AI-Powered Tools make their Mark in Skin Cancer Diagnostics Market
                     AI-Powered Tools make their Mark in Skin Cancer 
Diagnostics Market
Artificial Intelligence (AI) has donned a transformative role in skin cancer diagnostics,
with dermatologists playing a key part in its responsible development and 
implementation. AI entails the potential to enhance the speed and accuracy of skin 
cancer diagnosis, thereby generating better outcomes for patients. According to 
Inkwood Research, the global skin cancer diagnostics market is set to garner a 
revenue of $7252.15 million by 2032, projecting a CAGR of 7.13% during 2023-
2032.
This blog examines the existing and forthcoming AI-related diagnostic tools
making their mark in the global skin cancer diagnostics market.
 Mission Efficiency: Proscia’s DermAITM
DermAI™ was launched on 19th June 2019 by Proscia. It is a module 
on Proscia’s Concentriq™ platform. It leverages deep learning to classify and pre-
screen skin biopsies to help enhance laboratory quality & efficiency and minimize 
costly errors.
This development is against the backdrop of declining medical professionals entering
pathology. Besides, the standard diagnosis of the skin biopsies taken in the United 
States annually is based on a pathologist’s interpretation of tissue patterns through a
microscope. This 150-year-old subjective and manual practice lags with regard to the
rising demand for pathology diagnosis or critical data delivery for precision treatment.
The DermAI algorithm was trained and tested using patient biopsies from prominent 
commercial and academic dermis laboratories, including Thomas Jefferson 
University Hospital, University of Florida, Dermatopathology Laboratory of Central 
States, and Cockerell Dermatopathology. The multi-site study 
validated DermAI’s performance using more than 20,000 patient biopsy slides.
DermAI’s central capabilities include the following:
Improved Technical Component Reporting: It enables a dermatopathology lab to 
offer additional insights into its labwork. This will guide the lab in handling the 
professional component.
Automated QA: It analyses the entire caseload of the lab and provides an AI-based 
interpretation for every case. Also, DermAI offers an automated second layer of 
quality review across the lab.
Case Prioritization and Intelligent Workload Balancing: DermAI allows the lab to 
triage, sort, and prioritize cases. It optimizes the allocation of cases to 
dermatopathologists in a lab. The criteria include the order of cases to examine, 
subject matter expertise, and continuity.
Explains David West,  CEO o f  Proscia , “To date, attempts to apply AI to pathology 
have been engineered in isolated development environments using toy datasets. 
The challenge in fulfilling the promise of deep learning in diagnostic medicine is 
bringing to market a solution that can perform in the real world where we face 
tremendous variability among labs, systems, and specimen. Proscia is the first to 
deliver on this promise.” (Source)
 Eliminating Hassles Efficiently: MIT’s AI-Powered Tool for 
Melanoma Detection
As per MIT News, the researchers at MIT developed an AI-powered SPL 
(suspicious pigmented lesions) analysis system to precisely assess the pigmented 
lesion on the skin to detect anomalies. Physicians rely on visual inspection to identify
SPLs, which can indicate skin cancer. SPLs’ early-stage identification can 
considerably minimize treatment costs and enhance melanoma prognosis.
However, a swift finding of SPLs is difficult, impeded by the large volume of 
pigmented lesions that need evaluation. Accordingly, researchers 
at MIT collaborated to devise a new artificial intelligence pipeline using deep 
convolutional neural networks (DCNNs). These were applied to SPLs analysis 
through wide-filed photography.
Further, the tool uses DCNNs to effectively identify early-stage melanoma using 
cameras. The system was trained using 20,388 wide-filed images from 133 patients 
at the Hospital Gregorio Maranon in Madrid. The dermatologists then visually 
classified the lesions for comparison. The system displayed over 90.3% sensitivity in 
distinguishing SPLs from nonsuspicious lesions, thereby eliminating the need for 
time-consuming and cumbersome individual lesion imaging.
Says Luis R. Soenksen, a postdoc and a medical device expert currently acting 
 as   MIT’s   first Venture Builder in Artificial Intelligence and Healthcare , “Our research 
suggests that systems leveraging computer vision and deep neural networks, 
quantifying such common signs, can achieve comparable accuracy to expert 
dermatologists,” Soenksen explains. “We hope our research revitalizes the desire to 
deliver more efficient dermatological screenings in primary care settings to drive 
adequate referral.” (Source)
 Enroute Equalized Coherence: Dermalyser by AI Medical 
Technology
On 7th February 2023, AI Medical Technology, a Swedish start-up, announced the 
clinical trial results of Dermalyser conducted at 37 Swedish primary 
facilities. Dermalyser (a mobile application) is a diagnostic decision support system 
authorized with advanced artificial intelligence. The study included 240 patients 
seeking primary care for melanoma-suspected cutaneous lesions.
Dermalyser showcased an exceptional performance of 86% specificity 
and 95% sensitivity, surpassing primary care dermatologists and physicians.
Says Christoffer Ekström,  CEO of   AI Medical Technology , “The remarkably high 
sensitivity and specificity levels demonstrate the clinical performance and benefit of 
Dermalyser, particularly since the study was conducted in a real world, primary care 
setting representing different demographics, personnel, and geographical location.” 
(Source)
Further, Olle Larkö, Professor in Dermatology & Venereology and former Dean at 
Sahlgrenska University, adds, “Indeed exciting results, these numbers show 
potential of not only improving future visual diagnostic accuracy, but also decreasing
the amount of workload that dermatologist too often are dealing with in their daily 
practice. Nevertheless, additional studies are necessary to confirm the positive 
results.” (Source)
Future Implications of AI in Skin Cancer Diagnostics
Market
One application of artificial intelligence (AI) in skin cancer diagnosis is the use of 
deep learning algorithms to analyze skin lesion images. These algorithms can be 
directed on large datasets of images, facilitating the accurate identification of 
features and patterns associated with skin cancer. Another application of AI is 
decision support systems, which provide clinicians with recommendations and 
information about skin cancer treatment and diagnosis.
Furthermore, the use of AI in skin cancer diagnosis has the potential to minimize
healthcare costs and enhance patient outcomes. However, AI should not be treated
as a substitute for clinical judgment. Human expertise still triumphs when interpreting
the  results  generated  by  AI  algorithms.  Nevertheless,  several  AI-related
developments and tools are making their mark in the global skin cancer diagnostics
market.
By Akhil Nair
FAQs:
What are the different screening types used for skin cancer detection?
A: Dermatoscopy, biopsy imaging tests, lymph node, skin biopsy, and blood tests 
are the different screening types used for skin cancer detection
Which country projects promising growth potential for skin cancer 
diagnostics?
A: Germany projects promising growth potential for skin cancer diagnostics.