Five Healthcare and Life Sciences Trends to Watch for in 2025

As 2025 begins, a diverse array of technologies is opening up new possibilities for better human health and wellness. From nanosensors, which make it possible for the human body to interact with medical equipment in new ways, to new advanced medical imaging techniques, robotics, and artificial intelligence (AI), here are some key trends we expect to have a major impact on the Healthcare and Life Sciences field in 2025.

Sensors are all around us, in our homes and in the smartphones in our pockets. Now, nanosensors can be used inside us as well, either ingested or embedded within our bodies. There is growing interest in research and investment into the use of nanosensors to monitor our biochemistry, detect diseases and pathogens, and deliver therapeutic agents to selected cells and body regions more precisely than otherwise possible.

These capabilities are taking the concept of telehealth to a whole new level because patient monitoring, diagnosis, and care via these nanosensors support the trends toward “teleICUs” and “hospital-at-home”. These trends, where a patient can be monitored and treated effectively at home instead of having to go to a hospital, or not have to stay in a hospital as long, allows healthcare costs and workforce burdens to be lessened, and patient satisfaction to be increased.

As with any connected medical technology, it comes with challenges. The need for more robust and reliable connectivity, end-to-end security, data interoperability, and more caregiver and clinical home worker education are a few such challenges in these applications.

Cancer treatment using radiopharmaceutical drugs, sometimes referred to as nuclear oncology, is a relatively new but fast-growing trend that draws from chemistry, biology, physics, computer modeling, and nuclear imaging.

The idea is that a radioactive drug is injected into the body intravenously and then is selectively absorbed by specific tissues (such as a cancer lesion) throughout the body based on its specific binding mechanism. The radiation the drug releases is therefore localized to the lesions, leading to much more precise and effective targeting compared to external radiation sources, while minimizing radiation absorbed doses to surrounding healthy tissue. Several radiopharmaceutical drugs have been FDA-approved, and more are on the way.

The key to the most effective outcome with nuclear oncology is precision in delivering the exact quantity to the exact target. AI can potentially make this possible, but much more data is needed to train the models adequately. For example, many different types of measurements including scanner images, blood exams and other clinical biomarkers from the entire body are needed to determine with precision where and how much dosage will be deposited to minimize radiation exposure to healthy tissue and focus only on the diseased tissue. AI models require data that shows how the particular radioactive agent was produced, what type of scanner was used, and how the scanner was calibrated. Dynamic effects also need to be modeled, because circulation of the injected radioactive therapeutic agent across the human body changes over time after injection. IEEE SA can help build standards for such models.

AI has the ability to make this possible but more real-world data from diverse population sets, diagnostic imaging and genetic testing are required to train the models to be as exact as they need to be. However the data must be anonymized so that results from different patients and institutions can be shared and become available for training and validating useful, unbiased and explainable AI models that can be widely used and trusted by doctors and researchers in this field.

Another major medical imaging trend is the development of total-body PET scanners, where the whole body enters the scanner to simultaneously scan all organs from head to toe, or from the pelvis to the head. This technology allows data to be acquired much more efficiently than before, enabling much faster scans for the same administered dose or much lower dose for the same scan duration as before, without compromising the quality of the medical imaging exams. For example, a scan that used to take 10 minutes can now be completed in one minute for the same dose or with 1/10th of the original injected dose in 10 minutes. This translates to higher patient comfort and exam throughput or to significantly less radiation exposure for the patients and the medical personnel.

Because this technology allows more data to be acquired much faster, typical total-body exams contain very large amounts of data, which has become a critical issue for data management, transfer, and archiving systems in medical centers. The higher amount of details in the data means they can be analyzed at a deeper level in their raw format before they are reconstructed as medical images. Currently, the raw data is not standardized adequately. There is no uniform format across scanner manufacturers, so the data is not easily accessible or portable without proprietary knowledge and disclosures. Often, research agreements with each manufacturer is required, which can be a hurdle for academic research and development of AI models that can utilize the in-depth raw data information. Efforts are needed to standardize raw PET data so that a common, accessible and resource-efficient data format can be established for medical research and commercial applications exploiting the high fidelity information of the raw data.

According to the World Health Organization (WHO), between 2015 and 2050, the proportion of the world’s population over 60 years will nearly double from 12% to 22%. As the aging population continues to grow, the shortage of clinical, social, and familial caregiving support  grows as well. The situation is far more challenging in low- and middle-income countries,where there are fewer clinical resources and opportunities for medical education and training.

The use of robotic-based technologies, aided by growing AI capabilities, is increasingly regarded  as a way to alleviate clinical workforce shortages. For example, robotic systems could be used to collect, transport and deliver supplies and other materials throughout a medical facility. Other types of robotic systems could be used in physical therapy, to assist with exercises and to help with mobility issues, even for in-home care settings where an elderly person living independently might occasionally need such help. Robotics offer many different means of support to the aging population, including both clinical and emotional wellness.

Robotic-based digital technologies are also being developed to help with mental and emotional issues. Mental health issues are a growing epidemic in every age group and throughout the world, whether the issue at hand is a neurological disorder like post-traumatic stress disorder (PTSD); high levels of loneliness or anxiety as we age, or something else. Apps and virtual bots powered by AI, targeted at specific issues and outcomes, are being investigated as an enabling technology for highly immersive therapeutic and companionship experiences, as one way to address these issues.

Digital tools for mental health are one example. Mental health disorders are growing the fastest in lower- and middle-income countries, which also happen to have the fewest clinical therapists. Digital tools are filling the gap, but there aren’t enough safety, privacy, and trust guardrails in place, in the form of technical standards, best practices and guides, and regulatory measures.

To help overcome these hurdles, IEEE SA has established a Digital Mental Health Incubation Program. One of its first outputs is a white paper that proposes ethical guidelines for regulating mental health apps in an increasingly diverse and globalized world.

Other trends that aren’t quite as far along in development but are gathering momentum include the use of AI to characterize and use a person’s genome for highly individualized medical monitoring and treatment. (The genome comprises the specific characteristics of a person’s DNA). AI is key here because the human genome is vast; only a small portion has been mapped thus far, but even so, there are already many thousands of biomarkers of interest.

Genomic analysis is part of what’s known as precision health, a growing but incredibly data-intensive approach that also incorporates population-based data, a person’s specific disease state, social determinants of health, and other relevant data sets.

Because of the amount of personal data required, this trend comes with significant ethical considerations. Care must be taken so that AI outputs, which are open to the entire medical community for the common good, also protect patient privacy.

Another area where AI may play a significant role going forward has to do with aging populations in many areas of the world. Among the most common chronic conditions tied to aging and longevity are brain/neurological disorders such as Alzheimer’s and other forms of dementia. AI solutions are being developed to understand their causes more deeply and propose more effective treatments.

An emerging trend in the medical imaging field is the use of 3D printing to manufacture highly specialized test objects, called radioactive phantoms. With compartments of different concentrations of radioactive solutions, these containers can be used to conduct standardized quality assurance of clinical PET scanners, ensure harmonization of radioactive measurements across multiple sites in clinical trials and more. These 3D-printed phantoms more accurately resemble human anatomy than traditional cylindrical phantoms. They allow the distribution of radioactive tracers across the human body to enable more detailed studies of the molecular interactions of different tracers with specific organs and tissues.

3D printing can also be used to build special phantoms covering larger axial Fields of View (FOVs) optimized for total-body PET systems performance evaluation, which is not possible with current phantoms. However, these 3D printing methodologies have not yet been standardized. IEEE SA can help create standards for the 3D printing process to help guide safe and efficient deployment and harmonized application across the field of nuclear medicine.

Healthcare consumerism, where people are more empowered to take charge of their own healthcare decisions and personal data, may ultimately have its most significant impact in lower- and middle-income countries where traditional health resources are scarce.

That’s because low-cost, low-bandwidth wearable devices that individuals can use to track their vital signs, such as blood pressure, heart rate, blood sugar, etc., make a great deal of sense in those markets.

Newer wearables can do this with more precision than off-the-shelf fitness/activity trackers, and in the case of connected diabetes monitoring devices, the data they capture often can be transmitted to a centralized data service, where a health professional can help if needed. They do require reliable connectivity, though, and in many regions of the world and even in rural areas in higher-income countries, that is not always available.

Another challenge is that technical innovation is happening so quickly that, in some parts of the world, adequate regulation of these new medical devices and technologies falls short for various reasons, including a lack of global technical standards and full competency in evaluating and understanding of the technologies.

IEEE SA’s Healthcare and Life Sciences Practice supports innovation for the the global healthcare and life sciences ecosystem to develop solutions that enable sustainable access to quality care and improve overall wellness for all individuals. The practice consists of more than 1,500 multidisciplinary volunteer experts from 25 nations on six continents, all working together to advance practical, impactful solutions. We welcome your participation.

Learn More About IEEE SA’s Work in Healthcare and Life Sciences

Authors:

• Maria Palombini, Director, IEEE SA Healthcare & Life Sciences Global Practice
• Nikolaos A. Karakatsanis, Secretary, IEEE Nuclear Medical and Imaging Sciences Council, and liaison to IEEE SA from the IEEE Nuclear Plasma & Sciences Society