AI-Powered Tools make their Mark in Skin Cancer Diagnostics Market

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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 Proscias DermAITM
• DermAI was launched on 19th June 2019 by
  Proscia. It is a module
• on Proscias 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 pathologists 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 DermAIs performance using more than
  20,000 patient biopsy slides.

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• DermAIs 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 of 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 MITs 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 MITs 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)

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• 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

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