We’ve spent a lot of time over the past few months looking back at the history of The Pathologist and the field of laboratory medicine as a whole. And in doing so, we’ve highlighted various areas that could be key in moving the field forward. So what does the future hold? Although we cannot be definitive, one thing is for certain: technological advancements are certainly helping in shaping the future.
Interested in looking ahead? See the selection below!
Gerardo Fernandez explores the benefits of computational pathology in mitigating the effects of global pathologist shortages.
9. Pathologists at the Table: Keeping Our Place After COVID-19
As the COVID-19 pandemic moves further from people’s minds, will the work of pathologists follow suit and eventually be forgotten? Gary Procop discusses approaches to prevent this from occurring.
8. Better Breast Cancer Care for All
Sadaf Sarfraz and Muhammad Mustafa explore the potential of digital pathology in breast cancer diagnostics and care.
Like any new piece of equipment, it's crucial for users to fully understand the mechanics of their tools. AI, alongside digital and computational pathology, is no different, as Eric Walk explains.
6. MSI Marvel
Analytical science (specifically mass spectrometry) provides almost endless potential in clinical applications. Eva Cuypers joins us to discuss the possibilities.
With a reduced workforce and increased workload, histopathologists are pushed to breaking point. But the emerging digital era could empower patients and staff in diagnostics.
4. Picture Perfect: Embracing Digital Pathology 2.0
This first article in a six-part series highlights the exciting future digital pathology presents in the clinical lab.
3. An Imaging Revolution: from CERN to the Clinic
Is secondary ion mass spectrometry imaging the future of digital molecular pathology? Ron. M. A. Heeren discusses the benefits this application brings to diagnostics.
Pathology is at a crossroads after the effects of the COVID-19 pandemic and three key pathology figures believe that “digitizing pathology” is the way forward.
What is “deep learning” and how does it differ from “machine learning”? Chen Sagiv provides a simple explanation to fuel your understanding moving into the lab of the future.
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