Enhancing patient education and engagement through digital intelligence tools in dermatology

Grace Herrick; Kelly Frasier; Vivian Li; Haily Fritts; Emily Woolhiser; Julia Vinagolu-Baur

doi:10.18203/issn.2455-4529.IntJResDermatol20243342

Authors

Grace Herrick Department of Medical Education, Alabama College of Osteopathic Medicine, Dothan, AL, US
Kelly Frasier Department of Dermatology, Northwell Health, New Hyde Park, NY, US
Vivian Li Department of Medicine, Nuvance Health Vassar Brothers Medical Centre, Poughkeepsie, NY, US
Haily Fritts Department of Medical Education, Idaho College of Osteopathic Medicine, Meridian, ID, US
Emily Woolhiser Department of Medical Education, Kansas City University College of Osteopathic Medicine, Kansas City, MO, US
Julia Vinagolu-Baur Department of Medical Education, SUNY Upstate Medical University, Syracuse, NY, US

DOI:

https://doi.org/10.18203/issn.2455-4529.IntJResDermatol20243342

Keywords:

Digital intelligence tools, Dermatology, AI-powered applications, Personalized skincare, Tele dermatology, Wearable technology, Skin health management

Abstract

This comprehensive digital health review examines the expanding landscape of digital intelligence tools within dermatology, specifically examining the innovative role of AI-powered applications aimed at educating and engaging patients in managing their skin health. Drawing upon a wide array of existing research, this review scrutinizes the evolution and impact of various digital tools. These include interactive apps that leverage AI algorithms to deliver tailored skincare guidance, considering factors such as skin type, concerns, and environmental factors. Additionally, the review explores the emerging use of immersive virtual reality experiences to deepen patients’ understanding of skin conditions, offering vivid simulations and educational content. These innovative approaches showcase significant promise in not only improving patient outcomes but also fostering greater treatment adherence and overall satisfaction with dermatological care. Looking ahead, future research should prioritize fine-tuning AI algorithms to further personalize patient recommendations, exploring the integration of wearable technology for real-time monitoring, and conducting longitudinal studies to evaluate the sustained effectiveness and scalability of digital intelligence tools in promoting patient education and engagement. Moreover, innovative strategies such as gamification techniques and social support platforms hold considerable potential for enhancing patient empowerment and involvement in managing their skin health. Leveraging digital intelligence tools will eventually redefine the landscape of dermatological care, empowering patients and optimizing treatment outcomes through enhanced education and engagement.

Metrics

Metrics Loading ...

References

Hewitt RM, Bundy C, Newi AL. How do dermatologists' personal models inform a patient-centred approach to management: a qualitative study using the example of prescribing a new treatment (Apremilast). Br J Dermatol. 2022;187(1):82-8.

Sanga P, Singh J, Dubey AK, Khanna NN, Laird JR, Faa G. Derm AI 1.0: A robust, generalized, and novel attention-enabled ensemble-based transfer learning paradigm for multiclass classification of skin lesion images. Diagnostics. 2023; 13(19):3159.

Freeman K, Dinnes J, Chuchu N. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. 2020;368:645.

Craig CM, Stafford J, Egorova A, McCabe C, Matthews M. Can We Use the Oculus Quest VR Headset and Controllers to Reliably Assess Balance Stability? Diagnost. 2022;12(6):1409.

Neloy AA, Alam S, Bindu RA, Moni NJ. Machine learning based health prediction system using IBM cloud as PAAS. 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 2019;444-50.

Bokolo AJ. Exploring the adoption of telemedicine and virtual software for care of outpatients during and after COVID-19 pandemic. Ir J Med Sci. 2021;190(1):1-10.

Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl). 2017;7(4):351-67.

Lungu AJ, Swinkels W, Claesen L, Tu P, Egger J, Chen X. A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery. Expert Rev Med Devices. 2021;18(1):47-62.

Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2:10.

Baumann, L. Validation of a questionnaire to diagnose the Baumann skin type in all ethnicities and in various geographic locations. Journal of Cosmetics, Dermatological Sciences and Applications. 2016;6(1):34–40.

Jaiswal SK, Agarwal SM, Thodum P, Sharma VK. SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics. iScience. 2020;24(1):101925. Published 2020 Dec 10. https://doi.org/10.1016/j.isci.2020.101925

Marri SS, Inamadar AC, Janagond AB, Albadri W. Analysing the predictability of an artificial intelligence app (TIBOT) in the diagnosis of dermatological conditions: a cross-sectional study. JMIR Dermatol. 2023;6:45529.

Amici JM, Cogrel O, Jourdan M. Expert recommendations on supportive skin care for non-surgical and surgical procedures. J Eur Acad Dermatol Venereol. 2023;37(3):16-33.

Sun JY, Sun Y. An intelligent mobile platform to assist customized cosmetic selection using artificial intelligence and natural language processing. Computer science and information technology (CS and IT). Tamil Nadu, India: AIRCC Publishing Corporation. 2022:159-67.

Smak Gregoor AM, Sangers TE, Bakker LJ. An artificial intelligence-based app for skin cancer detection evaluated in a population-based setting. NPJ Digit Med. 2023;6(1):90.

Winkler JK, Kommoss KS, Toberer F. Performance of an automated total body mapping algorithm to detect melanocytic lesions of clinical relevance. Eur J Cancer. 2024;202:114026.

Bruno RR, Wolff G, Wernly B. Virtual and augmented reality in critical care medicine: the patient's, clinician's, and researcher's perspective. Crit Care. 2022;26(1):326.

Obagi ZA, Rundle CW, Dellavalle RP. Widening the scope of virtual reality and augmented reality in dermatology. Dermatol Online J. 2020;26(1):13030.

Yang J, Rhu J, Lim S. Impact of virtual reality education on disease-specific knowledge and anxiety for hepatocellular carcinoma patient scheduled for liver resection: a randomized controlled study. Int J Surg. 2024;110(5):2810-7.

Yousif J, Alkhouri F, St Claire K, Potts G. A practical guide to reducing preoperative and intraoperative patient anxiety in dermatologic surgery. Int J Dermatol. 2023;62(9):1154-9.

Martin D. Pre-operative visits to reduce patient anxiety: a study. Nurs Stand. 1996;10(23):33-8.

Higgins S, Feinstein S, Hawkins M, Cockburn M, Wysong A. Virtual Reality to Improve the Experience of the Mohs Patient-A Prospective Interventional Study. Dermatol Surg. 2019;45(8):1009-18.

Baschong A, Spiess F, Cattin PC, Navarini A, Mueller SM. Itch reduction using immersive virtual reality-An experimental pilot study. Dermatol Ther. 2021;34(4):15001.

Pathoulas JT, Flanagan KE, Walker CJ, et al. Use of virtual reality during scalp injections: A randomized interventional crossover study. J Am Acad Dermatol. 2022;87(5):1091-3.

Kyaw B, Saxena N, Posadzki P. Virtual Reality for Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration. J Med Internet Res. 2019;21(1):12959.

Prado G, Kovarik C. Cutting edge technology in dermatology: virtual reality and artificial intelligence. Cutis. 2018;101(4):236-7.

Kamińska D, Sapiński T, Wiak S, Tikk T, Haamer RE, Avots E, et al. virtual reality and its applications in education: survey. Information. 2019;10(10):318.

van der Kruk SR, Zielinski R, MacDougall H, Hughes-Barton D, Gunn KM. Virtual reality as a patient education tool in healthcare: A scoping review. Patient Educ Couns. 2022;105(7):1928-42.

Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520-6.

Hoffman HG, Boe DA, Rombokas E, et al. Virtual reality hand therapy: A new tool for nonopioid analgesia for acute procedural pain, hand rehabilitation, and VR embodiment therapy for phantom limb pain. J Hand Ther. 2020;33(2):254-62.

Berton A, Longo UG, Candela V. Virtual reality, augmented reality, gamification, and telerehabilitation: psychological impact on orthopedic patients' rehabilitation. J Clin Med. 2020;9(8):2567.

Martin LR, Williams SL, Haskard KB, Dimatteo MR. The challenge of patient adherence. Ther Clin Risk Manag. 2005;1(3):189-99.

Hodari KT, Nanton JR, Carroll CL, Feldman SR, Balkrishnan R. Adherence in dermatology: a review of the last 20 years. J Dermatolog Treat. 2006;17(3):136-42.

Bergerum C, Thor J, Josefsson K, Wolmesjö M. How might patient involvement in healthcare quality improvement efforts work-A realist literature review. Health Expect. 2019;22(5):952-64.

Veroff D, Marr A, Wennberg DE. Enhanced support for shared decision-making reduced costs of care for patients with preference-sensitive conditions. Health Aff (Millwood). 2013;32(2):285-93.

Armstrong N, Herbert G, Aveling EL, Dixon-Woods M, Martin G. Optimizing patient involvement in quality improvement. Health Expect. 2013;16(3):36-47.

Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. J Med Internet Res. 2023;25:40789.

Georgievskaya A, Danko D, Baxter R, Corstjens H, Tlyachev T. Artificial intelligence approaches for skin anti-aging and skin resilience research. 2023:189-214.

Afvari S, Beck TC, Kazlouskaya M, Afrahim R, Valdebran M. Diet, sleep, and exercise in inflammatory skin diseases. Our Dermatol Online. 2023;14(4):430-5.

Zhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial intelligence chatbot behaviour change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet: viewpoint. J Med Internet Res. 2020;22(9):22845.

Elwyn G, Ryan P, Blumkin D, Weeks WB. Meet generative AI your new shared decision-making assistant. BMJ Evid Based Med. 2024;29(5):292-5.

Szeto MD, Strock D, Anderson J. Gamification and Game-Based Strategies for Dermatology Education: Narrative Review. JMIR Dermatol. 2021;4(2):30325.

Aponso S, Tan YTN, Jain S, Oh CC. Gamification of dermatoscopy education using a smartphone mobile platform: A pilot study. JAAD Int. 2024;16:91-6.

Johnson KB, Wei WQ, Weeraratne D. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14(1):86-93.

Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. J Med Artif Intell. 2023;6:23-47

Mikal JP. Where form meets function: the impact of social media platform characteristics on access to online social support. Univer Access Inform Soc. 2022;21(4):1023-6.

Sanders R, Linn AJ, Araujo TB, Vliegenthart R, Van Eenbergen MC, Van Weert JC. Different platforms for different patients’ need: Automatic content analysis of different online health information platforms. Inter J Human-Comp Stud. 2020;137:102386.

Eldh A, Sverker A, Bendtsen P, Nilsson E. Health care professionals’ experience of a digital tool for patient exchange, anamnesis, and triage in primary care: qualitative study. JMIR Hum Factors. 2020;7(4):21698.

Maymone MB, Du T, Dellavalle RP. Healthcare and dermatology on WeChat. Dermatol Online J. 2019;25(4):13030.

Hossain SN, Jaglal SB, Shepherd J. Web-based peer support interventions for adults living with chronic conditions: scoping review. JMIR. 2021;8(2):14321.

Cortés H, Rojas-Márquez M, Del Prado-Audelo ML, Reyes-Hernández OD, González-Del Carmen M, Leyva-Gómez G. Alterations in mental health and quality of life in patients with skin disorders: a narrative review. Int J Dermatol. 2022;61(7):783-91.