<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">JBM</journal-id><journal-title-group><journal-title>Journal of Biosciences and Medicines</journal-title></journal-title-group><issn pub-type="epub">2327-5081</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/jbm.2022.107009</article-id><article-id pub-id-type="publisher-id">JBM-118544</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biomedical&amp;Life Sciences</subject></subj-group></article-categories><title-group><article-title>
 
 
  The Usefulness of the Artificial Intelligence Data in Analyzing the Skin in the Era of the Fourth Industrial Revolution
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Se</surname><given-names>Ryong Kang</given-names></name><xref ref-type="aff" rid="aff1"><sub>1</sub></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>Department of Medical Beauty, Dongwon University, Gwangju, Korea</addr-line></aff><pub-date pub-type="epub"><day>01</day><month>07</month><year>2022</year></pub-date><volume>10</volume><issue>07</issue><fpage>114</fpage><lpage>122</lpage><history><date date-type="received"><day>1,</day>	<month>June</month>	<year>2022</year></date><date date-type="rev-recd"><day>15,</day>	<month>July</month>	<year>2022</year>	</date><date date-type="accepted"><day>18,</day>	<month>July</month>	<year>2022</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  In the World economy forum Global Challenge Insight Report titled “The Future of Jobs-Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (FIR) in 2016”, a new industrial revolution was predicted to occur in the near future. This is followed by the opinion that it would be mandatory to prepare for the FIR because it will definitely change people’s way of working, consuming and thinking. There is a controversy as to the potential of AI in health care. To date, however, remarkable achievements have been made in the field of medicine, particularly entailing dermatology. Therefore, this study explored the usefulness of the AI data in analyzing the skin in the era of the FIR. The current study finally included a total of 300 subjects, for whom a self-reporting questionnaire survey was performed between June 09 and July 18, 2020. The current study proposed the following hypothesis: The AI data might be useful in analyzing the skin in the era of the FIR. This hypothesis was accepted. In conclusion, the current study suggests that the AI data might be useful in analyzing the skin in the era of the FIR. But this deserves further study.
 
</p></abstract><kwd-group><kwd>Beauty</kwd><kwd> Skin</kwd><kwd> Artificial Intelligence</kwd><kwd> Fourth Industrial Revolution</kwd><kwd> Cosmetics</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>To date, progress in information and communication technology (ICT) has led to the advancement of informationalization [<xref ref-type="bibr" rid="scirp.118544-ref1">1</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref2">2</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref3">3</xref>]. Information and communication have accounted for a great part of industries and daily lives. They have therefore been considered a high priority [<xref ref-type="bibr" rid="scirp.118544-ref4">4</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref5">5</xref>].</p><p>In the World economy forum Global Challenge Insight Report titled “The Future of Jobs-Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (FIR) in 2016”, a new industrial revolution was predicted to occur in the near future. This is followed by the opinion that it would be mandatory to prepare for the FIR because it will definitely change people’s way of working, consuming and thinking [<xref ref-type="bibr" rid="scirp.118544-ref6">6</xref>].</p><p>The industrial revolutions have caused changes in the labor market; they are characterized by replacement of human labor with machines replacing human labor. In other words, the first, second and third industrial revolutions can be briefly described as the replacement of manual work with the invention of a steam engine, the emergence of electric energy enabling mass production and the initiation of an automation era based on the internet and computer [<xref ref-type="bibr" rid="scirp.118544-ref7">7</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref8">8</xref>]. This will be followed by the emergence of the FIR, whose characteristics include cyber-physical system and artificial intelligence (AI) [<xref ref-type="bibr" rid="scirp.118544-ref6">6</xref>].</p><p>Diverse factors are involved in the emergence of the FIR. The AI based on high-speed networks and interfaces would raise the speed of production and it would promote the big data-based business models [<xref ref-type="bibr" rid="scirp.118544-ref6">6</xref>]. Thus, the world has minimized differences in time and space between the countries with the development of ICT, thus contributing to creating a single economic system. This can be well verified by the social network services. It is also expected that operational technology or cyber-physical system devices will monitor, coordinate and integrate information on a real-time basis [<xref ref-type="bibr" rid="scirp.118544-ref6">6</xref>].</p><p>The FIR is defined as a combination of technologies that are available on the market from various fields [<xref ref-type="bibr" rid="scirp.118544-ref9">9</xref>]. Such technologies are accompanied by concurrent growth in the collection and availability of data on the shop-floor [<xref ref-type="bibr" rid="scirp.118544-ref10">10</xref>]. Therefore, their use in an enterprise is often associated with the intensive implementation of the ICT in the context of digitization of industrial processes, cyber-physical systems, Internet of Things (IoT), human-robot collaboration and real-time big-data processing capabilities [<xref ref-type="bibr" rid="scirp.118544-ref11">11</xref>].</p><p>There is a controversy as to the potential of AI in health care [<xref ref-type="bibr" rid="scirp.118544-ref12">12</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref14">14</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref15">15</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref16">16</xref>]. To date, however, remarkable achievements have been made in the field of medicine, particularly entailing dermatology [<xref ref-type="bibr" rid="scirp.118544-ref17">17</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref18">18</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref19">19</xref>].</p><p>Given the above background, this study explored the usefulness of the AI data in analyzing the skin in the era of the FIR.</p></sec><sec id="s2"><title>2. Materials and Methods</title><sec id="s2_1"><title>2.1. Data Collection</title><p>The current study included a total of 321 subjects (n = 321) who were located in Seoul, Korea, for whom a self-reporting questionnaire survey was performed between June 09 and July 18, 2020. They were given questionnaire sheets using a convenience sampling method. Eligibility criterion for the current study is the experience of observing other customers’ complaint behavior. The subjects were informed of purposes and implications of the current study. Before study participation, they provided a verbal consent.</p><p>The self-reporting questionnaire survey was performed with the help of a research company, whose results were assessed based on a 5-point Likert scale (0 = “Never” and 4 = “Always”). Thus, a total of 12 questions, including five about demographic characteristics of the subjects, were prepared.</p><p>After the exclusion of 21 incomplete responses, a total of 300 valid responses (n = 300) were finally analyzed.</p></sec><sec id="s2_2"><title>2.2. Hypothesis Testing</title><p>The current study proposed the following hypothesis:</p><p>H: The AI data might be useful in analyzing the skin in the era of the FIR.</p></sec><sec id="s2_3"><title>2.3. Statistical analysis of the Data</title><p>Data was presented as mean &#177; standard deviation or number with percentage, where appropriate. The SPSS for windows Ver. 18.0 (SPSS Inc., Chicago, IL) and AMOS Ver. 18.0 (IBM Co., Armonk, NY) were used to analyze the data. A P-value of &lt;0.05 was considered statistically significant.</p><p>Standardized factor loading was calculated, and the data-model fit was evaluated based on the χ<sup>2</sup>-goodness-of-fit indices (GFI) and adjusted goodness-of- fit indices (AGFI). Based on a structural equation model, the degree of freedom (df), the number of variables that will be estimated (Q), root mean square residual (RMR), root-mean-square error approximation (RMSEA), normed fit index (NFI) and comparative fit index (CFI) were calculated. Moreover, unstandardized coefficient, factor loading, statistical significance, squared multiple correlations (SMCs), average variance extracted (AVE) and confidence were calculated.</p></sec></sec><sec id="s3"><title>3. Results</title><sec id="s3_1"><title>3.1. Baseline Characteristics of the Subjects</title><p>The subjects are composed of 133 men (44.3%) and 167 women (55.7%) (<xref ref-type="fig" rid="fig1">Figure 1</xref>). By the age, the subjects aged between 30 and 39 years old were the most prevalent (148/300, 49.3%) (<xref ref-type="fig" rid="fig2">Figure 2</xref>). By the type of employment, self-employed subjects or employees of a corporation were also the most prevalent (123/300, 41.0%) (<xref ref-type="fig" rid="fig3">Figure 3</xref>). By the monthly income, the subjects with a monthly income of USD 1848.89 - 2773.34 were also the most prevalent (95/300, 31.7%) (<xref ref-type="fig" rid="fig4">Figure 4</xref>). Baseline characteristics of the subjects are presented in <xref ref-type="table" rid="table1">Table 1</xref>.</p></sec><sec id="s3_2"><title>3.2. Confirmatory Factor Analysis</title><p>The goodness-of-fit was satisfactory (χ<sup>2</sup> = 102.407, df = 48, P = 0.000, RMR = 0.035, GFI = 0.944, AGFI = 0.909, CFI = 0.970, NFI = 0.946, RFI = 0.926, IFI = 0.971 and RMSEA = 0.062). Moreover, the standardized factor loading exceeded the critical level of 0.5 and the concept reliability did that of 0.7, both of which were found to be statistically significant.</p></sec><sec id="s3_3"><title>3.3. Hypothesis Testing</title><p>Results of hypothesis testing are presented in <xref ref-type="table" rid="table2">Table 2</xref>; the current study accepted</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Baseline characteristics of the subjects (n = 300)</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  colspan="2"  >Variables</th><th align="center" valign="middle" >Values</th></tr></thead><tr><td align="center" valign="middle"  colspan="3"  >Age</td></tr><tr><td align="center" valign="middle"  rowspan="4"  ></td><td align="center" valign="middle" >20 - 29 years old</td><td align="center" valign="middle" >77 (25.7%)</td></tr><tr><td align="center" valign="middle" >30 - 39 years old</td><td align="center" valign="middle" >148 (49.3%)</td></tr><tr><td align="center" valign="middle" >40 - 49 years old</td><td align="center" valign="middle" >52 (17.3%)</td></tr><tr><td align="center" valign="middle" >&#179;50 years old</td><td align="center" valign="middle" >23 (7.7%)</td></tr><tr><td align="center" valign="middle"  colspan="3"  >Sex</td></tr><tr><td align="center" valign="middle"  rowspan="2"  ></td><td align="center" valign="middle" >Men</td><td align="center" valign="middle" >133 (44.3%)</td></tr><tr><td align="center" valign="middle" >Women</td><td align="center" valign="middle" >167 (55.7%)</td></tr><tr><td align="center" valign="middle"  colspan="3"  >Occupation</td></tr><tr><td align="center" valign="middle"  rowspan="5"  ></td><td align="center" valign="middle" >Self-employment or employees of a corporation</td><td align="center" valign="middle" >123 (41.0%)</td></tr><tr><td align="center" valign="middle" >Profession</td><td align="center" valign="middle" >55 (18.3%)</td></tr><tr><td align="center" valign="middle" >Undergraduate or graduate student</td><td align="center" valign="middle" >20 (6.7%)</td></tr><tr><td align="center" valign="middle" >Housewife</td><td align="center" valign="middle" >30 (10.0%)</td></tr><tr><td align="center" valign="middle" >Others</td><td align="center" valign="middle" >72 (24.0%)</td></tr><tr><td align="center" valign="middle"  colspan="3"  >Monthly income</td></tr><tr><td align="center" valign="middle"  rowspan="6"  ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >30 (10.0%)</td></tr><tr><td align="center" valign="middle" >USD 924.45 - 1848.89</td><td align="center" valign="middle" >82 (27.3%)</td></tr><tr><td align="center" valign="middle" >USD 1848.89 - 2773.34</td><td align="center" valign="middle" >95 (31.7%)</td></tr><tr><td align="center" valign="middle" >USD 2773.34 - 3697.78</td><td align="center" valign="middle" >41 (13.7%)</td></tr><tr><td align="center" valign="middle" >USD 3697.78 - 4622.23</td><td align="center" valign="middle" >24 (8.0%)</td></tr><tr><td align="center" valign="middle" >&gt;USD 4622.23</td><td align="center" valign="middle" >28 (9.3%)</td></tr></tbody></table></table-wrap><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Hypothesis testing</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Hypothesis</th><th align="center" valign="middle" >β</th><th align="center" valign="middle" >T</th><th align="center" valign="middle" >P</th><th align="center" valign="middle" >Results</th></tr></thead><tr><td align="center" valign="middle" >The AI data might be useful in analyzing the skin in the era of the FIR.</td><td align="center" valign="middle" >0.382</td><td align="center" valign="middle" >5.760***</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >Accept</td></tr></tbody></table></table-wrap><p>Abbreviations: AI, artificial intelligence; FIR, fourth industrial revolution. Model’s goodness-of-fit: χ<sup>2</sup> = 102.407, df = 48, P = 0.000, RMR = 0.035, GFI = 0.944, AGFI = 0.909, CFI = 0.970, NFI = 0.946, RFI = 0.926, IFI = 0.971, RMSEA = 0.062.</p><p>the hypothesis “The AI data might be useful in analyzing the skin in the era of the FIR”.</p></sec></sec><sec id="s4"><title>4. Discussion</title><p>Dermatology is a specialty area that undergoes involvement of digitalization, telehealth and informatics [<xref ref-type="bibr" rid="scirp.118544-ref20">20</xref>]. This is accompanied by the increased use of the AI, which has led to its application to the dermatological practice [<xref ref-type="bibr" rid="scirp.118544-ref21">21</xref>].</p><p>Dermatological practice undergoes rapid evolution; there have been considerable changes in the diagnosis and treatment of dermatological conditions thanks to the emergence of new technology. Computer algorithms and the AI are helpful tools for dermatologists. Moreover, machine learning is also a useful tool in that it induces computer programs to learn automatically based on experience without explicit programming instructions [<xref ref-type="bibr" rid="scirp.118544-ref22">22</xref>] [<xref ref-type="bibr" rid="scirp.118544-ref23">23</xref>]. A complete understanding of the AI would therefore be mandatory.</p><p>The current study showed that the AI data might be useful in analyzing the skin in the era of the FIR. This is in agreement with a previous published study; according to a recent study, 85.1% of 1271 dermatologists were aware that the AI would play a pivotal role in making an accurate diagnosis of skin conditions [<xref ref-type="bibr" rid="scirp.118544-ref24">24</xref>].</p><p>Still, however, there are limitations of the applicability of the AI to dermatological practice, as follows: First, there is an insufficient amount of high-quality image data about diverse skin diseases. Second, the AI can be applied to dermatological practice only in a limited scope, although there is a great variability in the type of skin condition. Third, the AI-based diagnosis of dermatological diseases may cause legal and ethical issues in association with the data privacy [<xref ref-type="bibr" rid="scirp.118544-ref25">25</xref>].</p></sec><sec id="s5"><title>5. Conclusions</title><p>Skin diagnosis is currently performed for diagnostic, therapeutic and cosmetic purposes. With evolutionary changes in online market and personalized beauty services, there has been a continuous increase in demand for an easy, accurate method of skin diagnosis. In particular, an image analysis technology has been applied to skin diagnosis; its advantages include cost-effectiveness and high accessibility. Therefore, it is efficiently combined with the AI to provide customers with more personalized services. Recently, the AI has been of increasing interest; it is actively used to develop cosmetics and to recommend personalized cosmetics to customers. It is also expected that a deep learning-based automatic skin diagnosis technology will also be available for stakeholders in the cosmetic industry. Thus, it has greatly contributed to opening the era of beauty technology as a new paradigm in response to the demand of the FIR.</p><p>In conclusion, the current study suggests that the AI data might be useful in analyzing the skin in the era of the FIR. But this deserves further study.</p></sec><sec id="s6"><title>Conflicts of Interest</title><p>The author declares no conflicts of interest regarding the publication of this paper.</p></sec><sec id="s7"><title>Cite this paper</title><p>Kang, S.R. (2022) The Usefulness of the Artificial Intelligence Data in Analyzing the Skin in the Era of the Fourth Industrial Revolution. Journal of Biosciences and Medicines, 10, 114-122. https://doi.org/10.4236/jbm.2022.107009</p></sec></body><back><ref-list><title>References</title><ref id="scirp.118544-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Thangavel, G., Memedi, M. and Hedstrom, K. (2022) Customized Information and Communication Technology for Reducing Social Isolation and Loneliness Among Older Adults: Scoping Review. JMIR Mental Health 9, e34221. https://doi.org/10.2196/34221</mixed-citation></ref><ref id="scirp.118544-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Chen, Y.R. and Schulz, P.J. (2016) The Effect of Information Communication Technology Interventions on Reducing Social Isolation in the Elderly: A Systematic Review. Journal of Medical Internet Research, 18, e18. https://doi.org/10.2196/jmir.4596</mixed-citation></ref><ref id="scirp.118544-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Gual-Montolio, P., Martínez-Borba, V., Bretón-López, J.M., Osma, J. and Suso-Ribera, C. (2020) How Are Information and Communication Technologies Supporting Routine Outcome Monitoring and Measurement-Based Care in Psychotherapy? A Systematic Review. International Journal of Environmental Research and Public Health, 17, Article 3170. https://doi.org/10.3390/ijerph17093170</mixed-citation></ref><ref id="scirp.118544-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Yang, S., Fichman, P., Zhu, X., Sanfilippo, M., Li, S. and Fleischmann, K.R. (2020) The Use of ICT during COVID-19. Proceedings of the Association for Information Science and Technology, 57, e297. https://doi.org/10.1002/pra2.297</mixed-citation></ref><ref id="scirp.118544-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Schlomann, A., Seifert, A., Zank, S., Woopen, C. and Rietz, C. (2020) Use of Information and Communication Technology (ICT) Devices among the Oldest-Old: Loneliness, Anomie, and Autonomy. Innovation in Aging, 4, igz050. https://doi.org/10.1093/geroni/igz050</mixed-citation></ref><ref id="scirp.118544-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Min, J., Kim, Y., Lee, S., Jang, T.W., Kim, I. and Song, J. (2019) The Fourth Industrial Revolution and Its Impact on Occupational Health and Safety, Worker’s Compensation and Labor Conditions. Safety and Health at Work, 10, 400-408. https://doi.org/10.1016/j.shaw.2019.09.005</mixed-citation></ref><ref id="scirp.118544-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Sima, V., Gheorghe, I.G., Subic, J. and Nancu, D. (2020) Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review. Sustainability, 12, Article 4035. https://doi.org/10.3390/su12104035</mixed-citation></ref><ref id="scirp.118544-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Lee, M., Yun, J.J., Pyka, A., Won, D., et al. (2018) How to Respond to the Fourth Industrial Revolution, or the Second Information Technology Revolution? Dynamic New Combinations between Technology, Market, and Society through Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 4, Article 21. https://doi.org/10.3390/joitmc4030021</mixed-citation></ref><ref id="scirp.118544-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Parente, M., Figueira, G., Amorim, P. and Marques, A. (2020) Production Scheduling in the Context of Industry 4.0: Review and Trends. International Journal of Production Research, 58, 5401-5431. https://doi.org/10.1080/00207543.2020.1718794</mixed-citation></ref><ref id="scirp.118544-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Baldea, M. and Harjunkoski, I. (2014) Integrated Production Scheduling and Process Control: A Systematic Review. Computers &amp; Chemical Engineering, 71, 377-390. https://doi.org/10.1016/j.compchemeng.2014.09.002</mixed-citation></ref><ref id="scirp.118544-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Xu, L.D., Xu, E.L. and Li, L. (2018) Industry 4.0: State of the Art and Future Trends. International Journal of Production Research, 56, 2941-2962. https://doi.org/10.1080/00207543.2018.1444806</mixed-citation></ref><ref id="scirp.118544-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Hinton, G. (2018) Deep Learning—A Technology with the Potential to Transform Health Care. JAMA, 320, 1101-1102. https://doi.org/10.1001/jama.2018.11100</mixed-citation></ref><ref id="scirp.118544-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Mar, V.J. and Soyer, H.P. (2019) Artificial Intelligence for Melanoma Diagnosis: How can We Deliver on the Promise? Annals of Oncology, 30, e1-e3. https://doi.org/10.1093/annonc/mdy191</mixed-citation></ref><ref id="scirp.118544-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Naylor, C.D. (2018) On the Prospects for a (Deep) Learning Health Care System. JAMA, 320, 1099-1100. https://doi.org/10.1001/jama.2018.11103</mixed-citation></ref><ref id="scirp.118544-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Stead, W.W. (2018) Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA, 320, 1107-1108. https://doi.org/10.1001/jama.2018.11029</mixed-citation></ref><ref id="scirp.118544-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Zakhem, G.A., Motosko, C.C. and Ho, R.S. (2018) How should Artificial Intelligence Screen for Skin Cancer and Deliver Diagnostic Predictions to Patients? American Academy of Dermatology, 154, 1383-1384. https://doi.org/10.1001/jamadermatol.2018.2714</mixed-citation></ref><ref id="scirp.118544-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056</mixed-citation></ref><ref id="scirp.118544-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Haenssle, H.A., Fink, C., Schneiderbauer, R., et al. (2018) Man against Machine: Diagnostic Performance of a Deep Learning Convolutional Neural Network for Dermoscopic Melanoma Recognition in Comparison to 58 Dermatologists. Annals of Oncology, 29, 1836-1842. https://doi.org/10.1093/annonc/mdy166</mixed-citation></ref><ref id="scirp.118544-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">Han, S.S., Kim, M.S., Lim, W., Park, G.H., Park, I. and Chang, S.E. (2018) Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. Journal of Investigative Dermatology, 138, 1529-1538. https://doi.org/10.1016/j.jid.2018.01.028</mixed-citation></ref><ref id="scirp.118544-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">Han, S.S, Park, G.H., Lim, W., et al. (2018) Deep Neural Networks Show an Equivalent and often Superior Performance to Dermatologists in Onychomycosis Diagnosis: Automatic Construction of Onychomycosis Datasets by Region-Based Convolutional Deep Neural Network. PLOS ONE, 13, e0191493. https://doi.org/10.1371/journal.pone.0191493</mixed-citation></ref><ref id="scirp.118544-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">El-Azhary, R.A. (2019) The Inevitability of Change. Clinics in Dermatology, 37, 4-11. https://doi.org/10.1016/j.clindermatol.2018.09.003</mixed-citation></ref><ref id="scirp.118544-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. and Wang, Y. (2017) Artificial Intelligence in Healthcare: Past, Present and Future. Stroke and Vascular Neurology, 2, 230-243. https://doi.org/10.1136/svn-2017-000101</mixed-citation></ref><ref id="scirp.118544-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M. and Thrun, S. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056</mixed-citation></ref><ref id="scirp.118544-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Polesie, S., Gillstedt, M., Kittler, H., Lallas, A., Tschandl, P., Zalaudek, I. and Paoli, J. (2020) Attitudes towards Artificial Intelligence within Dermatology: An International Online Survey. British Journal of Dermatology, 183, 159-161. https://doi.org/10.1111/bjd.18875</mixed-citation></ref><ref id="scirp.118544-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">De, A., Sarda, A., Gupta, S. and Das, S. (2020) Use of Artificial Intelligence in Dermatology. Indian Journal of Dermatology, 65, 352-357. https://doi.org/10.4103/ijd.IJD_418_20</mixed-citation></ref></ref-list></back></article>