans are more effective as they calculate treatment efficacy, dosages, side effects, patient compliance and other factors on a prior basis. Agentic RAG (retrieval-augmented generation) is a commonly known question-answering system that employs these systems and LLMs to handle complex medical questions.
Streamlined Administration
These systems assist in enhancing the efficiency of administrative processes and reducing overhead by automating mundane tasks, managing schedules of healthcare professionals and surgeries, managing patient records, optimizing resource allocation round-the-clock, processing insurance claims etc. This is beneficial for reducing operational expenditure and aligning opportunities in the long run, which is a crucial aspect for all financial stakeholders.
Predictive Analytics
Agentic AI frameworks can predict health trends and potential healthcare-related issues before any major symptoms or causalities may occur. They do so by analyzing patient health and demographics to identify individual patients posing health risk. Furthermore, they recommend preventive interventions proactively to improve their impending health conditions and outcomes at lower costs. They can also be adapted for predictive maintenance of surgical, diagnostic and therapeutic equipment and electronics to minimize overall downtime and repairing costs.
Sample workflow of Federated Learning – training model for Agentic RAG systems
Drug Discovery
As we are aware, drug discovery is a time-consuming and expensive process and can take years for market release. Agentic AI frameworks can accelerate the drug discovery and development process by predicting interactions between drugs and the patient’s body as per their history, analyzing molecular structure, simulating trials over visualization software or embryo grading platform etc. This helps in faster breakthroughs, reducing time to market, drug release, efficacy analysis and associated costs, especially during pandemics. It also directly supports the differential applications offered by pharmaceutical companies, drug or medical device contract manufacturing, and research institutions.
Improved Accessibility
These systems improve access to Medtech innovations and healthcare services in remote areas by facilitating online patient monitoring, consultations, telemedicine and medicinal deliveries. This allows healthcare providers to improve their global outreach, leading to a sustainable and equitable healthcare system for all patients.
Others
Agentic AI frameworks also enable risk analysis and detect financial fraud with respect to insurance claims through real-time monitoring and adaptive algorithms. They increase sales conversion through tailored treatment plans. Also, they optimize the hospital supply chain through demand forecasting and medical inventory management system, leading to smarter decision-making, reduced overall costs and better delivered services.
Considerations associated with Agentic AI
Apart from the immense number of benefits of integrating these systems in the medical technology industry, healthcare businesses need to consider a few challenges associated with them as well –
1. Data Privacy: Data security and privacy is a major concern when it comes to sensitive healthcare data. Essential cybersecurity techniques, federated learning, and compliance need to be placed to mitigate these hurdles.
2. Ethics & Regulations: Regulation of these agentic RAG systems may vary as per region. Due to their complex nature, governments and regulatory bodies need to ensure that they pose no threat to patients, are safe, effective and functionally ethical. This is done on a continuous basis through approvals and checks with respect to regulations for medical devices, as and when applicable.
3. Model Bias: Since these systems work and learn from the environment as per their training data, biased input data can lead to skewed outcomes, especially in the case of diverse populations. The datasets need to be comprehensive for equitable healthcare delivery.
Strategize Your Agentic AI Approach with KritiKal
It is therefore fair to conclude that artificially intelligent agentic systems are making a significant impact on the Medtech industry in terms of how healthcare is managed and delivered. These systems support clinicians, streamline their tasks, improve efficiency and enable personalized care. Businesses can leverage KritiKal’s extensive experience in handling LLMs, LMMs and agentic system development to set clear goals, improve data quality and enable ethical governance to realize AI’s true potential. Through such strategic partnerships, they can improve patient outcomes, lower costs, expand access to care and accelerate drug-related research and development. Our team of developers with expertise in deep learning and reinforcement learning, organizations can enhance the Medtech landscape as these systems become more intelligent and adaptive, ensuring the balance between innovation, privacy, compliance and transparency in the healthcare domain. Please get in touch with us at sales@kritikalsolutions.com to know more about our AI-powered services and realize your Medtech requirements.
Buddha Suchith currently holds the position of Senior Software Engineer at KritiKal Solutions. He has extensive experience in the Computer Vision domain and has been involved in multiple end to end computer vision based real time and POC projects using Python and C++ as main technologies. With his passion towards vision solutions and research, he has helped KritiKal in timely delivery of these projects to major SMBs and Fortune500 companies.