Contributed Commentary by Dr. Mark Lloyd, FUJIFILM Healthcare Americas Corporation
October 10, 2025 | There are more cases of cancer in the U.S. than ever before. Over 2 million new cases are expected to be diagnosed in 2025 alone, and cancer incidence is on the rise in 6 of the 10 most common cancers. Delivering speedy, accurate diagnoses and treatment for a growing pool of cancer patients is a daunting challenge for today’s clinicians. Making matters worse, pathologists play a key role in cancer diagnosis and there is currently a worldwide shortage of these specialists.
Fortunately, the integration of digital pathology and AI tools into cancer diagnostics is transforming the field. Research shows that AI-driven image analysis improves cancer detection, biomarker discovery, and diagnostic consistency. Additionally, the application of AI in cancer pathology has shown significant potential to enhance diagnostic speed and accuracy, streamline workflows, and support precision oncology.
Labor Shortage, Limitations of Traditional Methods
In some areas of rural America there is no access at all to traditional pathology services. Some remote healthcare settings simply lack resources like complex molecular and genetic testing as well as highly specialized pathologists. In such cases, cancer diagnoses may be delayed—negatively affecting outcomes.
An equally important challenge concerns the traditional process used in cancer pathology whereby diagnosis and staging rely on manual examination of histopathological slides. The downside is that this process can be time-consuming, subjective, and susceptible to human error—all of which can lead to inaccurate diagnoses and a slower start to treatment for patients.
Benefits of AI-Driven Digital Pathology
AI-driven digital pathology can effectively address the limitations of traditional methods to help boost better cancer outcomes. Simply put, AI solutions are designed to improve efficiency and productivity, optimize laboratory workflows, and reduce turnaround times.
- Speedier diagnostic process—Traditional methods require pathologists to manually examine thousands of tissue samples. AI algorithms can analyze a large volume of medical data in a fraction of the time, reducing turnaround time for cancer diagnosis.
- Improved diagnostic accuracy—Human interpretation can be impacted by many factors—experience level, distraction, complexity of samples, and more. Conversely, AI provides consistent results, ensuring that diagnoses are less likely to be missed or misinterpreted, particularly in difficult cases or rare cancer types.
- Reduced variability in human judgement—Interpretation of slides can vary between different pathologists and that can impact cancer grading and treatment decisions. AI can help standardize diagnoses by providing an objective analysis of tissue samples.
- Multi-modal approach—AI systems evaluate more than just the slide sample. Rather, AI can integrate diverse patient data—genomics, patient history, radiology images—for a more comprehensive patient picture which allows for more personalized treatment plans.
- Enhanced access/improved health equity—With AI tools, pathologists can collaborate to analyze digital slides from anywhere, providing timely and accurate diagnoses even in areas that lack access to specialized pathology services.
- Increased operational efficiency—With AI systems, pathology labs experience reduced error rates, quicker turnaround times and improved user experience—all of which boosts efficiency.
AI Capabilities, Cancer Applications
The capabilities of AI algorithms are vast. Some assist in detecting patterns and anomalies that may be easily missed by the human eye. Other algorithms analyze cellular structures and detect subtle variations that may indicate the presence of pathological conditions. Still others are trained for predictive modeling and prognosis, offering significant potential in accurately predicting the progression of certain cancers which can assist in treatment decisions.
The data continues to grow regarding successful applications of AI-driven digital pathology to help detect and classify an array of cancer types:
- Breast cancer—Due to AI’s advanced image recognition capabilities, an AI model demonstrated exceptional accuracy in identifying breast cancer metastases in lymph nodes. This application helps pathologists accelerate the assessment process, so cancer can be more swiftly detected, and patients receive treatment sooner.
- Pediatric sarcoma—The disparateness of sarcomas makes them difficult and time-consuming for pathologists to classify, yet it’s a key step for guiding treatment. In a 2025 study, an AI-driven model identified/classified sarcoma subtypes with high accuracy using digital images from pathology slides.
- Skin cancer— A key study showcased promising capabilities of AI in melanoma detection—the AI model demonstrated a sensitivity of 95.0%, surpassing the performance of dermatologists (88.9%) in accurately identifying malignant lesions. Another study explored tumor-infiltrating lymphocytes (TILs), which are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes. While the traditional pathologist-read TIL assessment on slides is prone to interobserver variability, the findings indicate that an AI-driven TIL quantification tool for melanoma may provide consistent, reliable assessments.
- Prostate cancer—With prostate cancer, AI models are used to predict the probability of tumor aggressiveness by analyzing histological features. One study identified an AI model that showed a 70% accuracy in predicting the likelihood of prostate cancer progression—surpassing conventional methodologies. This assists clinicians in personalized approaches to treatment planning.
The AI Advantage in Cancer Care
The research clearly demonstrates the powerful impact of AI-driven digital pathology for addressing growing needs in cancer care. While digital pathology adoption in U.S. hospitals is still slowly gaining traction, experts believe the stage is set for a major shift. Moreover, market research shows that the use of AI-enabled digital pathology has gained significant momentum in recent years.
For hospitals and labs seeking to digitize their platforms, a “scanner agnostic,” open platform solution is ideal to maintain flexibility in selecting the scanner technology and AI tools that best meet your organization’s evolving needs. Technology vendors are leveraging their own intelligent AI as well as partnering with leaders in the space to offer comprehensive AI digital pathology offerings.
AI-powered digital pathology solutions are proving to be a significant advantage in cancer care. Ultimately, these technologies are helping pathologists streamline workflows, deliver more timely and accurate diagnoses, improve patient safety, advance precision medicine, and support better treatment decisions.
Dr. Mark Lloyd is the VP of Pathology at FUJIFILM Healthcare Americas Corporation. A prolific researcher, Mark has authored over 120 publications and several thousand citations. He holds seven patents and has more than a dozen patent applications pending. He can be reached at mark.lloyd@fujifilm.com.