Professor Saeed Amal: Orchestrating Intelligence for Precision Medicine with a Human Pulse

Medicine has always been about pattern recognition, hearing what the body whispers before it begins to scream. However, as healthcare data multiplies in size and complexity, even the most trained eyes and experienced minds need help seeing the full picture. That’s where artificial intelligence (AI) steps in, not to replace human judgment, but to amplify it.

At this intersection of algorithms and empathy is where you’ll find Professor Saeed Amal, a trailblazer who doesn’t just build smarter models but designs systems that listen like a seasoned physician, think like a scientist, and serve like a clinician. As the founder of The Amal Lab for Precision Medicine at Northeastern University and a visiting professor at Harvard Medical School, Prof. Amal has devoted his career to transforming data into life-saving insight, especially for patients facing cancer, cardiovascular disease, or barriers to care, where he developed advanced AI models to highly accurate diagnoses of 32 different types of cancers and developed Agentic AI methods to redefine medicine and healthcare in general

Bridging Algorithms and Human Health

Prof. Saeed Amal’s path began with his degree in computer science at the Technion, where an early interest in computer science sparked a lasting fascination with algorithms and their problem-solving power. It wasn’t until graduate school, however, that his focus widened, seeing the profound potential of data and AI, particularly in fields like medicine.

After completing his PhD, Prof. Amal joined Stanford University’s School of Medicine as a postdoctoral fellow. That phase proved pivotal. Working closely with clinicians and researchers, he witnessed the tangible role artificial intelligence could play in healthcare. He began applying deep learning to clinical datasets and digital pathology, recognizing that AI’s true promise wasn’t just in automation, it lay in its life-saving capabilities, especially for conditions like cardiovascular disease and cancer.

Before his current academic tenure, Prof. Amal served as VP of R&D at an AI med-tech startup. That time in the industry offered valuable lessons on translating research into practical solutions, an experience that continues to inform his approach today.

Now based at Northeastern University’s College of Engineering and the Roux Institute, Prof. Amal leads the Amal Lab for Precision Medicine. There, he and his interdisciplinary team work at the intersection of Agentic AI for autonomously planning, making decisions, and taking actions toward revolutionizing precision medicine and drug discovery, Artificial Intelligence, Multimodal medical data, and clinical insight, with the goal of enhancing diagnostic precision and personalizing treatment. The lab concentrates on cancer and cardiovascular disease, advancing tools for diagnostics, prognostics, treatment recommendations and planning, biomarkers discovery, and drug discovery.

Prof. Amal also holds a visiting professorship at Harvard Medical School’s Center for Transplant Research at Brigham and Women’s Hospital. His work there has included the development of transformer models to analyze digital pathology biopsies, providing accurate and timely predictions for kidney cancer. The research extends further, with a strong focus on AI methods to forecast kidney deterioration and rejection post-transplantation.

Across each chapter of his career, Prof. Amal has remained focused on connecting foundational science to real-world healthcare problems, motivated always by the difference it can make in people’s lives.

Integrating Intelligence in the Service of Care

Prof. Amal’s focus on merging artificial intelligence with cardiovascular healthcare began to take shape during his postdoctoral fellowship at Stanford University. Immersed in a collaborative environment that brought together cardiologists and data scientists, he saw firsthand the massive volume and complexity of clinical data being generated, ranging from ECGs and imaging to lab reports and physician notes. What stood out most was how little of that information was being fully leveraged to make timely, accurate decisions.

With cardiovascular disease remaining a leading global cause of death, the opportunity to apply AI for real clinical impact was both urgent and obvious. Prof. Amal recognized a critical need for tools that could synthesize multiple data types, identify early warning signs, assess risk more precisely, and support truly personalized care, capabilities that traditional approaches often struggled to deliver.

As he continued this line of research, the vision expanded. AI, he saw, could empower clinicians to act sooner and with greater clarity, while also making advanced diagnostics available at scale, even in under-resourced settings. That mission, using AI to close care gaps and improve patient outcomes, has remained the central force driving his work ever since.

Leading the Evolution of Multimodal Care Tools

In Prof. Amal’s work, where AI meets healthcare, a few core technologies continue to stand out as truly transformative.

One of the most significant breakthroughs has been the use of multimodal data, bringing together various sources such as medical imaging, electronic health records, genomic data, and even unstructured clinical text. By integrating these diverse inputs, Prof. Amal and his team have been able to create richer, more accurate models of individual patients. For instance, combining echocardiogram videos with clinical notes offers a much deeper understanding of how cardiovascular disease unfolds over time.

Agentic AI, Large Language Models, and Computer Vision that are based on Deep learning have also been a cornerstone of his approach. With convolutional neural networks (CNNs) and transformer-based models, Prof. Amal has tackled complex data like ECGs, pathology slides, and clinical narratives. These advanced models excel at uncovering patterns that may be invisible to traditional tools, or even to trained clinicians, offering new potential for earlier and more precise diagnoses, therapy recommendations, biomarker discovery, and drug discovery.

But building high-performing models isn’t enough. Prof. Amal places strong emphasis on explainable AI (XAI), recognizing that for clinicians to adopt these tools, they need to understand and trust the insights. His research incorporates interpretability techniques such as attention maps and feature attribution, making AI decisions more transparent, actionable, and clinically grounded.

Another area of growing promise in his work involves foundation models and self-supervised learning, especially in environments where labeled medical data is scarce. These methods enable more scalable and generalizable model development, improving performance across diverse patient populations.

Across all of these innovations, one principle guides Prof. Amal’s work: respect for the clinical context. He believes that metrics alone aren’t enough; AI must be designed to serve the people who rely on it at the bedside, enhancing, not complicating, the decisions that matter most.

Overcoming Barriers Between Code and Care

The path to building meaningful AI in healthcare has never been without its challenges, and Prof. Amal has encountered both technical and human hurdles along the way.

Early on, one persistent obstacle was gaining access to high-quality, representative clinical data. Much of this data is locked away in silos, often incomplete, messy, or unstructured. To overcome that, Prof. Amal and his team built strong, trust-based collaborations with hospitals and clinicians, not just to gain access, but to interpret the data in ways that align with how care is actually delivered. These clinical partnerships became essential in grounding the research in real-world relevance.

Another major challenge was translating innovations from the lab into busy clinical environments. AI models that perform well in controlled settings don’t always fit seamlessly into the fast-paced, high-stakes world of medicine. To close that gap, Prof. Amal focused on co-designing tools with physicians, engaging them early and often. This approach led to solutions that weren’t just technically sound, but also practical, intuitive, and trusted by the clinicians using them.

Team-building also posed its own complexities. Bringing together AI researchers, clinicians, and data engineers meant navigating very different vocabularies, workflows, and perspectives. Prof. Amal responded by fostering a lab culture built on mutual respect and continuous learning. Everyone is encouraged not just to master the technical “how,” but to deeply understand the clinical “why” behind their work.

And like any meaningful innovation journey, there were failures along the way, ideas that didn’t work out, models that didn’t perform as expected. But each setback became a learning moment. Prof. Amal and his team used those experiences to refine their thinking, strengthen their methods, and ultimately build more resilient, impactful solutions.

Pioneering Solutions That Clinicians Trust

Among the milestones in Prof. Amal’s career, one of the most meaningful was receiving the Emerging Visionary Award for AI and Healthcare from the National Academy of Inventors in 2025. The honor was more than a personal achievement; it was a powerful affirmation of his efforts to bring artificial intelligence into real clinical practice. The award highlighted his commitment to creating tools that don’t just live in research papers but support patients and clinicians in real-world care settings. It also reflected the interdisciplinary nature of his work, a quality he sees as essential in advancing healthcare.

Another key moment came when he was recognized with a TIER 1 Research Development Award for his groundbreaking work integrating AI into clinical pathology. His project, in collaboration with Santovia Path AI and Prima CARE, focuses on building AI tools to speed up cancer diagnoses, particularly for breast and prostate cancer. By bringing together digital pathology and machine learning, these partnerships aim to enhance both accuracy and efficiency in clinical workflows.

The work itself demonstrates how artificial intelligence, when used thoughtfully, can significantly improve patient outcomes. By integrating multimodal data, ranging from medical imaging and electronic health records to genomics, proteomics, and transcriptomics, Prof. Amal’s research seeks to reduce diagnostic delays and support more confident clinical decisions.

Prof. Amal was also named a “Rising Star” in the 2021 edition of Frontiers in Cardiovascular Medicine during his tenure at Stanford. His recognized work centered on the use of multimodal data and machine learning to improve cardiovascular care, integrating data from wearable sensors, imaging, genomics, and EHRs to create smarter diagnostic and treatment systems.

Another notable milestone was securing funding and finalizing a commercialization agreement for a patent on AI-based digital pathology, specifically focused on classifying cancerous cells in digitized tissue slide images. The project marks a major step toward bringing precision diagnostics to clinical environments at scale.

Prof. Amal also introduced an AI innovation into the first vascular interventional suite in New England at the Prima CARE Center for Vascular Disease. This system predicts adverse cardiovascular events such as amputation or mortality and offers personalized treatment recommendations to prevent them, a testament to his belief in actionable, preventive care.

Finally, one of his most personal accomplishments was founding The Amal Lab for Precision Medicine at Northeastern University. From setting the vision to recruiting an interdisciplinary team, the lab was designed to be more than just a research space. It’s a mission-driven hub where innovation, empathy, and science converge to create meaningful tools for real patients and real clinicians.

Fostering Innovation Through Human Connection

What sets Prof. Amal’s approach apart is his deeply integrated vision of artificial intelligence and healthcare, not as separate disciplines, but as fundamentally interconnected. From the beginning, he’s worked side by side with clinicians to ensure the AI models he develops tackle genuine clinical challenges and can function seamlessly within everyday medical workflows. This translational mindset, focused on bridging research with real-world utility, is a defining strength of his work.

Another key pillar of his approach is a commitment to multimodal, human-centered AI. Rather than limiting models to a single data source, Prof. Amal and his team build systems that draw on a wide range of patient information: from medical imaging and EHRs to genomics and physician notes. However, just as important as data diversity is the design philosophy behind the tools. His AI systems are crafted to work with people, not around them, emphasizing interpretability, usability, and clinical trust as core values, not afterthoughts.

Prof. Amal’s experience across academia, startups, and industry has also shaped a rare, practical understanding of the full innovation pipeline. He knows how to take an idea from concept to clinical deployment, balancing the rigorous pace of research with the urgency and constraints of real healthcare environments. That agility has made his lab uniquely positioned to deliver impact quickly and meaningfully.

Finally, mentorship and team culture are central to how Prof. Amal leads. He believes that meaningful innovation comes from empowered people, not just good ideas. He invests intentionally in building a collaborative, cross-disciplinary environment where each team member feels ownership, and where continuous learning is part of the lab’s DNA.

Grounding Innovation in Real-World Challenges

Prof. Amal’s advice to future researchers and entrepreneurs in AI healthcare begins not with technology, but with empathy. Real progress, he believes, doesn’t come from chasing the latest algorithms; it comes from understanding the problem first. That means listening closely to the needs of patients, clinicians, and healthcare systems. In his view, innovation should follow insight. Technology is powerful, but only when it’s built in service to the people does it aim to help.

Prof. Amal also urges the next generation to embrace interdisciplinary fluency. The future of healthcare AI, he believes, will be built not by isolated technical experts but by teams that bring together clinicians, engineers, ethicists, data scientists, and patients themselves. He encourages young innovators to step out of their comfort zones, to observe surgeries, attend hospital rounds, and engage with regulators. For him, context is everything, and the best tools are those that are shaped by a full view of the system they’re designed to support.

He’s also honest about the pace of progress. Healthcare moves more slowly than other sectors, and for good reason. Prof. Amal emphasizes that navigating data privacy, clinical validation, and real-world adoption takes time and patience. He reminds others that success doesn’t just belong to those with the sharpest minds; it comes to those who stay with the problem, who remain persistent in the face of obstacles.

Lastly, he emphasizes the need for ethical responsibility and equity. As AI systems become more influential in healthcare decisions, Prof. Amal believes researchers must be intentional about who their tools serve and who they might exclude. Fairness, transparency, and accountability aren’t optional add-ons in his framework; they are foundational to building AI that truly serves all.

Mentoring the Next Wave of Healthcare Innovators

Looking ahead to 2026 and beyond, Prof. Amal is focused on scaling up the work that has defined his career, bringing AI-driven solutions into real clinical environments to meaningfully improve patient care.

At The Amal Lab for Precision Medicine, his team is intensifying efforts in AI-based precision healthcare, especially around cancer and cardiovascular disease. A central goal is to integrate real-time diagnostic and prognostic tools directly into clinical workflows, delivering personalized, actionable insights to clinicians at the point of care.

One area that particularly excites him is early-stage detection. By using AI to identify subtle signals in patient data, signals that often go unnoticed by traditional approaches, Prof. Amal aims to move from reactive treatment toward predictive, preventive care. These efforts could fundamentally shift how we approach chronic conditions, catching them before they escalate.

His lab is also advancing its distinctive multimodal AI techniques, combining medical imaging, genetic data, and clinical records to develop more holistic models of patient health. These models aren’t just about identifying disease; they’re about building personalized prevention strategies that give patients and physicians the tools to stay ahead of long-term health risks.

Equally important to him is healthcare equity. Prof. Amal is passionate about expanding access to advanced diagnostic tools in under-resourced communities, where access to specialists and high-end diagnostics is often limited. He’s actively building partnerships to ensure that AI not only enhances care but also democratizes it, making high-quality healthcare more widely available.

In all, 2026 marks a phase of expansion and acceleration, taking the breakthrough ideas and models his lab has developed and scaling them into broader clinical practice, where they can make a real difference in people’s lives.

Leaving a Legacy Rooted in Purpose and People

When reflecting on the legacy he hopes to leave, Prof. Amal envisions a future where AI and multimodal medical data, spanning imaging, electronic health records, and omics, work together to fundamentally improve healthcare. His goal isn’t simply to push innovation for its own sake, but to build systems that change lives: by enabling earlier disease detection, empowering clinicians, and making high-quality care accessible to all.

Central to his vision is a deep commitment to patient-centered design. Prof. Amal believes that no matter how advanced the technology becomes, its true value lies in how well it serves people. His hope is to leave behind a framework where AI is not only powerful but also ethical, transparent, and equitable. That means building tools that can be trusted, tools that are fair, explainable, and designed with a clear sense of responsibility.

He also wants to inspire future leaders to prioritize collaboration over competition. Prof. Amal sees the future of AI in healthcare as something that will be shaped not by lone geniuses but by diverse, interdisciplinary teams, bringing together engineers, clinicians, ethicists, and patients to solve complex challenges from every angle. One part of his legacy, he hopes, will be helping create a culture where that kind of inclusive teamwork is the norm.

Mentorship is another pillar of his legacy. Prof. Amal hopes to be remembered for the time he spent supporting and guiding the next generation, helping young researchers navigate the complexities of healthcare AI, encouraging them to ask bold questions, and giving them room to grow. He believes mentorship is one of the most powerful ways to create a lasting impact, multiplying influence across people and projects.

Ultimately, Prof. Amal aims to leave behind not just ideas or papers, but real tools and systems that have changed how medicine is practiced, tools that help detect illness sooner, make better decisions easier, and bring high-quality care to those who need it most. If AI can help make healthcare more personalized, preventative, and equitable, that, to him, would be a legacy worth leaving.

Envisioning a Smarter, Fairer Future in Medicine

If Prof. Amal had to distill his message about the future of AI in healthcare into a single sentence, it would be this:

“The future of AI in healthcare isn’t just about building smarter machines, it’s about designing a more personalized, equitable, and accessible system that empowers both patients and providers to make better, data-informed decisions for longer, healthier lives.”

For Prof. Amal, it’s not just a technological vision, it’s a human one.