From cancer diagnosis to rare disease detection, artificial intelligence is fundamentally reshaping how we interpret medical images, streamline clinical workflows, and uncover patterns invisible to the human eye. But what does it really take to build trustworthy AI systems in healthcare? And how can organizations navigate the complex landscape of regulatory compliance, ethical considerations, and technical challenges?

In a recent episode of “AI Minute Mondays,” Suchi sat down with Nishant Agrawal, Associate Director of Machine Learning at PathAI, to explore the transformative role of AI in pathology. Their conversation reveals not just the technical innovations, but the human-centered approach required to deploy AI responsibly in healthcare.

From Computer Science to Healthcare Impact

Nishant’s journey into health tech wasn’t traditional. With a computer science background and specialization in machine learning and computer vision, he found his passion at the intersection of technology and social impact. After studying at Carnegie Mellon, he joined PathAI—a Boston-based health tech startup that was pioneering the digital transformation of pathology.

“I don’t come from a typical biology background,” Nishant admits, “but after working there for seven-plus years, I could maybe look at a pathology image and tell you that this is a cancer cell versus maybe this isn’t. Don’t take my word for it, though!”

His story reflects a broader trend: technologists with deep expertise in AI and machine learning are increasingly finding meaningful applications in healthcare, where their skills can have life-changing impact.

The Digital Transformation of Pathology

Pathology has historically been a manual, analog field. When you undergo a biopsy or routine screening, tissue samples are stained with specific antibodies and examined under a microscope by highly trained pathologists. These experts zoom in, pan across vast tissue samples, identify patterns, and assign diagnoses—a process that’s both time-consuming and inherently subjective.

But the field is rapidly becoming digitized. Modern scanners can now generate gigapixel images of tissue samples, creating a fascinating computer vision problem ripe for AI-driven disruption.

Real-World Applications: Where AI Makes a Difference

PathAI focuses on two primary verticals where AI is making significant impact:

1. Drug Development and Personalized Medicine

AI is helping biopharmaceutical companies accelerate drug development and identify which patients will respond best to specific therapies. A compelling example is PDL1 testing for immunotherapy.

The PDL1 Challenge:

  • PDL1 is a protein found on cancer cells and some immune cells
  • Higher PDL1 expression correlates with different prognosis and treatment options
  • Pathologists must manually estimate what percentage of cells express PDL1
  • This subjective assessment determines which patients receive life-changing immunotherapy

The AI Solution: Machine learning can automate this quantification with greater consistency and accuracy. PathAI’s research shows that ML-powered analysis can identify more patients who would benefit from immunotherapy—particularly those near threshold levels (around 1% expression) who might otherwise be missed.

“It really isn’t the most comfortable feeling when you find out that this is something that pathologists have to kind of eyeball and assign a percentage to,” Nishant explains. “This thing really should be done by machines, and maybe some human in the loop can review it.”

2. Digital Diagnostics in Clinical Settings

In routine testing laboratories, hospitals, and academic medical centers, AI can:

  • Prioritize cases: Flag high-risk patients whose samples should be reviewed first
  • Quality control: Identify staining issues before samples reach pathologists
  • Automate routine tasks: Free pathologists to focus on complex cases requiring expert judgment
  • Improve throughput: Increase diagnostic efficiency by an order of magnitude

These applications don’t replace pathologists—they augment their capabilities, allowing them to work more efficiently and focus on areas where human expertise is most valuable.

The Technology Behind the Scenes

PathAI’s machine learning team consists primarily of ML engineers and scientists who build deep learning models and own entire product lifecycles. Their work involves:

  • Model Development: Building deep learning models for specific pathology tasks
  • Research Innovation: Publishing on foundation models, novel evaluation strategies, and generalization techniques
  • Product Integration: Collaborating closely with product managers and customers
  • Continuous Iteration: Gathering feedback and refining products based on real-world performance

“We use machine learning to build these products and own entire product lifecycles,” Nishant explains. “Working with our customers to understand what’s most painful and annoying—things we really shouldn’t be relying on humans to do if they’re not good at it—then building something well thought out, evaluating it robustly, putting it in the field, gathering feedback, and iterating again.”

Building AI for healthcare requires navigating complex challenges around regulatory compliance, ethical considerations, accuracy, and data bias. PathAI’s approach centers on several key principles:

1. Robust Evaluation

Rather than establishing absolute “ground truth,” PathAI compares their AI systems against average pathologist performance on representative, intended-use populations. The FDA provides detailed guidelines on what these populations should look like, ensuring models are tested fairly and comprehensively.

2. Human-in-the-Loop Design

“It’s never about letting the machine run amok,” Nishant emphasizes. “Our focus is always on the human and the AI in the loop—seeing how that can really transform the field and move it forward.”

This approach reimagines pathology workflows rather than simply automating existing processes.

3. Model Interpretability

PathAI produces heat maps that show pathologists exactly where the AI is focusing its attention. Instead of scanning entire slides looking for malignancy, pathologists can review AI-flagged regions and agree or disagree with the system’s assessment.

This interpretability builds trust and allows pathologists to understand the AI’s reasoning process.

4. Comprehensive Risk Assessment

Before deploying any product, PathAI conducts thorough risk assessments to identify potential scenarios and edge cases that could pose risks to patient outcomes. These assessments inform product design decisions that eliminate or minimize error potential.

5. Proactive Monitoring

Once products are in the field, PathAI continuously monitors their performance and proactively works to improve them based on real-world data and feedback.

The Paradigm Shift: Beyond Technical Upgrades

AI in pathology represents more than just a technical upgrade—it’s a fundamental paradigm shift in how we approach diagnosis, drug development, and personalized medicine.

When implemented with proper controls and guardrails—robust evaluation, human-in-the-loop design, expert validation, and continuous monitoring—AI has the potential to:

  • Improve diagnostic accuracy and consistency
  • Accelerate drug development timelines
  • Enable personalized medicine by identifying which patients will respond to specific therapies
  • Increase healthcare access by augmenting limited pathology expertise
  • Reduce healthcare costs through improved efficiency

Key Takeaways for Organizations

For organizations considering AI adoption in healthcare or other high-stakes domains, PathAI’s approach offers valuable lessons:

  1. Start with real problems: Focus on use cases where AI can solve genuine pain points
  2. Design for human collaboration: Build systems that augment human expertise rather than replace it
  3. Prioritize interpretability: Make AI reasoning transparent and understandable
  4. Evaluate rigorously: Test against representative populations and real-world conditions
  5. Monitor continuously: Track performance in the field and iterate based on feedback
  6. Think beyond accuracy: Consider ethics, safety, equity, and user experience
  7. Build multidisciplinary teams: Combine technical expertise with domain knowledge

The Road Ahead

As pathology continues its digital transformation, the opportunities for AI-driven innovation will only grow. Foundation models, advanced evaluation strategies, and novel computational biomarkers promise to unlock insights that were previously impossible to detect.

But success will require more than just technical sophistication. It will demand a commitment to responsible AI development, continuous collaboration with healthcare professionals, and a relentless focus on patient outcomes.

“It’s always fun and motivating to talk about machine learning and technology-adjacent fields in healthcare,” Nishant reflects, “and how we can help move the needle there.”

For technologists passionate about social impact, healthcare AI represents one of the most exciting and meaningful frontiers in artificial intelligence.

About This Series

This article is based on an episode of AI Minute Mondays, where industry experts share insights on AI adoption, implementation, and impact across various domains. Watch the full conversation with Nishant Agrawal above to dive deeper into the technical details and hear more about his journey in health tech.

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