Most chronic diseases don’t begin with obvious symptoms or dramatic warning signs. Instead, they develop quietly over many years, as small changes accumulate in the body. A new perspective from researchers at the Buck Institute for Research on Aging notes that modern medicine often waits until disease is well underway and argues that new technologies could help detect risk much earlier, when prevention may be most effective.

The perspective, aptly titled “We Wait for Disease to Shout. What if We Listened When Biology Whispered?” introduces the concept of the “long tail” of biology. Rather than being caused by a single factor, most diseases and aging-related conditions develop from the combined impact of many small influences, including genetics, lifestyle, environmental exposures, sleep patterns, stress, and changes in the gut microbiome. Over time, these subtle shifts can gradually weaken the body’s resilience and increase the risk of chronic disease.

“By the time many diseases are diagnosed, the body has often been drifting off course for years,” said Nathan Price, PhD, Buck Institute professor, co-director of the Buck’s Center for Human Healthspan and senior author of the paper. “We now have the opportunity to detect those early changes by tracking what’s normal for each individual and noticing when biology starts to move in the wrong direction.”

The researchers highlight how diseases such as type 2 diabetes, heart disease, and neurodegenerative disorders often begin developing long before symptoms appear. For example, in type 2 diabetes, biological changes related to inflammation, metabolism, and insulin function can occur 10 to 15 years before blood sugar levels rise enough to trigger a diagnosis. The authors argue that catching these early warning signals could open the door to interventions that help delay or even prevent disease.

To make this possible, the perspective proposes a new personalized framework that treats each individual as their own biological reference point. By tracking changes over time, rather than comparing someone to population averages, researchers believe it may be possible to identify subtle shifts that signal increased risk.

Advances in health technology are making this approach increasingly realistic. Wearable devices can now continuously track heart rate, sleep, activity, and other physiological signals, while modern laboratory techniques allow scientists to measure thousands of biological markers from simple samples such as blood, saliva, urine, or even breath. Combined with artificial intelligence tools that can analyze complex patterns, these technologies could help translate large amounts of data into meaningful, personalized insights.

“Medicine has traditionally focused on treating disease after symptoms appear,” said Noa Rappaport PhD, lead author of the paper and an associate research professor at the Buck Institute. “Our goal is to shift toward protecting health by identifying risk earlier and understanding how each person’s biology changes over time.”

The authors also emphasize that major challenges remain. “Advanced biological testing can still be expensive, and healthcare systems are largely designed to treat illness rather than monitor long-term health,” said Lee Hood, MD, PhD, distinguished professor and co-director of the Buck’s Center for Healthspan. “Ensuring broad access to preventive technologies will be critical to preventing new health disparities. In addition, regulatory systems will need to adapt to evaluate new approaches that rely on personalized data and AI-driven analysis.”

Despite these challenges, the researchers say the tools needed to transform prevention are rapidly emerging. By combining wearable sensors, advanced biological measurements, and artificial intelligence, they envision a future in which healthcare focuses not just on treating disease, but on preserving health throughout life.

Citation: We Wait for Disease to Shout. What if We Listened When Biology Whispered?

DOI: 10.1016/j.cels.2025.101509  

Additional Buck Institute coauthor: Annalise Schweickart also contributed to the work.

COI: Nathan Price is chief scientific officer at Thorne and has a profit interest in the company. He also serves as an advisor to the Institute for Healthier Living, Abu Dhabi, and various companies where he has equity, including Vitaliti, Rue Four, ProPetDx, and Sera Prognostics.

Acknowledgements: This work was funded by an award from the Proactive Health Office of the Advanced Research Projects Agency for Health (ARPA-H) to the Personalized Analytics for Transforming Health (PATH) Project, the NIH NIA T32 AG000266 grant for Training in Basic Research on Aging and Age-Related Disease, and National Institutes of Health (NIH) grant no. U19AG023122 528

About the Buck Institute for Research on Aging

At the Buck, we aim to end the threat of age-related diseases for this and future generations. We bring together the most capable and passionate scientists from a broad range of disciplines to study mechanisms of aging and to identify therapeutics that slow down aging. Our goal is to increase human health span, or the healthy years of life. Located just north of San Francisco, we are globally recognized as the pioneer and leader in efforts to target aging, the number one risk factor for serious diseases including Alzheimer’s, Parkinson’s, cancer, macular degeneration, heart disease, and diabetes. The Buck wants to help people live better longer. Our success will ultimately change healthcare. Learn more at: 

Journal

Cell Systems

Funder

ARPA-H,
NIH/National Institute on Aging,
NIH/National Institutes of Health

DOI

10.1016/j.cels.2025.101509

The role of AI in biomedical engineering cannot be overstated. By harnessing vast amounts of data, researchers and clinicians can develop advanced diagnostic tools capable of identifying diseases and conditions at earlier stages than ever before. Machine learning models, trained on extensive datasets, can analyze patterns that human professionals might overlook, leading to more accurate diagnoses. Whether it’s interpreting complex imagery or evaluating genetic information, AI is revolutionizing the ways in which healthcare providers approach problem-solving and decision-making.

One noteworthy application of AI is in the realm of medical imaging technologies. Modern imaging techniques, such as MRI, CT scans, and ultrasound, generate immense volumes of data that require careful analysis. AI algorithms are adept at processing these images, improving phenomena such as image segmentation and synthesis. Consequently, these advancements not only elevate the accuracy of the readings but also significantly reduce the time required for radiologists and technicians to draw conclusions. This expedited analysis can lead to timely interventions, fundamentally changing patient prognoses.

Moreover, AI-driven tools are being utilized to enhance the design and development of medical devices. By employing sophisticated algorithms, engineers are equipped to optimize device functionality and performance. For instance, AI can facilitate the creation of smarter wearable devices that continuously monitor vital signs, sending alerts to healthcare professionals in real-time when anomalies are detected. The convergence of AI with device engineering not only fosters innovation but also ensures that the development process is tailored to meet the evolving needs of patients and providers alike.

AI is also at the forefront of personalized medicine, enabling tailored treatment plans based on individual patient profiles. With the integration of machine learning techniques, healthcare providers can analyze patient histories, genetic makeup, and lifestyle factors. This comprehensive approach allows for the prediction of treatment responses, fostering a paradigm shift where patients receive therapies that are more effective based on their unique characteristics. Personalized medicine is not just a theoretical ideal; it is becoming a practical reality thanks to AI’s capacity to handle multifaceted datasets in ways that were previously unimaginable.

The ethical implications surrounding AI in biomedical engineering also warrant attention. As reliance on AI systems increases, questions arise regarding data privacy, algorithmic bias, and accountability. Addressing these ethical considerations is crucial for fostering public trust and ensuring that technological advancements translate into equitable healthcare solutions. Continuous dialogue among engineers, clinicians, and policymakers will be essential in developing frameworks that govern the responsible use of AI technologies in clinical settings.

Research is flourishing in this area, with numerous studies investigating the multifaceted impact of AI across various aspects of healthcare. The ongoing exploration of these topics emphasizes the necessity for a collaborative approach among engineers, medical professionals, and AI specialists to maximize the benefits of technology in medicine. A multidisciplinary ethos is pragmatically essential to propagate understanding and navigate the intricate dynamics of AI’s application in healthcare environments.

In addition to diagnosis and treatment optimization, AI is instrumental in accelerating drug discovery processes. Pharmaceutical companies are leveraging machine learning models to identify potential compounds, predict interactions, and streamline clinical trials. By simulating interactions at a molecular level, researchers can focus resources on the most promising candidates while significantly reducing the time and cost associated with bringing new drugs to market. This revolution in drug development is poised to address some of healthcare’s most pressing challenges.

The potential integration of AI with neuroprosthetics is yet another avenue of exploration within biomedical engineering. Researchers are working towards creating intelligent prosthetic limbs that can respond to user intent through advanced neural interfaces. By interpreting brain signals and translating them into movement commands, these innovations may one day offer unparalleled levels of independence to individuals with mobility impairments, thereby redefining quality of life and personal agency.

As we forge ahead, continued advancements in AI must be met with vigilance regarding ethical, social, and practical implications. The future landscape of biomedical engineering is characterized by fusion and innovation, demanding that all stakeholders remain engaged and proactive in harnessing AI’s capabilities responsibly. This approach ensures that while we push the boundaries of technological advancements, we also advocate for solutions that prioritize human dignity, equity, and the overall betterment of society.

This new theme issue on "AI Applications in Biomedical Engineering" stands as a testament to the ongoing commitment to exploring and elevating these technological advancements. A collective scholarly effort will help illuminate areas of research that hold great promise, pushing the field towards impactful, real-world applications, and fostering an ecosystem where innovation in healthcare thrives.

As we look to the future, the integration of AI in healthcare and biomedical engineering is not merely a trend, but a transformative force reshaping the very foundations of medical practice and research.

Subject of Research: AI Applications in Biomedical Engineering
Article Title: Artificial Intelligence in Biomedical Engineering: A Revolutionary Frontier
News Publication Date: May 6, 2025
Web References: JMIR Biomedical Engineering
References: N/A
Image Credits: Credit: JMIR Publications

Keywords

Artificial Intelligence, Medical Imaging, Personalized Medicine, Drug Discovery, Ethical Implications of AI, Neuroprosthetics, Biomedical Engineering.