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FENNTARTHATÓSÁGI ÁTMENET:
KIHÍVÁSOK ÉS INNOVATÍV MEGOLDÁSOK
SUSTAINABILITY TRANSITIONS: CHALLENGES AND INNOVATIVE SOLUTIONS
Szerkesztők / Editors:
OBÁDOVICS Csilla, RESPERGER Richárd, SZÉLES Zsuzsanna, TÓTH Balázs István
3. sz. melléklet: Egyetemi Kiadó kiadvá-
nyon feltüntetendő adatok
(nyomtatott/elektronikus)
(kötéstábla/borítófedél)
KONFERENCIAKÖTET
Conference Proceedings
Nemzetközi tudományos konferencia
a Magyar Tudomány Ünnepe alkalmából
International Scientific Conference
on the Occasion of the Hungarian Science Festival
Sopron, 2023. november 23.
23 November 2023, Sopron
Nemzetközi tudományos konferencia a Magyar Tudomány Ünnepe alkalmából
International Scientific Conference on the Occasion of the Hungarian Science Festival
Sopron, 2023. november 23. / 23 November 2023, Sopron
FENNTARTHATÓSÁGI ÁTMENET:
KIHÍVÁSOK ÉS INNOVATÍV MEGOLDÁSOK
SUSTAINABILITY TRANSITIONS:
CHALLENGES AND INNOVATIVE SOLUTIONS
KONFERENCIAKÖTET
CONFERENCE PROCEEDINGS
LEKTORÁLT TANULMÁNYOK / PEER-REVIEWED PAPERS
Szerkesztők / Editors:
OBÁDOVICS Csilla – RESPERGER Richárd – SZÉLES Zsuzsanna – TÓTH Balázs István
SOPRONI EGYETEM KIADÓ
UNIVERSITY OF SOPRON PRESS
SOPRON, 2024
Nemzetközi tudományos konferencia a Magyar Tudomány Ünnepe alkalmából
International Scientific Conference on the Occasion of the Hungarian Science Festival
Sopron, 2023. november 23. / 23 November 2023, Sopron
A konferencia támogatói / Sponsors of the Conference:
Felelős kiadó / Executive Publisher: Prof. Dr. FÁBIÁN Attila
a Soproni Egyetem rektora / Rector of the University of Sopron
Szerkesztők / Editors:
Prof. Dr. OBÁDOVICS Csilla, Dr. RESPERGER Richárd,
Prof. Dr. SZÉLES Zsuzsanna, Dr. habil. TÓTH Balázs István
Lektorok / Reviewers:
Dr. habil. BARANYI Aranka, Prof. Dr. BÁRTFAI Zoltán, Dr. BARTÓK István, Dr. BEDNÁRIK Éva,
Bazsóné Dr. BERTALAN Laura, Dr. CZIRÁKI Gábor, Dr. DIÓSSI Katalin, Dr. habil. JANKÓ Ferenc,
Dr. KERESZTES Gábor, Dr. habil. KOLOSZÁR László, Dr. KÓPHÁZI Andrea,
Prof. Dr. KULCSÁR László, Dr. MÉSZÁROS Katalin, Dr. NEDELKA Erzsébet,
Dr. NÉMETH Nikoletta, Dr. NÉMETH Patrícia, Prof. Dr. OBÁDOVICS Csilla,
Dr. PALANCSA Attila, Dr. habil. PAPP-VÁRY Árpád Ferenc, Dr. RESPERGER Richárd,
Dr. habil. SZABÓ Zoltán, Prof. Dr. SZÉLES Zsuzsanna, Dr. SZÓKA Károly, Dr. TAKÁTS Alexandra,
Dr. habil. TÓTH Balázs István, Pappné Dr. VANCSÓ Judit
ISBN 978-963-334-499-6 (pdf)
DOI: 10.35511/978-963-334-499-6
Creative Commons license: CC BY-NC-SA 4.0 DEED
Nevezd meg! - Ne add el! - Így add tovább! 4.0 Nemzetközi
Attribution-NonCommercial-ShareAlike 4.0 International
4
SZERVEZŐK
Soproni Egyetem Lámfalussy Sándor Közgazdaságtudományi Kar (SOE LKK),
A Soproni Felsőoktatásért Alapítvány
A konferencia elnöke: Prof. Dr. SZÉLES Zsuzsanna egyetemi tanár, dékán (SOE LKK)
Tudományos Bizottság:
elnök: Prof. Dr. OBÁDOVICS Csilla PhD egyetemi tanár, Doktori Iskola-vezető (SOE LKK)
társelnök: Dr. habil. TÓTH Balázs István PhD egyetemi docens, igazgató (SOE LKK)
tagok: Prof. Dr. FÁBIÁN Attila PhD egyetemi tanár (SOE LKK), rektor (SOE)
Prof. Dr. SZÉKELY Csaba DSc professor emeritus (SOE LKK)
Prof. Dr. KULCSÁR László CSc professor emeritus (SOE LKK)
Prof. Dr. SZALAY László DSc egyetemi tanár (SOE LKK)
Prof. Dr. Clemens JÄGER PhD egyetemi tanár, dékán (FOM)
Dr. habil. BARANYI Aranka PhD egyetemi docens (SOE LKK)
Dr. habil. POGÁTSA Zoltán PhD egyetemi docens (SOE LKK)
Dr. habil. SZABÓ Zoltán PhD egyetemi docens (SOE LKK)
Dr. habil. PAPP-VÁRY Árpád Ferenc PhD tudományos főmunkatárs (SOE LKK)
Dr. Rudolf KUCHARČÍK PhD egyetemi docens, dékán (EUBA FIR)
Szervező Bizottság:
elnök: Dr. RESPERGER Richárd PhD adjunktus (SOE LKK)
tagok: Dr. KERESZTES Gábor PhD egyetemi docens, dékánhelyettes (SOE LKK)
Dr. habil. Eva JANČÍKOVÁ PhD egyetemi docens (EUBA FIR)
Dr. habil. KOLOSZÁR László PhD egyetemi docens, intézetigazgató (SOE LKK)
Dr. HOSCHEK Mónika PhD egyetemi docens, intézetigazgató (SOE LKK)
PAPPNÉ Dr. VANCSÓ Judit PhD egyetemi docens, intézetigazgató (SOE LKK)
Dr. SZÓKA Károly PhD egyetemi docens (SOE LKK)
titkár: NEMÉNY Dorka Virág kutatási asszisztens (SOE LKK)
5
ORGANIZERS
University of Sopron Alexandre Lamfalussy Faculty of Economics (SOE LKK),
For the Higher Education in Sopron Foundation
Conference Chairperson: Prof. Dr. Zsuzsanna SZÉLES PhD Professor, Dean (SOE LKK)
Scientific Committee:
Chair: Prof. Dr. Csilla OBÁDOVICS PhD Professor, Head of Doctoral School (SOE LKK)
Co-Chair: Dr. habil. Balázs István TÓTH PhD Associate Professor, Director (SOE LKK)
Members: Prof. Dr. Attila FÁBIÁN PhD Professor (SOE LKK), Rector (SOE)
Prof. Dr. Csaba SZÉKELY DSc Professor Emeritus (SOE LKK)
Prof. Dr. László KULCSÁR CSc Professor Emeritus (SOE LKK)
Prof. Dr. László SZALAY DSc Professor (SOE LKK)
Prof. Dr. Clemens JÄGER PhD Professor, Dean (FOM)
Dr. habil. Aranka BARANYI PhD Associate Professor (SOE LKK)
Dr. habil. Zoltán POGÁTSA PhD Associate Professor (SOE LKK)
Dr. habil. Zoltán SZABÓ PhD Associate Professor (SOE LKK)
Dr. habil. Árpád Ferenc PAPP-VÁRY PhD Senior Research Fellow (SOE LKK)
Dr. Rudolf KUCHARČÍK PhD Associate Professor, Dean (EUBA FIR)
Organizing Committee:
Chair: Dr. Richárd RESPERGER PhD Assistant Professor (SOE LKK)
Members: Dr. Gábor KERESZTES PhD Associate Professor, Vice Dean (SOE LKK)
Dr. habil. Eva JANČÍKOVÁ PhD Associate Professor (EUBA FIR)
Dr. habil. László KOLOSZÁR PhD Associate Professor, Director of Institute (SOE LKK)
Dr. Mónika HOSCHEK PhD Associate Professor, Director of Institute (SOE LKK)
Dr. Judit PAPPNÉ VANCSÓ PhD Associate Professor, Director of Institute (SOE LKK)
Dr. Károly SZÓKA PhD Associate Professor (SOE LKK)
Secretary: Dorka Virág NEMÉNY Research Assistant (SOE LKK)
6
TARTALOMJEGYZÉK / CONTENTS
Plenáris szekció
Plenary Session
How to Make European Integration Fair and Sustainable?
István P. SZÉKELY ................................................................................................................ 13
1. szekció: Fenntartható gazdálkodás és menedzsment, körforgásos gazdaság
Session 1: Sustainable Economy and Management, Circular Economy
A zöld ellátási láncok aktuális kérdései - Kritikai szakirodalmi összefoglalás
PIRICZ Noémi ........................................................................................................................ 27
Well-being - kulcs a fenntartható működéshez
KÓPHÁZI Andrea – KOVÁCSNÉ LACZKÓ Éva Mária ........................................................ 36
Szervezeti kultúra és fenntarthatóság
KOVÁCSNÉ LACZKÓ Éva Mária ......................................................................................... 48
Az új mexikói kvótakereskedelmi rendszer és erdészeti vonatkozásai
KIRÁLY Éva – BOROVICS Attila .......................................................................................... 61
A designesztétika gazdasági megközelítésének lehetőségei
REMÉNYI Andrea – ZALAVÁRI József ................................................................................. 76
A körforgásos üzleti modellek a vállalati gyakorlatokban
KRIZA Máté ............................................................................................................................ 98
2. szekció: Társadalmi kihívások és társadalmi innovációk a fenntartható fejlődésben
Session 2: Social Challenges and Innovations in Sustainable Development
Társadalmi kihívások a divatipari fogyasztás terén
VIZI Noémi ........................................................................................................................... 119
Klímaszorongás jelenléte az X, Y és Z generáció életében
SZEBERÉNYI András ........................................................................................................... 128
Közelségi torzítás – a home office egyik kihívása
IONESCU Astrid .................................................................................................................. 147
Megérti-e a választ, ha megkérdezi kezelőorvosát, gyógyszerészét? Az egészségműveltség
mérésének aktuális kérdései Magyarországon
PORZSOLT Péter ................................................................................................................. 154
A digitális egészségügyi ellátás, mint innováció mérési lehetőségei
KOVÁCS Erika ..................................................................................................................... 168
7
3. szekció: Fenntartható pénzügyek és számvitel
Session 3: Sustainable Finance and Accounting
A közösségi költségvetési számvitel koncepciója és dilemmái
SISA Krisztina A. – SIKLÓSI Ágnes – VERESS Attila – DENICH Ervin ............................ 181
Az iszlám banki számvitel digitalizációjának elméleti és filozófiai megközelítése
CSEH Balázs ........................................................................................................................ 193
A vállalkozások csődkockázatának és a kötvényminősítések együttmozgása
SZÁNTÓ Tünde Katalin ....................................................................................................... 202
A globális minimumadó következményei és megvalósíthatósága a multinacionális
vállalatok számára
MATTIASSICH Enikő – SZÓKA Károly .............................................................................. 211
4. szekció: Fenntartható turizmus és marketing
Session 4: Sustainable Tourism and Marketing
A fenntartható turizmus: valóság vagy átverés?
PALANCSA Attila ................................................................................................................. 221
Metamarketing: fenntartható innovációk a valós és virtuális lehetőségek imperatív
szimbiózisa mentén
REMÉNYI Andrea ............................................................................................................... 237
A fennmaradás és fenntarthatóság aspektusainak vizsgálata a szálláshely-szolgáltatással
foglalkozó KKV-szektorban rendkívüli helyzetek idején
VARGYAS Daniella – KERESZTES Gábor .......................................................................... 261
Tudatosság és fenntarthatóság a nyaralás alatt is
MÉSZÁROS Katalin – HOSCHEK Mónika – Németh Nikoletta ......................................... 270
A soproni egyetemisták külföldi tervei
OBÁDOVICS Csilla – RUFF Tamás ................................................................................... 283
Country Branding of the Hashemite Kingdom of Jordan
Mohammad Hani KHLEFAT ............................................................................................... 295
Community-Based Tourism in Southeast Asia
Thi Thuy Sinh TRAN – Nikoletta NÉMETH – Md. Sadrul Islam SARKER – Yuan ZHANG –
NHAT ANH NGUYEN .......................................................................................................... 309
8
5. szekció: Sustainable Finance and Accounting, Sustainable Development
Session 5: Sustainable Finance and Accounting, Sustainable Development
Stakeholder Engagement in the Development of the Sustainability Reporting Standards
of the Global Reporting Initiative (GRI) and of the International Sustainability
Standards Board (ISSB)
Alina ALEXENKO ................................................................................................................ 329
The IFRS and the Financial Accounting System in Algeria: A Literature Review
Asma MECHTA – Zsuzsanna SZÉLES – Ágnes SIKLÓSI .................................................... 342
Potential Effects of Industry 4.0 Technologies on Environmental Sustainability - A
Systematic Literature Review
Mohamed EL MERROUN .................................................................................................... 351
The Use of Geothermal Energy for Sustainable Development and Economic Prosperity
Nadjat KOUKI – Andrea VITYI ........................................................................................... 365
6. szekció: Sustainability Transformation and Circular Economy
Session 6: Sustainability Transformation and Circular Economy
A fenntarthatóság, a társadalmi szerepvállalás és a felelős vállalatirányítás
szabályozásának szerepe a vállalati innovációban
BARTÓK István János .......................................................................................................... 381
Circular Economy Research Ttrends in the Textile and Apparel Industry:
A Bibliometric Analysis
Md. Sadrul Islam SARKER – Thi Thuy Sinh TRAN – István János BARTÓK .................... 389
The Historical Evolution of Employee Idea Management: A Comprehensive Review
Viktória ANGYAL ............................................................................................................... 405
7. szekció: Sustainable Economy and Management
Session 7: Sustainable Economy and Management
Bewältigungsstrategien eines nachhaltigen Managements von Organisationen innerhalb
einer VUCA-Umwelt: Eine systematische Literaturrecherche
Mike WEISS .......................................................................................................................... 421
Influences of Autonomous Vehicles on Sustainability: A Systematic Literature Review
Phillipp NOLL – Zoltán SZABÓ ........................................................................................... 436
Trends in Sustainable Leadership
Roland SEESE – Katalin DIÓSSI ......................................................................................... 452
Recruiting for Resilience: An Economic Approach to Mitigate Candidate Ghosting
Laureana Anna Erika TEICHERT ....................................................................................... 460
9
Führung auf Distanz - Herausforderungen für Führungskräfte durch die Nutzung von
Home-Office
Norbert KLEIN ..................................................................................................................... 473
A Generative AI and Neural Network Approach to Sustainable Digital Transformation:
A Focus on Medical and Marketing Sectors
Alexander Maximilian RÖSER – Cedric BARTELT ............................................................ 483
Allgemeine Alterswahrnehmung bei StudentInnen in den österreichischen und
ungarischen Grenzregionen
Dorottya PAKAI – Csilla OBÁDOVICS ............................................................................. 498
8. szekció: Társadalmi kihívások és társadalmi innovációk a fenntartható fejlődésben
Session 8: Social Challenges and Innovations in Sustainable Development
Fenntartható olvasás a digitális korban
MOLNÁR Csilla ................................................................................................................... 509
Okos és fenntartható városfejlesztés felelősségteljes digitális innovációval
GYULAI Tamás – NAGY Marianna ..................................................................................... 518
A coaching szerepe a vezetőfejlesztésben
KÓPHÁZI Andrea – Éva LÖWE .......................................................................................... 535
9. szekció: Fenntartható gazdálkodás és menedzsment
Session 9: Sustainable Economy and Management
A szolgáltatók szerepe és felelőssége a desztinációk fenntartható turizmusának
megteremtésében, illetve kialakításában: Szisztematikus irodalmi áttekintés
TEVELY Titanilla Virág – BEHRINGER Zsuzsanna ........................................................... 548
Bükfürdő imázsának élménymarketing alapú vizsgálata
HORVÁTH Kornélia Zsanett ................................................................................................ 563
A public relations (PR) tevékenység határai és viszonya a marketinghez - Egy PR
szakemberek körében végzett kvantitatív kutatás eredményei
KÁROLY Róbert – LUKÁCS Rita – PAPP-VÁRY Árpád Ferenc ......................................... 572
Márkázott szuperhősök: Hogyan formálják a különböző termék- és szolgáltatásmárkák
Amerika kapitány és Vasember karakterét a Marvel filmekben?
PAPP-VÁRY Árpád Ferenc – RÖNKY Áron ........................................................................ 591
Sztármárka-építés hosszú távon: Cristiano Ronaldo és CR7 márkájának megítélése –
Egy kvalitatív kutatás tapasztalatai
KORIM Dorina – PAPP-VÁRY Árpád Ferenc ..................................................................... 609
10
10. szekció: Sustainable Economy and Management I.
Session 10: Sustainable Economy and Management I.
The Role of Mountain Tourism Activities and Facilities on Domestic Tourism
Consumption in Tourism Destinations
Deborah KANGAI – Eliyas Ebrahim AMAN – Árpád Ferenc PAPP-VÁRY – Viktória SZENTE
............................................................................................................................................... 624
Sustainable Project Management
Attila LEGOZA ..................................................................................................................... 633
The Effect of Sustainability Development Using the Example of Green Washing
Dijana VUKOVIĆ – Tanja UNTERSWEG ........................................................................... 641
Sustainable Strategies in Case of Start-Up Enterprises
Peter IMRICSKO .................................................................................................................. 654
Sustainable Strategic Management at Multinational Companies
Peter IMRICSKO ................................................................................................................... 663
The EU as a “Leadiator” in Climate Governance - a Successful Soft Power Instrument?
An Analysis with a Focus on Sustainable Mobility
Sarah DIEHL ........................................................................................................................ 674
Az irodatér komfortjának vizsgálata a munkavállalók szempontjából – Út a jövő
optimális irodája felé
GROZDICS Anett Tímea – BORSOS Ágnes ......................................................................... 684
Mögliche Auswirkungen von CSRD & ESRS auf die digitale Wirtschaft und der
Fertigungsindustrie in Deutschland: aus der Perspektive der Industrieperformance und
der nachhaltigen Entwicklung
Mohammad Reza ROBATIAN .............................................................................................. 696
11. szekció: Sustainable Economy and Management II.
Session 11: Sustainable Economy and Management II.
Sustainability and Climate Protection in Hospitals - Green Hospitals in the Future in
Germany
Patricia Carola MERTEN .................................................................................................... 719
Territoriality in Climate Adaptation? Space Interpretations of Different Disciplines and
Fields and their Potential Utilization in the Examination of Climate Adaptation’s
Territorial Aspects
Attila SÜTŐ .......................................................................................................................... 727
Sustainable Unity in the European Insurance Market: Calculating Personal Injury
Claims (From Experience to Methodology)
Zsolt Szabolcs EKE .............................................................................................................. 745
11
12. szekció: Poszter szekció
Session 12: Poster Session
A dendromassza-hasznosítás, mint megújuló természeti erőforrás szerepe a
fenntartható, körkörös gazdaságban
SZAKÁLOSNÉ MÁTYÁS Katalin ......................................................................................... 755
Az I szektor karbonhatékonyságának vizsgálata Magyarországon
KOVÁCSNÉ SZÉKELY Ilona – MAGYAR Norbert – JAKUSCHNÉ KOCSIS Tímea .......... 761
A visegrádi országok egészségügyi reformjainak és intézkedéseinek összehasonlítása
VITÉZ-DURGULA Judit – SÓTONYI Tamás Péter ............................................................. 766
A márkaépítés hatása a fogyasztói lojalitásra a Magyar Telekom esetében
TAKÁTS Alexandra – SZÁSZ Zsombor Levente ................................................................... 780
Examining the Impact of Certain Factors on the Delivery Time of a Manufacturing
Firm Using Data Science Methods
Zsolt TÓTH – József GARAB ............................................................................................... 800
Artificial Intelligence with an Economic Growth Perspective
Fırat ŞAHIN ......................................................................................................................... 809
483
DOI: 10.35511/978-963-334-499-6-Roser-Bartelt
A Generative AI and Neural Network Approach to Sustainable Digital Transformation:
A Focus on Medical and Marketing Sectors
Alexander Maximilian RÖSER
PhD Student
István Széchenyi Economics and Management Doctoral School, University of Sopron, Hungary,
FOM University of Applied Science for Economics and Management, Essen, Germany,
isf – Institute for Strategic Finance, FOM University of Applied Science, Essen, Germany
c39bpt@uni-sopron.hu (Corresponding Author)
Cedric BARTELT
PhD Student
István Széchenyi Economics and Management Doctoral School, University of Sopron, Hungary,
FOM University of Applied Science for Economics and Management, Essen, Germany
iiorjk@uni-sopron.hu
Abstract:
Artificial intelligence (AI), in particular generative AI (GAI) and neural networks (NN), is
emerging as a transformative force in the evolving landscape of digital transformation in indus-
tries such as medicine and marketing. The paper describes the actual and potential impact of AI
on the advancement of sustainable practices in these areas through a theoretical study of exist-
ing research. Through case studies, from NN in medicine, e.g., skin cancer diagnosis to GAI in
marketing strategy, we highlight current applications and challenges. Despite the undeniable
capabilities of AI in these domains, there remains a dearth of research that links AI to tangible
sustainability outcomes. Our findings on AI in marketing highlight its importance in enhancing
branding, optimizing pricing, refining channels and personalizing promotions. Furthermore, the
application possibilities of NN in medical are presented. Due to the high model quality in the
field of cancer detection, diagnostic processes can be designed more efficiently by NN and thus
staff shortages can be compensated cost-efficiently and patient-centered. However, while AI
offers promising opportunities, it also requires an ethical, socially responsible approach, as un-
derscored by recent studies. This research underscores the imperative of AI integration for ho-
listic, sustainable outcomes in the medical and marketing sector.
Keywords: Artificial Intelligence, Generative Artificial Intelligence, Neural Networks, Sus-
tainable Practices, Ethics and Social Responsibility
JEL Codes: Q55, L10, M31, I11, C45
1. Introduction
Artificial Intelligence (AI) has emerged as a pivotal force in medicine and marketing, heralding
transformative changes across these sectors. In medicine, AI's potential to improve medical
diagnostics, treatment plans, and decision-making processes is increasingly recognized, as it
adeptly identifies patterns in patient data, facilitates natural language processing, and supports
sophisticated image analysis, automation, and predictive analytics (Davenport & Kalakota,
2019; Deng, 2014; Jiang et al., 2017; Wiens & Shenoy, 2018). At the same time, in marketing,
AI-driven digital transformation is revolutionizing business operations, enhancing customer en-
gagement, optimizing e-commerce platforms, and refining overarching strategies (Gołąb-An-
drzejak, 2023). AI-powered tools, including chatbots and virtual assistants, are streamlining
484
service delivery and increasing personalization in customer interactions (Tinashe et al., 2022).
In particular, Generative Artificial Intelligence (GAI) is increasingly being used in marketing
for content personalization, market insight generation, and innovative content marketing strat-
egies (Kshetri et al., 2023). The integration of AI technologies, particularly GAI and Neural
Networks (NN), in these areas not only improves operational efficiency, cost savings, and brand
image, but also drives long-term success and competitiveness (Nishant et al., 2020).
This paper aims to explore the intersection of AI and digital transformation, specifically
focusing on the role of GAI in marketing and NN in medicine. We intend to address the fol-
lowing guiding research question: How can marketing and medicine achieve sustainable digital
transformation using artificial intelligence? To answer this question, we will conduct a meth-
odological meta-analysis of existing research to highlight the impact of AI in promoting sus-
tainable practices in these sectors. Our research will include specific case studies – examining
NN in skin cancer diagnosis and GAI in marketing – to illustrate their current applications and
anticipate future implications and challenges.
To further refine our investigation, we pose complementary research questions. Regard-
ing GAI in marketing, we ask: (1) How is GAI currently being used in marketing? And (2) How
can GAI contribute to a sustainable digital transformation of the marketing process? In the con-
text of NN in medicine, our focus shifts to: (3) How are NNs currently being used in medicine?
And (4) How can NNs contribute to sustainable digital transformation in diagnostics? These
inquiries are critical in today's context, where the concept of sustainability goes beyond envi-
ronmental and ethical considerations. The integration of AI in creating sustainable practices
and operations in both medicine and marketing is increasingly becoming a critical issue. De-
spite considerable progress in AI integration, significant research gaps remain, particularly re-
garding the implications of GAI and NN through the lens of sustainability and linking AI capa-
bilities to sustainable outcomes in these sectors.
2. Methodology
This study employs a methodological meta-analysis of existing research on GAI and NN in
marketing and medicine. Given the diverse and evolving nature of studies in this area, a non-
statistical meta-analysis is considered most appropriate. This approach allows for a comprehen-
sive synthesis of the literature where statistical meta-analysis may not be applicable (Döring,
2023; Medjedović, 2014).
First, a literature search is conducted focusing on criteria specifically relevant to the ap-
plication of AI in medicine and marketing, with an emphasis on GAI and NN and their role in
sustainable practices. This process guides a comprehensive search across multiple databases,
including Scopus, Semantic Scholar, typeset.io, Web of Science, emerald insight, and Google
Scholar, resulting in a robust data set of literature relevant to the intersection of these technol-
ogies with sustainable practices. Each retrieved reference undergoes a rigorous quality assess-
ment to ensure alignment with the study's themes and methodological soundness. The criteria
for this evaluation include relevance, methodological rigor, credibility of data sources, and the
study's contribution to the field (Flick, 2020). High-quality references are then annotated suc-
cinctly to summarize their key findings, methods, conclusions, and any limitations or gaps. This
process ensures a clear understanding of each study's contribution to the research questions at
hand. The synthesis phase is a critical component of the methodology. It involves a thorough
analysis and integration of high-quality, relevant literature. This process not only compares and
contrasts different approaches and findings, but also discusses common themes, divergences
and innovative applications of AI in the context of sustainability. The aim is to fill existing
485
research gaps and provide new insights into the sustainable use of AI technologies in medicine
and marketing.
By adopting this methodological approach, the study aims to provide a replicable, relia-
ble, and trustworthy synthesis of existing research that addresses the critical question of how
AI, specifically GAI and NN, can facilitate sustainable digital transformation in medicine and
marketing.
3. Theory and state of research
3.1. Definition of digital transformation
Digital transformation, a term widely discussed in the academic literature, refers to the com-
prehensive integration of digital technologies into various aspects of an organization, leading
to fundamental changes in business operations and value delivery (Kretschmer & Khashabi,
2020; Westerman et al., 2014). However, there is no generally accepted definition of the term
(Teichert, 2019). In the academic literature, digital transformation is not only a technological
change, but also a strategic and organizational overhaul that affects business processes, cus-
tomer experience, and organizational culture (Kretschmer & Khashabi, 2020; Matt et al., 2015).
It is characterized as a continuous process that includes digitization, digitalization, and digital
transformation, with the latter being the most pervasive phase (Matt et al., 2015). Digital trans-
formation strategies are cross-functional and have far-reaching implications, requiring dedi-
cated leadership roles to drive and oversee the transformation process (Weber et al., 2022). The
impact of leadership on digital transformation is profound, requiring strategic and prioritized
changes to business activities, processes, competencies, and models to fully leverage the op-
portunities presented by digital technologies (Sow & Aborbie, 2018).
According to the research topic, digital transformation in medicine is transforming patient
care and services, providing opportunities to improve pandemic strategies, increase access to
services, and improve the quality of care (Gopal et al., 2019; Jabarulla & Lee, 2021; Pilares et
al., 2022). The COVID-19 pandemic has highlighted the need for innovative solutions and tech-
nologies (Agostino et al., 2021). Digital transformation has led to new roles and technologies,
such as information platforms and remote services (Hermes et al., 2020). It has also improved
the performance of key medicine business processes and simplified information technology
(Laurenza et al., 2018). AI has transformed medicine, enabling breakthroughs in healthcare
delivery and paving the way for effective, reliable, and safe AI systems (Bajwa et al., 2021).
Looking more specifically at the marketing sector, the academic community has yet to
extensively explore the relationship between marketing and sales in the digital age (Hauer et
al., 2021). Nevertheless, the ever-changing technological landscape demands that all business
units undergo digital transformation. Digital transformation involves the use of digital technol-
ogies to redesign traditional and non-digital business processes and services in response to
changing market and consumer demands (Cheromukhina, 2022). In the context of digital trans-
formation in marketing, there has been a significant amount of research in the field of market-
ing, and this research has primarily focused on specific digital tools. Among these tools, mar-
keting through social media platforms is the most studied. Close behind are other technologies
such as data analytics, mobile marketing, the Internet of Things, artificial intelligence, and In-
dustry 4.0. These technologies have all been the subject of extensive research in the marketing
field (Cioppi et al., 2023). However, there are other technologies that have not received as much
attention in the literature. These technologies apply machine learning (ML), virtual/augmented
reality, and security protection systems (Cioppi et al., 2023). A thorough analysis is required to
understand the correlation between marketing and sales in the digital age (Hauer et al., 2021).
Overall, the marketing domain is vast and diverse, encompassing a wide range of digital tools
486
and concepts. While certain technologies have received more attention than others, it is crucial
to recognize the importance of exploring both specific technologies and the broader concept of
digitization to gain a comprehensive understanding of the field.
3.2. Definition of sustainability and sustainable Artificial Intelligence
The Brundtland Commission defined sustainability in 1987 as "development that meets the
needs of the present without compromising the ability of future generations to meet their own
needs" (WCED, S. W. S., 1987). Since then, sustainability has developed far beyond its original
scope. The definition emphasizes the need to consider environmental, economic and social di-
mensions as interdependent (Davidson, 2010; McKenzie, 2004; Morelli, 2011; Olawumi &
Chan, 2018), which together enable achieving sustainability. In particular, environmental sus-
tainability emphasizes the interaction with the environment for the management of natural re-
sources so that future generations are not harmed by excessive resource consumption (Zarte et
al., 2019). This comprehensive approach to sustainability is crucial for the development and
application of AI, which must consider ecological integrity, social equity, and economic effi-
ciency.
Environmental sustainability means that current and future generations' needs for re-
sources and services can be met without compromising ecological integrity (Morelli, 2011).
Concepts and topics include low-impact transportation, sustainable agriculture, and the com-
bined conservation of environmental assets and management of waste and pollution (Callicott
& Mumford, 1997). Research on AI for sustainability has explored many of these issues, and
the focus has been on specific AI applications for biodiversity enhancement. Other trends are
also emerging. First, a largely overlooked component of AI is cognition and robotics. Some
pattern recognition and classification methods were based on ML models, such as Artificial
NNs (ANN) and clustering (Voyant et al., 2017). Second, renewable energy received much
more attention compared to saving energy. Third, most articles on renewable energy focused
on only one type of renewable energy rather than hybrid energy sources. Solar is the most com-
monly studied renewable energy source. Only one article discusses public opinion on energy
production (Nuortimo & Härkönen, 2018), and none examines the financial feasibility of mar-
ket deployment. There appears to be an overemphasis on technical difficulties in studies of AI.
The definition of social sustainability focuses on the development and promotion of prac-
tices and structures that support long-term well-being and equity within a society (Van
Wynsberghe, 2021). Social sustainability includes the protection and promotion of human
rights, equity, access to resources and education, and the promotion of social inclusion and
community spirit (Dovers & Handmer, 1992). Creating conditions that ensure ecological integ-
rity and social justice in all areas of human activity is part of social sustainability (Mensah,
2019). This includes the preservation of environmental resources for present and future gener-
ations, and the development of economic models and social values in ways that take into ac-
count the needs of all members of society (Dovers & Handmer, 1992). Transparency and the
exchange of information between stakeholders are crucial to the promotion and safeguarding
of social sustainability in various initiatives and legislation (Van Wynsberghe, 2021). This ap-
proach aims to balance progress with the preservation of human and environmental values,
prioritizing societal well-being and promoting social inclusion (Dovers & Handmer, 1992).
Recognizing the interdependence of environment, economy and society, social sustainability is
a holistic approach that seeks to create and maintain a just, inclusive and safe environment for
all (Mensah, 2019; Van Wynsberghe, 2021).
Economic sustainability refers to the efficient use of resources to maximize operational
profit and increase market value. This form of sustainability encompasses substituting natural
487
resources with man-made ones, as well as reusing and recycling (Khakurel et al., 2018; Kin-
dylidi & Cabral, 2021). At its core, it is about balancing economic growth with the protection
of the environment and social welfare. Economic sustainability is not just focused on maxim-
izing short-term profits but also includes a long-term perspective, emphasizing the conservation
of resources for future generations and the fair distribution of these resources. According to
Van Wynsberghe (2021), sustainable development oscillates not only between innovation and
equitable resource distribution but also between the needs of the environment, economy, and
society. In the realm of AI, this tension between innovation and sustainability, as well as the
sustainability of AI training and usage, must be considered. Kindylidi and Cabral (2021) high-
light that the European Commission in its White Paper on AI emphasizes the value of AI in
achieving sustainable economic growth and societal well-being and promotes the circular econ-
omy in the single market. This underscores the importance of considering the environmental
impacts of AI throughout its lifecycle and supply chain. Overall, economic sustainability is a
multidimensional concept that goes beyond mere profit maximization, placing ecological and
social aspects at the forefront. It is a crucial component of a comprehensive approach to sus-
tainability and plays a vital role in creating a just, resilient, and equitable society. This approach
ensures that economic activities are not only profitable but also sustainable in the long run,
contributing to the well-being of current and future generations.
Sustainable AI is a comprehensive concept that encompasses the entire life cycle of AI systems
and aims to promote ecological integrity, social equity, and economic efficiency (Van
Wynsberghe, 2021). It encompasses both the use of AI for sustainable purposes and the sus-
tainability of AI itself, which includes assessing and reducing its environmental impact (Kin-
dylidi & Cabral, 2021). Economic sustainability in this context means developing and deploy-
ing AI systems in a way that not only improves operational efficiency and optimizes resource
management (Kar et al., 2022), but also creates long-term economic value by reducing costs
and increasing market opportunities. This requires a balanced approach that integrates innova-
tive solutions, ethical and social responsibility, and economic considerations to meet the needs
of current and future generations while protecting the environment (Nishant et al., 2020). There-
fore, environmental, and social sustainability, as well as economic profitability and value crea-
tion, must be considered for sustainable AI in marketing and medicine.
3.3. Generative Artificial Intelligence and Neural Networks: Definition and delimitation
In recent years, there has been a significant increase in interest in the disciplines of GAI and
NN due to their close relationship. GAI is a type of AI algorithm that generates new data in-
stances that mimic an existing collection of data. One of the most prominent techniques in GAI
are generative adversarial networks (GANs), which consist of two NNs, a generator and a dis-
criminator, that work together to produce realistic outputs (Creswell et al., 2018). GANs have
been widely used in various applications such as image generation, data augmentation, and
video synthesis (Kakkar & Singh, 2021). ANNs, on the other hand, which are modeled after the
structure of the human brain, are an essential component of artificial intelligence. They evaluate
complex data and identify patterns. Nodes in NNs are interconnected and work together to pro-
cess information. These methods are useful for tasks such as speech and image identification,
natural language processing, and decision making (Hansen & Salamon, 1990).
Understanding the differences between GAI and NNs is critical to recognizing their indi-
vidual strengths and weaknesses. NNs are the foundational technology that allows GAI to focus
on generating novel data. ANNs, or GANs, are essential to the operation of GAI systems. By
analyzing patterns and characteristics in the training data, they provide the basis for learning
new information and generating new data (Yao & Liu, 1997). The development of GAI and
NNs has raised concerns about the potential misuse of AI-generated content, such as deepfakes
488
and false information (Hwang et al., 2021). Therefore, it is crucial to establish ethical standards
and boundaries for the use of NNs and GAI.
4. Generative Artificial Intelligence and Neural Networks in Digital Transformation
4.1. Neural Networks in medicine
NNs are a powerful tool in medicine, especially for diagnosing and classifying diseases. NNs
have shown promising results in dermatology, oncology, and pathology. In fact, deep learning
has outperformed many dermatologists in classifying dermoscopic melanoma images (Brinker
et al., 2019). In this study, enhanced deep learning methods were used to train a Convolutional
Neural Network (CNN) with a large dataset of dermoscopic images. Therefore, out of 100 tissue
scans, 20 were tumors and 80 were benign moles, 157 dermatologists were asked to classify
them as malignant or non-malignant. Based on this study, only 7 dermatologists were better
than the classification algorithm, 14 were equal and 136 performed worse within this classifi-
cation task (Brinker et al., 2019). This study demonstrated the potential of the NN to accurately
diagnose skin neoplasms. The NN outperformed over 86% of the dermatologists. Therefore,
the use of AI, especially NN, in the context of skin cancer diagnosis can be highly recom-
mended.
Additionally, another study highlighted the effectiveness of CNN in accurately classify-
ing genetic mutations in gliomas, a type of brain neoplasm (Chang et al., 2018). It highlighted
the potential of CNN approaches to model the animal visual cortex. By simulating multiple
layers of neurons, these approaches transform raw input images into complex representations.
This, in turn, demonstrates the potential of NN in aiding the diagnosis and classification of
complex diseases, such as gliomas (Chang et al., 2018).
NN has shown promise in the field of oral medicine. A pilot study was conducted on the
classification of clinical autofluorescence spectra of oral leukoplakia using an ANN (Van
Staveren et al., 2000). The study demonstrated the potential of NN to aid in the diagnosis of
oral leukoplakia and showed its versatility in different medical domains. This study can be de-
scribed as one of the oldest studies of NN in medicine, where an NN achieves a sensitivity of
86% by analyzing spectral data instead of the image classification used today (Van Staveren et
al., 2000).
An additional study demonstrated the ability of deep learning to predict microsatellite
instability directly from histology in gastrointestinal cancer. This expands the application of
NNs in oncology and pathology (Kather et al., 2019). The potential of NNs in providing valu-
able insights and predictions from histological data can significantly impact the diagnosis and
treatment of gastrointestinal neoplasms.
NN are not only useful for diagnosis, but also for detecting diseases. A study presented
an efficient method for detecting skin cancer using CNN, highlighting the potential of NNs in
aiding early detection and diagnosis of skin cancer(Sreelakshmi et al., 2023). NNs have a sig-
nificant impact on preventive medicine and public health.
Another study compared the performance of a conventional image analyzer with a CNN
in diagnosing skin lesions (Sies et al., 2020). The study further highlighted the superiority of
the NN in accurately diagnosing skin neoplasms. This comparative study provides valuable
insights into the potential of NNs in revolutionizing the field of dermatology and skin cancer
diagnosis.
In a separate research study, a CNN was used to detect brain tumors. The study used a
CNN that was improved through the use of a policy optimizer, which resulted in a remarkable
accuracy rate of 95.98% in identifying brain tumors (Wu & Shen, 2023). To improve the per-
formance of the CNN, certain techniques were implemented, including skull removal and noise
489
reduction. As a result, the optimized CNN model outperformed previous methods, demonstrat-
ing the great potential of NNs in medical image processing (Wu & Shen, 2023). This approach
can be compared to the successful use of NNs in various other fields, such as dermatology,
where they have proven to be highly effective in accurately identifying diseases. In summary,
NN has shown great promise in various medical fields, including dermatology, oncology, pa-
thology, and preventive medicine. NN can accurately diagnose, classify, and detect disease,
demonstrating its potential to revolutionize medical practice and improve patient outcomes.
4.2. Generative Artificial Intelligence in marketing
The application of AI in B2B marketing has received considerable attention, with companies
using AI to identify strategic options and reduce operating costs (Huang & Rust, 2021; Paschen
et al., 2020). AI research in B2B marketing remains underrepresented in comparison with B2C
marketing. Especially when focusing on the application of GAI in B2B marketing (Dwivedi et
al., 2021; Keegan et al., 2022), there is a noticeable lack of in-depth studies focusing on AI
applications in B2B marketing. GAI, an advanced segment of AI capable of independently gen-
erating new content, has already played a transformative role in the B2C domain (Giri et al.,
2019). However, the potential and challenges of B2B GAI remain largely unexplored due to
the complexity of customer relationships and longer sales cycles in the B2B market (Keegan et
al., 2022). There is an urgent need for research, especially in the application of GAI in B2B
marketing, given the lower intensity of AI research in B2B compared to B2C and the relative
newness of GAI (Kshetri et al., 2023). Studying GAI in this context offers promising opportu-
nities for developing and understanding innovative B2B marketing strategies.
GAI has revolutionized marketing by introducing innovative capabilities and changing
traditional approaches (Butler, 2023). It has an impact on content creation, campaign optimiza-
tion, and audience engagement strategies (Kshetri, 2023a). A substantial portion of marketers
are using GAI tools in their creative process, with 66 percent using them to brainstorm and
nearly half creating final content from scratch (Butler, 2023). This shift in marketing content
creation and management is significant. GAI also provides a more efficient and cost-effective
approach to the creation of custom visual content (Fui-Hoon Nah et al., 2023). More than 100
billion pieces of content, including articles, blog posts, and social media posts, have been cre-
ated using GAI, revolutionizing traditional campaign processes with real-time content creation,
adaptive optimization, and faster results (Cromwell et al., 2023).
Tools such as OpenAI's ChatGPT, GPT-4, and DALL-E2, as well as marketing-specific
variants such as Neuroflash, HeyGen, and Canva are increasingly being used in marketing ac-
tivities. ChatGPT in particular has become very popular. Approximately 86% of marketing
managers in Germany believe that the importance of AI in marketing will increase in the com-
ing years (Bünte, 2023). GPT-4 offers a significant advantage in creating more engaging and
relevant content through its advanced speech generation capabilities, including the ability to
process both text and image input. This is critical to creating personalized customer experi-
ences. DALL-E2's text-to-image prompting capabilities further enhance the potential for gen-
erating realistic images and artwork. This adds a valuable dimension to visual marketing. In
addition, Microsoft's introduction of a Copilot for Office 365 or Adobe's Firefly integration
within the Creative Suite highlights the growing role of GAI in digital advertising (Kshetri,
2023b). This innovation allows advertisers to create different iterations of ad copy, generate
background images via text prompts, and adjust image cropping for different media formats. It
demonstrates the growing integration of GAI tools within the marketing ecosystem. GAI's mul-
tifaceted impact on marketing ranges from content generation to campaign customization to
visual content creation (Kshetri et al., 2023). Marketers can now engage more effectively with
their audiences, create personalized content, and execute innovative campaigns with tools like
490
ChatGPT, neuroflash, HeyGen, and Canva. The future of marketing with GAI is poised to offer
even more personalized and effective strategies, driving the industry toward more innovative
and customer-centric approaches.
The integration and effectiveness of GAI in marketing is influenced by several key fac-
tors. The ease of use and accessibility of GAI tools, such as ChatGPT, are critical, as they do
not require specialized skills, broadening their appeal to marketers (Kshetri, 2023a). The triala-
bility of these tools, often offered through free access periods, encourages experimentation and
understanding without an initial financial commitment (J. Chen et al., 2023). In addition, the
cost-effectiveness of GAI tools, illustrated by affordable pricing models and subscriptions,
makes them accessible to a wide range of organizations, including SMEs (T.-P. Chen, 2023;
Kshetri et al., 2023). The availability of various GAI tools tailored to specific marketing needs
also plays a critical role. This diversity allows marketers to select tools that best meet their
specific needs, whether for accuracy, content security, or graphical sophistication (Kshetri et
al., 2023; O´Brien, 2023). In addition, the ability to customize these tools for specific content
domains and marketing strategies is critical. Techniques such as prompt training and similarity
algorithms allow for the fine-tuning of GAI models to align with unique brand messages, prod-
ucts, and services (Davenport & Alavi, 2023). Together, these factors are driving the marketing
industry toward more innovative, efficient, and personalized approaches.
In the context of marketing, GAI presents both transformative opportunities and signifi-
cant limitations. False results are a primary concern, as GAI models such as LLMs can produce
plausible but incorrect or nonsensical content, a phenomenon termed “hallucination” (Ji et al.,
2022; Spitale et al., 2023). Bias and fairness issues arise from societal biases embedded in train-
ing data, leading to unfair or stereotypical representations (Caliskan et al., 2017; Hartmann et
al., 2023; OpenAI, 2023). Copyright infringement also poses legal challenges, as GAI may
replicate copyrighted works. In addition, the environmental impact of developing and using
GAI systems is a growing concern due to their significant energy consumption (Van Wyns-
berghe, 2021).
5. Discussion
5.1. Impact of Neural Networks in medicine
NN have had a profound impact on medicine. They have revolutionized various aspects of
medical practice and contributed to social and economic sustainability. NNs have made treat-
ment approaches more efficient and patient-centered, ultimately improving medicine outcomes
and reducing the burden on healthcare systems.
The use of NNs in medicine and its transition to impact is evident in the field of derma-
tology, where deep learning has demonstrated superiority over dermatologists in the classifica-
tion of dermoscopic melanoma images, highlighting the potential of NNs to provide accurate
and efficient diagnosis, thereby enabling patient-centered treatment (Brinker et al., 2019; Wu
& Shen, 2023). NNs play a critical role in ensuring timely and accurate diagnosis, which is
essential for effective patient care.
In addition, CNN has demonstrated its impact in oncology by accurately classifying ge-
netic mutations in gliomas (Chang et al., 2018). Accurate classification of genetic mutations
can have significant implications for personalized treatment approaches. This can lead to tar-
geted therapies, improving patient outcomes and reducing the economic burden of ineffective
treatments. The study indicated that NN can predict microsatellite instability in gastrointestinal
cancer directly from histology. This highlights the potential of NN to provide valuable insights
for personalized treatment strategies (Kather et al., 2019). This approach not only improves
491
patient outcomes but also contributes to the economic sustainability of medicine by reducing
unnecessary treatments and associated costs.
Moreover, the impact of NN in preventive medicine is evident from the work in skin
cancer diagnosis, which presented an efficient method for skin cancer detection using CNN
(Sreelakshmi et al., 2023). NN-based approaches for early detection of skin cancer can improve
patient outcomes and reduce the economic burden of advanced-stage treatments, contributing
to the overall sustainability of medicine systems.
The study by Sies et al. (2020) compared the performance of a conventional image ana-
lyzer with a CNN in diagnosing skin lesions. The results emphasize the potential of NN to
revolutionize dermatological diagnosis, leading to more efficient and accurate diagnosis, and
enabling timely and patient-centered treatment.
In conclusion, NNs have a far-reaching impact on medicine. They contribute to social and
economic sustainability by enabling efficient, patient-centered treatment approaches. The ap-
plication of NN has the potential to revolutionize medical practice and improve healthcare out-
comes.
5.2. Impact of generative artificial intelligence for the marketing
GAI has shown promise in marketing by improving content creation, campaign optimization,
and audience engagement. Tools such as ChatGPT and Neuroflash have shown the potential to
improve the customer experience through personalized content and efficient campaign man-
agement. However, GAI applications have limitations. For example, there is a risk of generating
incorrect or nonsensical results, known as “hallucination”. This requires careful monitoring and
validation of GAI-generated content to ensure accuracy and relevance. In addition, societal bi-
ases in training data raise concerns about fairness and representation, requiring critical exami-
nation of data sources. Legal challenges related to copyright infringement are also a significant
risk, as GAI's ability to replicate copyrighted works requires careful navigation.
GAI systems require a balanced approach to contribute to long-term economic growth
and market value without depleting natural resources. They should optimize operating costs,
foster innovation, and promote social welfare. This is consistent with the principles of sustain-
able AI and promotes ecological integrity. AI systems can be catalysts for sustainable economic
practices that benefit current and future generations. However, their energy-intensive nature
raises concerns about their sustainability. The integration of GAI in marketing is growing due
to its ease of use, accessibility, cost-effectiveness, and adaptability. This enables companies,
including SMEs, to use GAI for innovative marketing strategies. The variety of available GAI
tools, each tailored to specific marketing needs, further enhances this integration, allowing mar-
keters to choose tools that meet their unique needs.
The study explores the use of GAI in marketing, highlighting its potential to revolutionize
marketing strategies from content creation to customer engagement. Despite challenges such as
accuracy, bias, and legal issues, GAI's potential for economic sustainability and innovation re-
mains undiminished. Its ability to personalize marketing efforts and its cost-effectiveness make
it a valuable tool for future marketing efforts. The research provides a balanced view of GAI's
impact on marketing and offers insights for integrating it into sustainable, effective, and ethi-
cally responsible marketing strategies. The findings provide a valuable framework for the
evolving marketing landscape.
492
6. Conclusion
This paper critically examines the transformative role of GAI in marketing and NN in medicine,
specifically addressing their potential to facilitate sustainable digital transformation in these
sectors.
In medicine, NNs have proven to be disruptive, particularly in dermatology and oncology.
Studies such as Brinker et al. (2019) demonstrate the superiority of NNs in diagnosing diseases
such as melanoma, where they outperformed the majority of dermatologists. In a similar vein,
Chang et al. (2018) and Kather et al. (2019) show how NNs can effectively classify genetic
mutations in complex diseases such as gliomas and gastrointestinal cancers, paving the way for
more personalized treatment approaches. These advances in medical diagnostics and treatment
underscore NNs' critical role in improving patient care and contributing to health systems' eco-
nomic sustainability.
Conversely, in marketing, GAI has emerged as a powerful tool. It is reshaping content
creation, customer engagement, and campaign optimization. As highlighted by Butler (2023)
and Cromwell et al. (2023), the adoption of tools such as ChatGPT and Neuroflash demonstrates
the effectiveness of GAI in creating personalized marketing strategies. Nevertheless, the chal-
lenges of content accuracy, embedded bias, and legal complexity discussed by Ji et al. (2022)
and Hartmann et al. (2023) require careful and ethical application of this technology.
Reflecting a broader trend toward leveraging AI for sustainable digital transformation,
the integration of GAI and NN in marketing and medicine, respectively, is on the rise. While
GAI in marketing faces hurdles such as content verification and bias mitigation, its ability to
generate innovative and tailored marketing solutions is undeniable. Similarly, a significant leap
towards efficient, accurate and patient-centric medical care is being made through the applica-
tion of NN in medicine, particularly in disease diagnosis and prediction. In conclusion, the
burgeoning role of GAI in marketing and NN in medicine represents not only technological
advancement, but also a commitment to sustainable practices. These AI technologies, with their
unique strengths and limitations, are critical to driving the digital transformation of these sec-
tors. As AI continues to evolve, its integration across sectors must be guided by ethical consid-
erations, sustainability goals, and a commitment to improving human well-being.
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