AI that Thinks Ahead.

Tools that See Beyond.

With FotoFinder AI, you bring artificial intelligence directly into your practice – intuitive to use, powerful in application, and precise in analysis.
Whether in Total Body Dermoscopy, dermoscopy, or trichoscopy: FotoFinder AI is the intelligent core of our technology.

Because modern skin diagnostics requires more than brilliant images. It needs systems that think with you – and smart tools that support confident decision-making in everyday clinical practice.
That’s why we developed two AI-powered solutions that complement each other perfectly – mobile or stationary, instantly available, and designed for in-depth comparisons.

The Expert Tool for Precise Assessment

Moleanalyzer pro

Your Real-Time AI Assistant

AIMEE

Automated Hair Analysis and Trichoscopy

Trichoscale DX

From Machine Learning to Deep Learning

Early algorithms relied on machine learning. Today, FotoFinder AI uses state-of-the-art deep learning models for pre-assessment of skin lesions. By mimicking human learning from examples and experiences, artificial neural networks enable complex processes similar to biological learning.

This requires a vast database. FotoFinder maintains one of the world’s largest collections of dermoscopic images with corresponding medical assessments. Continuous updates of the algorithms are possible thanks to decades of collaboration with physicians worldwide.

While many AI projects remain in prototype stage or lack sufficient data, the Moleanalyzer pro is already validated in numerous clinical studies.

MoleAnalyzer Pro

The Expert Tool for Precise Assessment

Our advanced analysis solution for high-end imaging systems provides extensive options for comparing and evaluating lesions.

Follow-ups, side-by-side comparisons, and intelligent analyses make it an indispensable tool for your daily practice.

AIMEE

Your Real-Time AI Assistant

AIMEE supports your decision-making with a clinically validated AI score – instantly, directly on the device. For more reliable results, faster decisions, and an enhanced patient experience.

Integrated into our high-end systems or available on-the-go with skeen and an active Hub Cloud Solutions subscription, AIMEE delivers unlimited AI evaluations for hundreds of lesions – flexible, reliable, and ready wherever you are.

Bodyscan

Intelligent Arrangement of Skin Lesions

The Bodyscan mosaic view extracts lesions from ALL total body images and arranges them intelligently on ONE screen – sorted by location or by category: new, changed, or unchanged.

In the Bodyscan comparison view, lesions are color-coded to highlight new, evolving, or stable findings at a glance.

Trichscale DX

Automated Hair Analysis and Trichoscopy

The Trichoscale DX software supports the evaluation of hair and scalp – painless, without plucking, and possible with trimmed or untrimmed hair.

It automatically calculates measured area, count, density, average length, anagen–telogen ratio, as well as the number and density of vellus and terminal hairs. In addition, it provides data on yellow dots and follicular units – a valuable tool for trichology consultations!

Scientific Studies

Sage Journals – 2023

Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients

M. Crawford, P. Hull et al. (2023)

JAMA Network - 2023

Human With Machine

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study

Winkler et al. (2023)

ScienceDirect – 2023

Observational study

Investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically ‘unclear’ by dermatologists

K. S. Kommoss at al. (2023)

EJC – 2022

Does sex matter?

Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection

Sies et al. (2022)

PubMed – 2021

Skin lesions of face and scalp

Classification by a market-approved convolutional neural network in comparison with 64 dermatologists

Haenssle et al. (2021)

PubMed – 2021

Comparative Study

The use of noninvasive imaging techniques in the diagnosis of melanoma: a prospective diagnostic accuracy study

MacLellan et al. (2021)

ScienceDirect – 2021

Original Research

Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition

Winkler et al. (2021)


 

ScienceDirect – 2020

Man against machine reloaded

Performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions

Haenssle et al. (2020)

PubMed – 2020

Skin lesions of face and scalp

Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.

Haenssle et al. (2020)

PubMed – 2020

Melanoma recognition by a deep learning convolutional neural network

Performance in different melanoma subtypes and localisations.

Winkler et al. (2020)

ScienceDirect – 2020

The Use of Non-Invasive Imaging Techniques in the Diagnosis of Melanoma

A Prospective Diagnostic Accuracy Study

MacLellan et al. (2020)

Nature Medicine – 2020

Human–computer collaboration for skin cancer recognition

Tschandl  et al. (2020)

PubMed – 2020

Past and present of computer-assisted dermoscopic diagnosis

Performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesion.

Sies et al. (2020)

PubMed – 2020

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas

Haenssle et al. (2020)

Wiley Online Library – 2019

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas

Fink et al. (2019)

PubMed – 20218

Man against machine

Haenssle et al. (2018)

The Story of FotoFinder AI Tools

FotoFinder recognized the potential of artificial intelligence for early skin cancer detection early on – and has been pioneering intelligent software solutions for dermatology ever since.

2023

AIMEE

Artificial Intelligence Mole Examination and Evaluation: The AI assistant revolutionizing dermoscopy workflows.

2019

Total Body Dermoscopy

Automated total body mapping & dermoscopy evolve into Total Body Dermoscopy.

2017

Moleanalyzer pro

The first intelligent skin analysis with AI scoring for lesion pre-assessment: Artificial Intelligence meets Human Experience.

2001

Bodyscan

A quantum leap in image comparison: the first Bodyscan master, developed with Fraunhofer IBMT.

1998

Tübinger Moleanalyzer

In cooperation with the University of Tübingen, FotoFinder launched the first machine-learning tool for dermatology – pioneering work that continues to shape the field today.