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Artificial intelligence framework reveals nuance in performance of multimodal AI for health care

Artificial intelligence framework reveals nuance in performance of multimodal AI for health care
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Dimitris bertsimasLuisr soenksenIgnacio fuentesAyu maSloan school of managementNational science foundation graduate research fellowshipAbdul latif jameel clinicMachine learningJameel clinicBoeing leadersGlobal operations professorNational science foundation graduate research

Developing a face mask that can detect COVID-19

Developing a face mask that can detect COVID-19
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Nuevo leóxLuisr soenksenPeter nguyenLuis soenksenEmily hendersonJohns hopkins universityMonterrey institute of technologyResearch scientistWyss instituteKeck graduate instituteUniversity of texasRice universityWyss institute at harvard universityHarvard universityVenture builderApple watch

How a Team from MIT is Using an AI Tool to Detect Melanoma

How a Team from MIT is Using an AI Tool to Detect Melanoma Thought LeadersLuis R. Soenksen Ph.D.Research Scientist and Venture Builder Massachusetts Institute of Technology AZoRobotics speaks with Luis R. Soenksen, entrepreneur and medical device expert, currently acting as MIT s first Venture Builder in Artificial Intelligence and Healthcare. Luis was part of a team that has developed a system that uses neural networks to spot ugly duckling pre-cancerous lesions on a patient s skin, helping to detect melanoma more efficiently. Can you give our readers a summary of your recent research? What were the main aims of the research?

Nuevo leóxLeadersluisr soenksenLuisr soenksenJoan nugentJoan nugentaprJohns hopkins universityMonterrey institute of technologyMassachusetts institute of technologyArtificial neural networksShowa teamInnovation initiativeConvolutional neural networksResearch scientistVenture builderArtificial intelligenceNeural networks

Using deep learning algorithm to spot skin cancer

Using deep learning algorithm to spot skin cancer Updated: Updated: April 07, 2021 13:39 IST The researchers tested the system against visually classified lesions by dermatologists and found that it achieved over 90% sensitivity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds. Share Article AAA Suspicious pigmented lesions, which can be indicative of skin cancer, are normally spotted by physicians through visual examination. | Photo Credit: Getty Images The researchers tested the system against visually classified lesions by dermatologists and found that it achieved over 90% sensitivity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds. (Subscribe to our Today s Cache newsletter for a quick snapshot of top 5 tech stories. Click here to subscribe for free.)

Luisr soenksenGregorio maraMassachusetts institute of technologyWorld health organisationWorld healthMassachusetts instituteUsing deep learningDermatologist level detectionSuspicious pigmented skin lesionsWide field imagesScience translationalVenture builderHospital gregorio maraக்ரிகாரியோ மாராமாசசூசெட்ஸ் நிறுவனம் ஆஃப் தொழில்நுட்பம்உலகம் ஆரோக்கியம் ஆர்கநைஸேஶந்

AI System Analyzes Wide-Field Images of Skin of Melanoma Patients

AI System Analyzes Wide-Field Images of Skin of Melanoma Patients Written by AZoRoboticsApr 5 2021 Melanoma is a kind of malignant tumor that causes over 70% of all skin cancer-related deaths across the world. For several years, physicians have depended on visual inspection to determine suspicious pigmented lesions (SPLs), which could indicate the occurrence of skin cancer. Image Credit: Animation courtesy of the researchers. In primary care settings, such early-stage determination of SPLs can enhance melanoma prognosis and considerably reduce the treatment cost. The difficulty is that it is challenging to find and prioritize SPLs quickly. This is because of the high volume of pigmented lesions that must usually be assessed for potential biopsies.

Comunidad de madridJamesj collinsMarthaj grayLuisr soenksenJuventudy deportesGregorio maraJw kieckhefer professor of health sciencesInstitute for medical engineeringVenture builderArtificial intelligenceMedical engineeringHealth sciencesTermeer professorDeep learningDermatologist level detectionSuspicious pigmented skin lesions