Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz: The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. In: Translational Cancer Research. Band7, Nr.3, 6. Juli 2018, ISSN2219-6803, S.803–816, doi:10.21037/21823 (amegroups.com [abgerufen am 9. Dezember 2018]).
br.de
Künstliche Intelligenz erkennt Alzheimer früher als Ärzte. In: BR24. (br.de [abgerufen am 1. Dezember 2018]).
Jeffrey David Iqbal, Rasita Vinay: Are we ready for Artificial Intelligence in Medicine? In: Swiss Medical Weekly. Nr.19, 19. Mai 2022, doi:10.4414/smw.2022.w30179 (smw.ch [abgerufen am 26. August 2022]).
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz: The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. In: Translational Cancer Research. Band7, Nr.3, 6. Juli 2018, ISSN2219-6803, S.803–816, doi:10.21037/21823 (amegroups.com [abgerufen am 9. Dezember 2018]).
P Tschandl, N Codella, BN Akay, G Argenziano, RP Braun, H Cabo, D Gutman, A Halpern, B Helba, R Hofmann-Wellenhof, A Lallas, J Lapins, C Longo, J Malvehy, MA Marchetti, A Marghoob, S Menzies, A Oakley, J Paoli, S Puig, C Rinner, C Rosendahl, A Scope, C Sinz, HP Soyer, L Thomas, I Zalaudek, H Kittler: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. In: The Lancet. Oncology. 20. Jahrgang, Nr.7, Juli 2019, S.938–947, doi:10.1016/S1470-2045(19)30333-X, PMID 31201137.
P Tschandl, C Rosendahl, H Kittler: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. In: Scientific data. 5. Jahrgang, 14. August 2018, S.180161, doi:10.1038/sdata.2018.161, PMID 30106392.
HA Haenssle, C Fink, R Schneiderbauer, F Toberer, T Buhl, A Blum, A Kalloo, ABH Hassen, L Thomas, A Enk, L Uhlmann, Groups. Reader study level-I and level-II: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. In: Annals of oncology: official journal of the European Society for Medical Oncology. 29. Jahrgang, Nr.8, 1. August 2018, S.1836–1842, doi:10.1093/annonc/mdy166, PMID 29846502.
P Tschandl, C Rosendahl, BN Akay, G Argenziano, A Blum, RP Braun, H Cabo, JY Gourhant, J Kreusch, A Lallas, J Lapins, A Marghoob, S Menzies, NM Neuber, J Paoli, HS Rabinovitz, C Rinner, A Scope, HP Soyer, C Sinz, L Thomas, I Zalaudek, H Kittler: Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. In: JAMA dermatology. 155. Jahrgang, Nr.1, 1. Januar 2019, S.58–65, doi:10.1001/jamadermatol.2018.4378, PMID 30484822.
MA Marchetti, NCF Codella, SW Dusza, DA Gutman, B Helba, A Kalloo, N Mishra, C Carrera, ME Celebi, JL DeFazio, N Jaimes, AA Marghoob, E Quigley, A Scope, O Yélamos, AC Halpern, Collaboration. International Skin Imaging: Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. In: Journal of the American Academy of Dermatology. 78. Jahrgang, Nr.2, Februar 2018, S.270–277.e1, doi:10.1016/j.jaad.2017.08.016, PMID 28969863.
HA Haenssle, C Fink, F Toberer, J Winkler, W Stolz, T Deinlein, R Hofmann-Wellenhof, A Lallas, S Emmert, T Buhl, M Zutt, A Blum, MS Abassi, L Thomas, I Tromme, P Tschandl, A Enk, A Rosenberger, Groups. Reader Study Level I and Level II: 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. In: Annals of oncology: official journal of the European Society for Medical Oncology. 31. Jahrgang, Nr.1, Januar 2020, S.137–143, doi:10.1016/j.annonc.2019.10.013, PMID 31912788.
P Tschandl, H Kittler, G Argenziano: A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions. In: The British journal of dermatology. 177. Jahrgang, Nr.3, September 2017, S.867–869, doi:10.1111/bjd.15695, PMID 28569993.
SS Han, MS Kim, W Lim, GH Park, I Park, SE Chang: Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. In: The Journal of investigative dermatology. 138. Jahrgang, Nr.7, Juli 2018, S.1529–1538, doi:10.1016/j.jid.2018.01.028, PMID 29428356.
C Yu, S Yang, W Kim, J Jung, KY Chung, SW Lee, B Oh: Acral melanoma detection using a convolutional neural network for dermoscopy images. In: PloS one. 13. Jahrgang, Nr.3, 2018, S.e0193321, doi:10.1371/journal.pone.0193321, PMID 29513718.
A Esteva, B Kuprel, RA Novoa, J Ko, SM Swetter, HM Blau, S Thrun: Dermatologist-level classification of skin cancer with deep neural networks. In: Nature. 542. Jahrgang, Nr.7639, 2. Februar 2017, S.115–118, doi:10.1038/nature21056, PMID 28117445.
S Polesie, M Gillstedt, H Kittler, A Lallas, P Tschandl, I Zalaudek, J Paoli: Attitudes towards artificial intelligence within dermatology: an international online survey. In: The British journal of dermatology. 17. Januar 2020, doi:10.1111/bjd.18875, PMID 31953854.
SS Han, I Park, W Lim, MS Kim, GH Park, JB Chae, CH Huh, SE Chang, JI Na: Augment Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. In: The Journal of investigative dermatology. 5. Februar 2020, doi:10.1016/j.jid.2020.01.019, PMID 32243882.
A Lallas, G Argenziano: Artificial intelligence and melanoma diagnosis: ignoring human nature may lead to false predictions. In: Dermatology practical & conceptual. 8. Jahrgang, Nr.4, Oktober 2018, S.249–251, doi:10.5826/dpc.0804a01, PMID 30479851.
C Navarrete-Dechent, SW Dusza, K Liopyris, AA Marghoob, AC Halpern, MA Marchetti: Automated Dermatological Diagnosis: Hype or Reality? In: The Journal of investigative dermatology. 138. Jahrgang, Nr.10, Oktober 2018, S.2277–2279, doi:10.1016/j.jid.2018.04.040, PMID 29864435.
A Ngoo, A Finnane, E McMeniman, JM Tan, M Janda, HP Soyer: Efficacy of smartphone applications in high-risk pigmented lesions. In: The Australasian journal of dermatology. 59. Jahrgang, Nr.3, August 2018, S.e175-e182, doi:10.1111/ajd.12599, PMID 28240347.
Yiming Ding, Jae Ho Sohn, Michael G. Kawczynski, Hari Trivedi, Roy Harnish: A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. In: Radiology. 6. November 2018, ISSN0033-8419, S.180958, doi:10.1148/radiol.2018180958 (rsna.org [abgerufen am 1. Dezember 2018]).
Pfau M, Walther G, von der Emde L, Berens P, Faes L, Fleckenstein M, Heeren TFC, Kortüm K, Künzel SH, Müller PL, Maloca PM, Waldstein SM, Wintergerst MWM, Schmitz-Valckenberg S, Finger RP, Holz FG: Artificial intelligence in ophthalmology: Guidelines for physicians for the critical evaluation of studies. In: Ophthalmologe. 117. Jahrgang, Nr.10, Oktober 2020, S.973–988, doi:10.1007/s00347-020-01209-z, PMID 32857270.
Ruamviboonsuk, P., Krause, J., Chotcomwongse, P. et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. npj Digit. Med.2, 25 (2019). doi:10.1038/s41746-019-0099-8
A. Y. Lee et al.: Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems. In: Diabetes Care 2021; 44: S. 1168–1175, doi:10.2337/dc20-1877
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. In: Nat Biomed Eng. 2. Jahrgang, Nr.3, März 2018, S.158–164, doi:10.1038/s41551-018-0195-0, PMID 31015713.
Dieck S, Ibarra M, Moghul I, Yeung MW, Pantel JT, Thiele S, Pfau M, Fleckenstein M, Pontikos N, Krawitz PM: Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals. In: Transl Vis Sci Technol. 9. Jahrgang, Nr.7, Juni 2020, S.8, doi:10.1167/tvst.9.7.8, PMID 32832215, PMC 7414790 (freier Volltext).
Batchu et al. Review of Applications of Machine Learning in Mammography and Future Challenges; Oncology 2021 doi:10.1159/000515698
Arash Azhand, Sophie Rabe, Swantje Müller, Igor Sattler, Anika Heimann-Steinert: Algorithm based on one monocular video delivers highly valid and reliable gait parameters. In: Scientific Reports. 11, 2021, doi:10.1038/s41598-021-93530-z.
Avishek Choudhury, Emily Renjilian, Onur Asan: Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review JAMIA Open, 3, 2020, 459–471 doi:10.1093/jamiaopen/ooaa034
Stellungnahme Vorstand Bundesärztekammer: Präzisionsmedizin: Bewertung unter medizinisch-wissenschaftlichen und ökonomischen Aspekten; Deutsches Ärzteblatt (2020) doi:10.3238/baek sn praezision 2020;
Jack Wilkinson et al.: Time to reality check the promises of machine learning-powered precision medicine. Lancet Digital Health 2020; doi:10.1016/S2589-7500(20)30200-4
Adam R. Schertz et al.: Sepsis Prediction Model for Determining Sepsis vs SIRS, qSOFA, and SOFA. In: JAMA Netw Open. Band6, Nr.8, 2023, doi:10.1001/jamanetworkopen.2023.29729.
Andrew Wong et al.: External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. In: JAMA Intern Med. Band181, Nr.8, 2021, S.1065–1070, doi:10.1001/jamainternmed.2021.2626.
Cheryl M. Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim: Prediction of psychosis across protocols and risk cohorts using automated language analysis. In: World Psychiatry. Band17, Nr.1, 19. Januar 2018, ISSN1723-8617, S.67–75, doi:10.1002/wps.20491, PMID 29352548.
C. J. Haug, J. M. Drazen, Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 N Engl J Med 2023;388:1201-8. DOI:10.1056/NEJMra2302038
Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med 2023; 388: 1233-9. DOI:10.1056/NEJMsr2214184 (Appendix)
Habicht J et al. : Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nature Medicine. (2024) DOI:10.1038/s41591-023-02766-x.
Ryan Han, Ioannidis J, Topol EJ. et al.: Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. In: Lancet Dig Health. Band6, Nr.5, Mai 2024, S.e367–e373, doi:10.1016/S2589-7500(24)00047-5.
Jesus Gomez Rossi et al.: Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and DiabeticRetinopathy In: JAMA Network Open. 2022;5(3) doi:10.1001/jamanetworkopen.2022.0269
UJ Muehlematter, P Daniore, KN Vokinger Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis Lancet Digital Health (2021) doi:10.1016/S2589-7500(20)30292-2, PMID 33478929
Jeffrey David Iqbal, Nikola Biller-Andorno: The regulatory gap in digital health and alternative pathways to bridge it. In: Health Policy and Technology. Band11, Nr.3, 1. September 2022, ISSN2211-8837, S.100663, doi:10.1016/j.hlpt.2022.100663.
Kevin Williams, Elizabeth Bilsland, Andrew Sparkes, Wayne Aubrey, Michael Young: Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. In: Journal of The Royal Society Interface. Band12, Nr.104, 6. März 2015, ISSN1742-5689, S.20141289, doi:10.1098/rsif.2014.1289, PMID 25652463.
Schultze J et al. (2021): Swarm Learning for decentralized and confidential clinical machine learning. Nature. doi:10.1038/s41586-021-03583-3
Kaissis G, Ziller A et al. (2021): End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence. doi:10.1038/s42256-021-00337-8
Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek: The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. In: Journal of Biomedical Informatics. Band113, Januar 2021, S.103655, doi:10.1016/j.jbi.2020.103655.
Boris Babic, Sara Gerke, Theodoros Evgeniou, I. Glenn Cohen: Beware explanations from AI in health care. In: Science. Band373, Nr.6552, 16. Juli 2021, ISSN0036-8075, S.284–286, doi:10.1126/science.abg1834.
NDR: Wie nützlich sind „Hautkrebs-Apps“? (ndr.de [abgerufen am 1. Dezember 2018]).
nih.gov
ncbi.nlm.nih.gov
P Tschandl, N Codella, BN Akay, G Argenziano, RP Braun, H Cabo, D Gutman, A Halpern, B Helba, R Hofmann-Wellenhof, A Lallas, J Lapins, C Longo, J Malvehy, MA Marchetti, A Marghoob, S Menzies, A Oakley, J Paoli, S Puig, C Rinner, C Rosendahl, A Scope, C Sinz, HP Soyer, L Thomas, I Zalaudek, H Kittler: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. In: The Lancet. Oncology. 20. Jahrgang, Nr.7, Juli 2019, S.938–947, doi:10.1016/S1470-2045(19)30333-X, PMID 31201137.
P Tschandl, C Rosendahl, H Kittler: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. In: Scientific data. 5. Jahrgang, 14. August 2018, S.180161, doi:10.1038/sdata.2018.161, PMID 30106392.
HA Haenssle, C Fink, R Schneiderbauer, F Toberer, T Buhl, A Blum, A Kalloo, ABH Hassen, L Thomas, A Enk, L Uhlmann, Groups. Reader study level-I and level-II: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. In: Annals of oncology: official journal of the European Society for Medical Oncology. 29. Jahrgang, Nr.8, 1. August 2018, S.1836–1842, doi:10.1093/annonc/mdy166, PMID 29846502.
P Tschandl, C Rosendahl, BN Akay, G Argenziano, A Blum, RP Braun, H Cabo, JY Gourhant, J Kreusch, A Lallas, J Lapins, A Marghoob, S Menzies, NM Neuber, J Paoli, HS Rabinovitz, C Rinner, A Scope, HP Soyer, C Sinz, L Thomas, I Zalaudek, H Kittler: Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. In: JAMA dermatology. 155. Jahrgang, Nr.1, 1. Januar 2019, S.58–65, doi:10.1001/jamadermatol.2018.4378, PMID 30484822.
MA Marchetti, NCF Codella, SW Dusza, DA Gutman, B Helba, A Kalloo, N Mishra, C Carrera, ME Celebi, JL DeFazio, N Jaimes, AA Marghoob, E Quigley, A Scope, O Yélamos, AC Halpern, Collaboration. International Skin Imaging: Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. In: Journal of the American Academy of Dermatology. 78. Jahrgang, Nr.2, Februar 2018, S.270–277.e1, doi:10.1016/j.jaad.2017.08.016, PMID 28969863.
HA Haenssle, C Fink, F Toberer, J Winkler, W Stolz, T Deinlein, R Hofmann-Wellenhof, A Lallas, S Emmert, T Buhl, M Zutt, A Blum, MS Abassi, L Thomas, I Tromme, P Tschandl, A Enk, A Rosenberger, Groups. Reader Study Level I and Level II: 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. In: Annals of oncology: official journal of the European Society for Medical Oncology. 31. Jahrgang, Nr.1, Januar 2020, S.137–143, doi:10.1016/j.annonc.2019.10.013, PMID 31912788.
P Tschandl, H Kittler, G Argenziano: A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions. In: The British journal of dermatology. 177. Jahrgang, Nr.3, September 2017, S.867–869, doi:10.1111/bjd.15695, PMID 28569993.
SS Han, MS Kim, W Lim, GH Park, I Park, SE Chang: Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. In: The Journal of investigative dermatology. 138. Jahrgang, Nr.7, Juli 2018, S.1529–1538, doi:10.1016/j.jid.2018.01.028, PMID 29428356.
C Yu, S Yang, W Kim, J Jung, KY Chung, SW Lee, B Oh: Acral melanoma detection using a convolutional neural network for dermoscopy images. In: PloS one. 13. Jahrgang, Nr.3, 2018, S.e0193321, doi:10.1371/journal.pone.0193321, PMID 29513718.
A Esteva, B Kuprel, RA Novoa, J Ko, SM Swetter, HM Blau, S Thrun: Dermatologist-level classification of skin cancer with deep neural networks. In: Nature. 542. Jahrgang, Nr.7639, 2. Februar 2017, S.115–118, doi:10.1038/nature21056, PMID 28117445.
S Polesie, M Gillstedt, H Kittler, A Lallas, P Tschandl, I Zalaudek, J Paoli: Attitudes towards artificial intelligence within dermatology: an international online survey. In: The British journal of dermatology. 17. Januar 2020, doi:10.1111/bjd.18875, PMID 31953854.
SS Han, I Park, W Lim, MS Kim, GH Park, JB Chae, CH Huh, SE Chang, JI Na: Augment Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. In: The Journal of investigative dermatology. 5. Februar 2020, doi:10.1016/j.jid.2020.01.019, PMID 32243882.
A Lallas, G Argenziano: Artificial intelligence and melanoma diagnosis: ignoring human nature may lead to false predictions. In: Dermatology practical & conceptual. 8. Jahrgang, Nr.4, Oktober 2018, S.249–251, doi:10.5826/dpc.0804a01, PMID 30479851.
C Navarrete-Dechent, SW Dusza, K Liopyris, AA Marghoob, AC Halpern, MA Marchetti: Automated Dermatological Diagnosis: Hype or Reality? In: The Journal of investigative dermatology. 138. Jahrgang, Nr.10, Oktober 2018, S.2277–2279, doi:10.1016/j.jid.2018.04.040, PMID 29864435.
A Ngoo, A Finnane, E McMeniman, JM Tan, M Janda, HP Soyer: Efficacy of smartphone applications in high-risk pigmented lesions. In: The Australasian journal of dermatology. 59. Jahrgang, Nr.3, August 2018, S.e175-e182, doi:10.1111/ajd.12599, PMID 28240347.
Pfau M, Walther G, von der Emde L, Berens P, Faes L, Fleckenstein M, Heeren TFC, Kortüm K, Künzel SH, Müller PL, Maloca PM, Waldstein SM, Wintergerst MWM, Schmitz-Valckenberg S, Finger RP, Holz FG: Artificial intelligence in ophthalmology: Guidelines for physicians for the critical evaluation of studies. In: Ophthalmologe. 117. Jahrgang, Nr.10, Oktober 2020, S.973–988, doi:10.1007/s00347-020-01209-z, PMID 32857270.
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. In: Nat Biomed Eng. 2. Jahrgang, Nr.3, März 2018, S.158–164, doi:10.1038/s41551-018-0195-0, PMID 31015713.
Dieck S, Ibarra M, Moghul I, Yeung MW, Pantel JT, Thiele S, Pfau M, Fleckenstein M, Pontikos N, Krawitz PM: Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals. In: Transl Vis Sci Technol. 9. Jahrgang, Nr.7, Juni 2020, S.8, doi:10.1167/tvst.9.7.8, PMID 32832215, PMC 7414790 (freier Volltext).
Cheryl M. Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim: Prediction of psychosis across protocols and risk cohorts using automated language analysis. In: World Psychiatry. Band17, Nr.1, 19. Januar 2018, ISSN1723-8617, S.67–75, doi:10.1002/wps.20491, PMID 29352548.
UJ Muehlematter, P Daniore, KN Vokinger Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis Lancet Digital Health (2021) doi:10.1016/S2589-7500(20)30292-2, PMID 33478929
Kevin Williams, Elizabeth Bilsland, Andrew Sparkes, Wayne Aubrey, Michael Young: Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. In: Journal of The Royal Society Interface. Band12, Nr.104, 6. März 2015, ISSN1742-5689, S.20141289, doi:10.1098/rsif.2014.1289, PMID 25652463.
Yiming Ding, Jae Ho Sohn, Michael G. Kawczynski, Hari Trivedi, Roy Harnish: A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. In: Radiology. 6. November 2018, ISSN0033-8419, S.180958, doi:10.1148/radiol.2018180958 (rsna.org [abgerufen am 1. Dezember 2018]).
Jeffrey David Iqbal, Rasita Vinay: Are we ready for Artificial Intelligence in Medicine? In: Swiss Medical Weekly. Nr.19, 19. Mai 2022, doi:10.4414/smw.2022.w30179 (smw.ch [abgerufen am 26. August 2022]).
Michael Moorstedt: Die blinden Flecken der künstlichen Intelligenzen. In: sueddeutsche.de. 11. August 2019, ISSN0174-4917 (sueddeutsche.de [abgerufen am 13. August 2019]).
Alanna Vial, David Stirling, Matthew Field, Montserrat Ros, Christian Ritz: The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. In: Translational Cancer Research. Band7, Nr.3, 6. Juli 2018, ISSN2219-6803, S.803–816, doi:10.21037/21823 (amegroups.com [abgerufen am 9. Dezember 2018]).
Yiming Ding, Jae Ho Sohn, Michael G. Kawczynski, Hari Trivedi, Roy Harnish: A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. In: Radiology. 6. November 2018, ISSN0033-8419, S.180958, doi:10.1148/radiol.2018180958 (rsna.org [abgerufen am 1. Dezember 2018]).
Cheryl M. Corcoran, Facundo Carrillo, Diego Fernández-Slezak, Gillinder Bedi, Casimir Klim: Prediction of psychosis across protocols and risk cohorts using automated language analysis. In: World Psychiatry. Band17, Nr.1, 19. Januar 2018, ISSN1723-8617, S.67–75, doi:10.1002/wps.20491, PMID 29352548.
Jeffrey David Iqbal, Nikola Biller-Andorno: The regulatory gap in digital health and alternative pathways to bridge it. In: Health Policy and Technology. Band11, Nr.3, 1. September 2022, ISSN2211-8837, S.100663, doi:10.1016/j.hlpt.2022.100663.
Kevin Williams, Elizabeth Bilsland, Andrew Sparkes, Wayne Aubrey, Michael Young: Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. In: Journal of The Royal Society Interface. Band12, Nr.104, 6. März 2015, ISSN1742-5689, S.20141289, doi:10.1098/rsif.2014.1289, PMID 25652463.
Michael Moorstedt: Die blinden Flecken der künstlichen Intelligenzen. In: sueddeutsche.de. 11. August 2019, ISSN0174-4917 (sueddeutsche.de [abgerufen am 13. August 2019]).
Boris Babic, Sara Gerke, Theodoros Evgeniou, I. Glenn Cohen: Beware explanations from AI in health care. In: Science. Band373, Nr.6552, 16. Juli 2021, ISSN0036-8075, S.284–286, doi:10.1126/science.abg1834.
