RSS bericht

Abstract

Purpose of Review

This review summarizes current and prospective applications of artificial intelligence (AI) and smartphone technologies to automated diagnosis and teledermatology.

Recent Findings

Healthcare institutions are rapidly scaling up telehealth programs and embracing long-distance consultation. New developments in deep learning, a type of artificial intelligence, have reached dermatologist-level performance in diagnosing cases of melanoma from lesion images. The smartphone industry projects that next-generation devices and widespread adoption will put deep learning-capable hardware in the hands of consumers everywhere in the coming decade.

Summary

The expansion in teledermatology programs over the past decade is driven by efforts to lower cost of care, expand access to underserved areas, and improve the monitoring of chronic conditions. Although long-distance diagnosis still underperforms relative to traditional, in-person diagnosis, deep learning technologies have demonstrated the potential to achieve results on par with face-to-face care. Current mobile app diagnosis systems rely on unproven technologies which do not achieve the same standard of accuracy. Over the next few years, research in teledermatology must refine deep learning methods to work with highly variable smartphone images in order to achieve functional long-distance diagnoses.

Abstract

Purpose of Review

This review summarizes current and prospective applications of artificial intelligence (AI) and smartphone technologies to automated diagnosis and teledermatology.

Recent Findings

Healthcare institutions are rapidly scaling up telehealth programs and embracing long-distance consultation. New developments in deep learning, a type of artificial intelligence, have reached dermatologist-level performance in diagnosing cases of melanoma from lesion images. The smartphone industry projects that next-generation devices and widespread adoption will put deep learning-capable hardware in the hands of consumers everywhere in the coming decade.

Summary

The expansion in teledermatology programs over the past decade is driven by efforts to lower cost of care, expand access to underserved areas, and improve the monitoring of chronic conditions. Although long-distance diagnosis still underperforms relative to traditional, in-person diagnosis, deep learning technologies have demonstrated the potential to achieve results on par with face-to-face care. Current mobile app diagnosis systems rely on unproven technologies which do not achieve the same standard of accuracy. Over the next few years, research in teledermatology must refine deep learning methods to work with highly variable smartphone images in order to achieve functional long-distance diagnoses.

Abstract

Purpose of Review

This review summarizes current and prospective applications of artificial intelligence (AI) and smartphone technologies to automated diagnosis and teledermatology.

Recent Findings

Healthcare institutions are rapidly scaling up telehealth programs and embracing long-distance consultation. New developments in deep learning, a type of artificial intelligence, have reached dermatologist-level performance in diagnosing cases of melanoma from lesion images. The smartphone industry projects that next-generation devices and widespread adoption will put deep learning-capable hardware in the hands of consumers everywhere in the coming decade.

Summary

The expansion in teledermatology programs over the past decade is driven by efforts to lower cost of care, expand access to underserved areas, and improve the monitoring of chronic conditions. Although long-distance diagnosis still underperforms relative to traditional, in-person diagnosis, deep learning technologies have demonstrated the potential to achieve results on par with face-to-face care. Current mobile app diagnosis systems rely on unproven technologies which do not achieve the same standard of accuracy. Over the next few years, research in teledermatology must refine deep learning methods to work with highly variable smartphone images in order to achieve functional long-distance diagnoses.

Abstract

Purpose of Review

This review summarizes current and prospective applications of artificial intelligence (AI) and smartphone technologies to automated diagnosis and teledermatology.

Recent Findings

Healthcare institutions are rapidly scaling up telehealth programs and embracing long-distance consultation. New developments in deep learning, a type of artificial intelligence, have reached dermatologist-level performance in diagnosing cases of melanoma from lesion images. The smartphone industry projects that next-generation devices and widespread adoption will put deep learning-capable hardware in the hands of consumers everywhere in the coming decade.

Summary

The expansion in teledermatology programs over the past decade is driven by efforts to lower cost of care, expand access to underserved areas, and improve the monitoring of chronic conditions. Although long-distance diagnosis still underperforms relative to traditional, in-person diagnosis, deep learning technologies have demonstrated the potential to achieve results on par with face-to-face care. Current mobile app diagnosis systems rely on unproven technologies which do not achieve the same standard of accuracy. Over the next few years, research in teledermatology must refine deep learning methods to work with highly variable smartphone images in order to achieve functional long-distance diagnoses.