Multiplex immunofluorescence (mIF) promised to revolutionize pathology. With its ability to visualize multiple biomarkers simultaneously at single-cell resolution, it seemed destined to become the cornerstone of precision diagnostics. Yet, years later, mIF remains largely confined to research labs. Pathologists - the very people it was designed to empower - have been slow to adopt it. Why?
Some blame the pathologists, claiming they’re too conservative, too wedded to traditional methods to embrace innovation. But that critique misses the mark. The real issue isn’t the pathologists - it’s the technology itself.
The reality is that today’s mIF is an unwieldy maze of technical variables and subjective decisions. Every stunning mIF image comes at the cost of carefully calibrated exposure times, laser power, gain, and countless other settings. The work doesn’t end there - thresholding parameters, segmentation algorithms, and software post-processing further complicate the workflow.
The result? Data that may look impressive but often feels more like an art project than a reliable diagnostic tool. Variability is rampant. Two labs analyzing the same sample with the same markers can produce drastically different results simply due to differences in settings or assumptions baked into their workflows.
How, then, can pathologists trust these assays? Pathology is grounded in reproducibility and clarity, yet mIF, as currently practiced, offers neither in a consistent or dependable manner.
Trust is the cornerstone of pathology. A diagnosis must be reproducible, defensible, and free from unnecessary ambiguity. Current mIF workflows are inadequate on all three counts.
When the same sample can yield different results in different labs due to subjective tuning and software variability, who can pathologists trust? How can they be sure they’ve chosen the “right” image settings or algorithmic thresholds? Worse, the sheer number of variables often obscures whether observed patterns are biologically meaningful or merely artifacts of the process.
This isn’t resistance to change, it’s pragmatism. Pathologists understand the stakes better than anyone. Their hesitation stems not from conservatism but from a demand for rigor and reliability.
mIF was born in research labs, where variability and complexity are a feature not a bug. Researchers thrive on iterating workflows to refine results. But pathology isn’t research. It’s clinical practice. Variability in this context isn’t just inconvenient - it’s dangerous.
The common refrain from some in the tech world that “pathologists need to adapt” is both misguided and condescending. The burden isn’t on pathologists to conform to mIF. The responsibility lies with mIF developers to create tools that are trustworthy, reproducible, and intuitive.
Artificial intelligence can bridge the gap. By automating image acquisition, segmentation, and analysis, AI removes much of the subjectivity that undermines trust in mIF. Machine learning algorithms can ensure better reproducibility across labs by standardizing processes and eliminating human variability.
AI also simplifies interpretation. Instead of overwhelming pathologists with raw data, it can deliver concise, actionable insights that integrate seamlessly into their workflows. The goal isn’t to replace pathologists but to give them tools they can trust—tools that make their work more efficient and reliable. AI has the power to transform mIF from a research experiment into a clinical asset.
For mIF to succeed, the focus must shift from advocating for reproducibility to defining how to achieve it. Does the solution lie in better reagents, tighter controls, or standardized protocols? Or is the key embedding human expertise into algorithms and automating processes to minimize variability?
Equally critical is empowering pathologists by ensuring AI simplifies their work rather than complicates it. Pathologists prefer bright field imaging for good reason—it’s fast, reliable, and intuitive. To advance mIF, we must rethink how users interact with its images and data, creating workflows as seamless and natural as bright field imaging.
This reminds me of a debate I had years ago with friends devoted to their SLR cameras. They believed only skilled photographers could craft the perfect image through precise manual controls. Yet today, AI-powered smartphone cameras produce stunning results effortlessly, making professional-grade quality accessible to anyone. And who can forget the uproar from Blackberry loyalists when the iPhone launched? “How dare they remove the buttons? Buttons are for professionals!”
At its core, mIF is just another form of imaging. AI and deep learning have the potential to revolutionize it by streamlining workflows, ensuring consistency, broadening accessibility, and rebuilding trust through thoughtful standardization and automation. The future lies in embedding human expertise into systems that make mIF robust, reliable, and accessible to all.
Pathologists aren’t holding mIF back. They’re asking the right questions: Can I trust this? Is it reproducible? Will it help me help my patients? These are the questions mIF developers must answer.
The resistance isn’t a rejection of technology, it’s a demand for rigor, reliability, and respect for clinical standards. This isn’t a problem to fix; it’s a challenge to embrace.
Let’s build tools that earn their trust.