| Journal of Collective Sciences and Sustainability
Received: 10 May 2025; Revised: 18 June 2025; Accepted: 22 June 2025; Published Online: 24 June 2025
J. Collect. Sci. Sustain., 2025, 1(1), 25405 | Volume 1 Issue 1 (June 2025) | DOI: https://doi.org/10.64189/css.25405
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0).
AI-Driven Open-Source Intelligence in Digital
Forensics for Cybercrime Investigation
Nitin Soni
*
and Rakesh Poonia
*
Department of Computer Applications, Engineering College, Bikaner, Rajasthan, 334004, India
*Email: nitinsoni.mca@ecb.ac.in (Nitin Soni) rakesh.poonia@ecb.ac.in (Rakesh Poonia)
Abstract
The growing complexity and frequency of cybercrimes have surpassed the capabilities of traditional digital
forensics methods. This study investigates the potential for an enhancement in digital forensics based on an
integration with Artificial Intelligence (AI) and Open-Source Intelligence (OSINT) sources. A proactive approach
to cybercrime investigations is proposed. AI-driven OSINT tools can collect, process, and analyze vast amounts
of publicly available data from diverse sources such as social media, forums, and the dark web at incredible
speeds. These tools can identify patterns, anomalies, and potential threats with unprecedented accuracy and
speed by applying machine learning algorithms and natural language processing techniques. This article
explores the operational dynamics of AI-driven OSINT, how it augments capabilities of forensic investigators to
better anticipate and thwart cyberattacks before they escalate. This paper further provides a comprehensive
review of the current challenges in digital forensics, such as the limitations in handling data and the reactive
nature in traditional methods. Using very elaborate case studies, we clearly highlight the practical application
of AI-driven OSINT in a variety of cybercrime scenarios which improve investigative outcomes by a significant
margin.
Keywords: Digital forensics; Open-Source Intelligence (OSINT); Cybercrime investigation; Data mining techniques;
Threat intelligence.
1. Introduction
In this era of sophistication in the world of cybercrime. Digital forensics is the systematic collection, analysis,
and preservation of electronic information to retrieve evidence as well as support criminal investigations. Yet
more advanced methods are being deployed by cybercriminals to render traditional forensic methods
inadequate and ineffective for the amount, speed, and variety of data being generated in the digital realm.
Digital forensics integrated with artificial intelligence (AI) has now brought open-source intelligence (OSINT)
closer to achieving its very practical pinnacle. OSINT normally refers to information gathered from the public
domain, such as websites and social media forums. With AI technologies, it can be adopted differently for
cybercrime investigation; theoretically, use it proactively to identify, analyze, and/or predict potential threats.
In general terms, OSINT analysis comprises gathering data from publicly accessible sources to build usable
knowledge. Instead of relying on classified or proprietary information, it seeks information from open sources
such as the internet, social networking sites, news portals, government databases, or meeting places on common
interests. The purpose may be for anyone, from national security considerations to law enforcement,
commercial competitive intelligence, or cybersecurity.
With the coming into being of the digital world, the establishment brought about the exponential growth of data
that could be unconsciously exposed by either an individual or an organization. Profiles on these social media
applications reveal anything from roles and competencies, social affiliations, recent activities, and even location
movement data. Likewise, the website of an organization and press releases can offer information on
organizational structures, schedule
dates of projects, and the specific intent of strategizing. Each of these pieces of information stands innocent in
the free world, whereas a skillful analyst can piece this data together to produce an intelligence profile.
OSINT is used to collect information in certain situations, going intermediates in legal proceedings or for hostile
intelligence, and much of its efficacy lies in being legal and accessible. It needs no hacking or secret surveillance;
rather, it involves tools such as advanced search engines, scraping software, metadata analyzers, and mapping
platforms to pull meaningful patterns out of data points from telecommunications. Thus, OSINT is a technique
admired both by genuine security practitioners who conduct offensive threat assessments and by adversaries
who organize targeted attacks.
Artificial intelligence is trained to scan huge amounts of data using machine learning, natural language, and data
mining techniques to profile patterns to derive actionable intelligence. It helps in recognizing cyber threats
quickly, utilizing precious time in searching for clues in a crime or fraud case, and responding timely to all strata.
Hopefully, it will bring a sense of elevations to providing an overall framework for risk intelligence gathering in
the fast-evolving cyberspace and all its attendant threats.
The study investigates the application of AI-enabled OSINT for elevating digital forensics about the advantages
and challenges it brings along with concrete cases in real time. The paper's review of existing practices and
innovations attempts to show how an AI-enabled OSINT could make transformation from highly centralized
reactive measures to proactive strategies in cybercrime investigations scope. Fig. 1 shows flow chart of the
process of identifying potential threats through AI-enhanced open-source intelligence.
Fig. 1: Flow chart of the process of identifying potential threats through AI-enhanced open-source intelligence.
2. Literature review
The adoption of Artificial Intelligence (AI) and Open-Source Intelligence (OSINT) has been faced with rapid
development in terms of research emerging from digital forensics with great promise in combating cybercrime.
This literature review revolves around available research on digital forensics, the contributions of OSINT to
cyber security, and the impact that AI technologies will make to the entire discipline.
2.1 Gap analysis
Despite the promising findings in the literature, several notable gaps persist in the current body of research:
1. Limited real-world implementation studies: Most research is conducted in controlled environments or
using synthetic datasets. There is a lack of case studies or longitudinal analyses showing how AI-OSINT
solutions perform in real-world, high-stakes cyber forensic investigations.
2. Scalability and integration challenges: While AI models perform well in isolated tasks (e.g., classifying
phishing emails), integration into full-scale digital forensic workflows—alongside traditional tools and legal
procedures-remains underexplored.
3. Data quality and verification issues in OSINT: OSINT sources can be noisy, incomplete, or unreliable. Few
studies address how to verify, filter, and manage the integrity of OSINT data when used in digital forensic
contexts.
4. Legal and ethical constraints: The intersection of AI, OSINT, and digital forensics raises significant concerns
about privacy, consent, and admissibility in court. Yet, most studies overlook legal frameworks or policy
implications.
5. Lack of standardized evaluation metrics: Existing research often uses varying benchmarks to evaluate AI-
driven forensic tools, making it difficult to compare methods or replicate findings across different domains.
6. Human-AI collaboration and explainability: Few investigations consider how forensic analysts interact with
AI systems or trust their outputs. The explainability of AI decisions in legal or investigative settings is still a
critical unmet need.
2.1 Digital forensics: current landscape and challenges
Digital forensics is an established field aimed at the retrieval and analysis of electronic data in support of legal
proceedings. Conventional forensic techniques rely on the manual collection and analysis of data, which may be
time-consuming and susceptible to human error. Casey discusses the limitations of traditional digital forensics
with regard to handling the ever-growing volume of digital evidence, further emphasizing the need for more
efficient methodologies.
[1]
Beebe and Clark described digital forensic as incident responsive, only responding to the incident when it has
occurred. This generally leads to slow response, with cybercriminals exploiting the given vulnerability and
slipping through the mesh.
[2]
Additionally, the heterogeneity of digital devices and data types increases the level
of complexity during forensic investigations. Advanced and more automated tools will be required to address
these increased complexities.
2.2 Open-source intelligence in cybersecurity
OSINT has gained a significant role in cybersecurity by acquiring intelligence from public sources. Clarke and
Papadopoulos defined OSINT as an inexpensive, easily accessible tool for gathering information that can help
one understand possible threats that cannot be identified by using traditional methods of intelligence
gathering.
[3]
The authors explained how OSINT can increase situational awareness, especially regarding the
early signs of cyberattacks.
Akhgar et al. discuss the use of OSINT in law enforcement and point out that OSINT is applied in monitoring
criminal activities on social media.
[4]
That also indicates to readers that the information obtained by OSINT may
be false, deceptive, unreliable, or incomplete.
2.3 Artificial intelligence in digital forensics
These techniques have brought a fundamental change in many areas of cybersecurity, including the digital
forensic. According to Jain et al.,
[5]
machine learning algorithms play a role in the automation of data, which offers
a way to save time and lessen the burden on forensic investigations. In this regard, the authors demonstrated
that AI can identify patterns and anomalies in vast datasets that might otherwise go undetected by human
analysts.
Zawoad and Hasan have been further suggested alternatively through AI-aided frameworks that realizable real-
time forensic analysis will rely on text-data interpretation from various sources.
[6]
These frameworks aim to
enhance the detection and response to threats, AI will evolve traditional forensic practices and continues to
accelerate and transform traditional forensic practices.
2.4 AI-driven OSINT: bridging the gap
Within this conjunction of AI and OSINT lies an enabling atmosphere for adopting a more proactive approach
toward cybercrime investigation, complementing the most prominent deficiencies that digital forensics
possesses compared to its traditional counterparts. Sharma and Mehta provide an extensive literature review
on AI-enabled OSINT tools in the area of threat intelligence and incident response.
[7]
The premise being
advanced here is that OSINT collection and analysis can potentially be automated with AI tools, delivering
perceptions with precision and in a timely manner.
Van der Walt and Eloff, interactive methods of AI-driven OSINT should work together with forensic workflows
so that predictive capability would increase.
[8]
Their research shows successful case studies through which
security breaches were preemptively identified using AI-driven OSINT. This proves the real-life applicability of
the method.
2.5 Challenges and ethical considerations
Despite the fact that AI-driven OSINT will have many bright prospects in digital forensics, however, there would
be many challenging issues. As per Chakraborty et al.,
[9]
ethical implication of using AI surveillance and data
collecting has been portrayed with respect to privacy and liberty. The development of strong and robust legal
frames is recommended toward the responsible application of AI-OSINTs.
Kuner et al.
[10]
pointed out the challenges of regulation when it comes to cross-border data flows in OSINT
operations, arguing that international cooperation and standardization are necessary for these issues.
3. Methodology
This section will cover various methods that study towards AI and OSINT integration using the proposed
implications for digital forensics. The methods would here include the designing of the research framework as
shown in Fig. 2, methods for the collection of data, techniques for analysis, and approaches to evaluation.
3.1 Research framework
The use of a hybrid methodological framework, combining theoretical investigation, case studies, and empirical
simulation, allows for a full scope of investigation with respect to conceptual and practical approaches toward
this topic. The main components of the framework are:
Exploratory research: Examining the traditional digital forensics that challenge AI use in OSINT and its potential
to bridge the gap.
Case study analysis: The real-life situation where AI and OSINT have been harnessed to maximum potential.
Simulation and testing: Experiments in a laboratory to attribute value to AI OSINT platforms.
3.2 Data collection
For maximizing the research, different sources of data are put to use for the following purposes:
OSINT sources: Information obtained from social networks, forums, blogs, dark web markets, news portals and
public databases.
Academic literature: Peer-reviewed articles, conference papers, and trade publications related to AI, OSINT, and
digital forensics are being analyzed for building a theoretical base for project work.
Case studies: Real life incidents and investigations are analyzed for understanding applications of AI-based
OSINT tools.
Simulated cybercrime scenarios: Synthetic datasets are generated to simulate a cyber-crime scenario for testing
of the AI-driven OSINT tool.
Fig. 2: Schematic of research farmwork design.
3.3 Implementation of AI-driven OSINT
This research traces the path of integrating AI into OSINT workflows, based upon key technologies and
techniques such as:
3.4 AI-driven OSINT tools measure the following key performance indicators
Accuracy is measured in terms of precision and recall in detecting cyber threats. Time efficiency: Amount of
time that would be needed to analyze massive datasets and formulate actionable insights.
• Scalability: Assessing if the tools support the growth in volumes of OSINT data
• Proactivity: How AI-led OSINTs can predict and counter cybercrimes before they occur
3.5 Case study analysis
Real-life investigations of cybercrime are analyzed using AI-driven OSINT to show application in real world.
Cases chosen include:
• The use of Machine learning algorithms to detect phishing campaigns and stop them
• Using dark web traffic monitoring and analyses to identify when data breaches happen.
• Social media analysis towards early detection of coordinated cyber-attacks.
3.6 Ethical and legal
The study aims to address challenges related to ethics and law involving AI-driven OSINT, such as;
• Data Privacy-Compliance on GDPR and CCPA while processing and analyzing data from OSINT.
• Biasness and Fairness-Reduced algorithmic bias in AI systems to ensure equality in the delivered results.
• Transparency- Explaining AI-Driven decisions while building trust as well as an accountability mechanism
in place.
3.7 Validation and testing
The findings from case studies and simulation are validated based on:
• Comparison Analysis: a comparison of performances between AI-powered OSINT-based tools and that of
traditional forensics practices.
• Review with Experts: requests for feedback of the proposed practicality by professional cybersecurity
practitioners/forensic analysts.
4. Case studies
The following section presents in-depth case studies and simulated scenarios that show the application and
effectiveness of AI-driven OSINT in enhancing digital forensics. The results of these investigations are compared
with traditional methods to point out improvements in accuracy, efficiency, and proactivity.
4.1 Case study 1: detection of phishing campaigns through AI-Driven OSINT
Scenario: A financial institution reported increased phishing emails targeted at its customers. The attackers
posed as the bank and sought sensitive information via phishing links.
OSINT data collection: Email samples and phishing reports available in public forums and repositories, such as
PhishTank. Social media sites where the victims posted reports of phishing. DNS records and metadata from the
phishing domains.
AI integration: Machine learning algorithms (e.g. Random Forest) were trained to classify emails as malicious
or benign based on features such as URL structure, sender metadata, and content patterns. Natural Language
Processing (NLP) analyzed email content to detect fraudulent intents, such as urgency or reward-based
language.
Results: The AI-driven OSINT system identified 92% of phishing emails with a false positive rate of 4%.
[11]
Real-
time domain monitoring detected new phishing websites within minutes of their activation. The financial
institution was able to alert customers and block phishing domains proactively, reducing the impact of the
attack.
Impact: Compared to traditional forensic methods, the AI-driven OSINT approach reduced detection time by
70% and minimized customer losses. Table 1 shows the comparison between detection of phishing campaigns
through AI-Driven OSINT and traditional methods across key performance metrics.
Table 1: Comparision between detection of phishing campaigns through AI-Driven OSINT and traditional methods
across key performance metrics
Metric
AI-Driven OSINT (this case study)
Traditional methods
Detection accuracy
~ 92%
~ 75% (manual/forensic level)
False positives rate
~ 4%
Higher (manual errors, heuristics)
Detection latency
Real-time / minutes
Hours to days
Coverage / proactivity
High (automated detection across OSINT)
Mostly reactive
Cost efficiency
Moderate initial setup, long-term savings
High recurring costs
Customer impact
Minimally disruptive, early alerts
Chance of higher losses
4.2 Case study 2: dark web monitoring for data breach detection
Situation: A cybersecurity firm was hired to determine the existence of a data breach into a retail business,
where dark web sources stated that customer records were being sold.
4.2.1 Methodology
OSINT data gathering: Crawlers and scrapers were used to gather relevant data on dark web marketplaces and
forums. Keyword usage included its name, records of customers, and financials of the business.
AI Infusion: Advanced AI-powered pattern recognition tools were instrumental in uncovering a potential data
breach linked to the retail company. These algorithms scanned vast amounts of online data and were able to
identify and connect leaked datasets back to the organization, even when the data was shared anonymously or
in fragmented forms.
In addition, AI-driven image recognition technology played a key role by analyzing visuals shared across dark
web forums. It successfully detected screenshots that appeared to show the company’s internal systems—an
alarming sign that unauthorized access may have occurred.
At the same time, sentiment analysis tools monitored online discussions across forums and underground
networks. These tools picked up on conversations that showed a sudden spike in concern or interest around the
company’s name, particularly posts that included language suggesting a recent data compromise. This flagged
the incident early, allowing investigators to take a closer look before the issue could escalate further.
Results: AI-driven OSINT identified the breached dataset two weeks before it was publicly reported. The retail
company was able to notify affected customers and secure vulnerable systems before further exploitation. This
forensic evidence, obtained through dark web monitoring, was also used to identify attackers and aid the law
enforcement agencies.
Impact: The proactive approach had prevented enormous financial losses as well as reputational damage.
Traditional approaches, where detection would be possible only weeks after the incident, would have been too
late.
4.3 Case study 3: monitoring social media for coordinated cyberattacks
Case Study: Law enforcement agencies were tasked with investigating a series of Distributed Denial of Service
(DDoS) attacks on critical infrastructure, suspected to be coordinated via social media.
4.3.1 Methodology
OSINT data collection: Monitoring of social media platforms, forums, and messaging groups for keywords and
hashtags related to the attacks.
Real-time feeds were analyzed to detect discussions or posts indicating planned activities.
AI integration: NLP models scanned text for coordination indicators, including date, time, and target
infrastructure. Sentiment analysis detected posts with hostile intent. Network analysis mapped connections
between users discussing the attacks.
Results: The AI-driven OSINT system identified a cluster of accounts coordinating the DDoS attacks. Law
enforcement disrupted the planned attacks by apprehending key individuals and taking down their
communication channels. Forensic evidence collected from social media was admissible in court and aided
prosecution.
Impact: Compared to the traditional investigation processes, the system developed by AI for OSINT quickened
response time by 60% and prevented further damage to critical infrastructure.
4.3.2 Performance evaluation
The results from these case studies and simulations were evaluated based on key performance metrics as shown
in Table 2.
Table 2: Comparison between AI-Driven OSINT and traditional intelligence gathering methods across key
performance metrics
Metric
AI-Driven OSINT
Traditional methods
Detection Accuracy
92%
75%
Response Time
Reduced by 60-70%
Longer due to manual analysis
Proactivity
High
Low (mostly reactive)
Data Handling
Scalable
Limited by manual capacity
Cost-Effectiveness
Moderate initial cost, long-term
savings
High recurring costs
5. Key findings
1. Improved threat detection: OSINT systems based on AI have proven to be comparatively efficient viz-a-viz
traditional mechanisms in their threat intelligence usage. Also, early warning enabled organizations to avert an
unprecedented misfortune as long before it occurred.
2. Enhanced proactivity: Using real-time analytics and predictive models, proactive responses were sparked
from the paradigm of investigation switch from reactive to that of prevention.
3. Efficiency in resource allocation: Automating data gathering and analysis has minimized investigators'
burdens and left them free for more important matters.
4. Challenges addressed: The tools of AI OSINT have raised that challenge in managing the mountains of data,
especially with hidden pattern recognition on unstructured information.
6. Limitations
Despite their advantages, AI-driven OSINT tools face challenges:
• False Positives: While improved, some cases exhibited false positives that required manual verification.
• Ethical Concerns: Real-time monitoring of public platforms raised privacy concerns, necessitating
compliance with legal frameworks.
• Complex Implementation: Initial deployment of AI systems required significant expertise and resources.
7. Discussion
The entry of AI and OSINT into the arena of digital forensics signifies one of the paradigmatic changes in the
investigation of cybercrime. The implications that are borne by studying their case studies and findings spell
out the transformational power of such technologies for purposes of understanding the challenges they would
need to undergo. This section presents larger inferences drawn from AI-based OSINT applications including
discussion regarding the applications' merits and disadvantages, ethical considerations, and probable future
directions.
7.1 Strengths of AI-driven OSINT in digital forensics
Advanced pro-activity: A very significant feature of the AI-based OSINT is that it changes the paradigm of
criminal investigations into an already reactive position to one which is proactive. AI will assist with not only
real-time monitoring, prediction analytics, and threat detection on the current intelligence being analyzed but
will also have a predictive capability to foresee possible attacks so that actions can be taken to prevent them.
Scalability and efficiency: The conventional premise of digital forensics faces interesting challenges owing to
the vast data production of this digital age. Artificial Intelligence in OSINT would remove this artificially imposed
barrier by automating data collection and analysis so that scaling up becomes possible alongside minimal
manual effort. This has enabled greater deployment of resources by investigators for higher-priority tasks
which might include prosecuting cybercriminals or securing vulnerable systems.
Enhanced accuracy and insights: The AI-OSINT based on machine learning and natural language processing can
highlight patterns and connection that human analytical minds might never identify. More importantly,
enhanced accuracy in a threat's existence is ensured so that the available information is actionable; hence,
insights are generated about decision-making.
Broad applicability: It also provides the tools to be deployed on many different kinds of domains including
corporate cybersecurity, law enforcement, and national defense, as its versatility allows for it to observe any
source through social media, forums, or dark web networks in real-time for monitoring a plethora of cyber
threats.
7.2 Limitations and challenges
False positives and bias: Although AI-driven systems are highly accurate, they cannot be completely right. False
positives are still present, as with the phishing detection case study when a small portion of benign e-mails were
caught incorrectly. This is also where there is a presence of biasing in the data used for the training of an AI
model to produce skewed outcomes.
Ethical and privacy issues: It raises additional questions regarding privacy and surveillance when it comes to
acquiring and processing publicly available information. AI OSINT tools for collection must comply with data
protection legislation, such as the GDPR and CCPA, to avoid violation of individual rights. Much care should be
taken to balance both possible effectiveness in cybercrime prevention and ethical responsibility. Their use
requires many different technical skills and resources, as well as an infrastructure. The smaller organizations
or law enforcement agencies that do not have budget allocations for AI-powered OSINT tools will find it almost
impossible to adopt.
Evasion techniques by cyber criminals: As the use of AI increases, so the chances are that cybercriminals will
construct their strategies to evade detection, be it by enciphered channels or by creating dummy data. Such a
zero-sum game requires perpetual advancement in the efficacy of AI and also OSINT technologies.
7.3 Ethical and legal issues
Transparency and accountability: To be held accountable, AI must provide transparency in its decision-making.
Therefore, an investigator must explain and justify how AI-dependent tools achieve their conclusions,
particularly in a court of law.
Cross-border data challenges: The cybercrime investigation frequently involves data held in several
jurisdictions where different legal frameworks apply. As a result, an international regulation harmonization is
required for smooth collaboration and data-sharing.
Fairness and non-discrimination: From a moral and legal point of view, it is imperative that AI models refrain
from targeting specific individuals or groups disproportionately. This can also be achieved through constant
auditing and refining of algorithms.
7.4 Future directions
Advancing AI technologies: Future research should work towards powerful AI models that can deal with all sorts
of data, produce less false positives, and adapt to dynamic attacks over time.
Ethical AI development: By integrating morals into the design and use of such tools, the public will be assured
of trust in the delivery mechanism and the lawyers declared as it goes along.
Collaboration and knowledge sharing: Collaboration among governments, private organizations, and academia
will encourage innovation and contribute to improving the efficiency of AI driven OSINT. Sharing knowledge of
what threats terminate, as well as best practices, will benefit the larger community of cyber security.
8. Conclusion
The shift toward including Artificial Intelligence (AI) and Open-Source Intelligence (OSINT) in digital forensics
and cybercrime investigations is critical. This article would address and describe how AI-enabled OSINT can
assist the investigation of cybercrime into a new proactive one. Automated collection and analysis of data and
patterns from large data sets combined with the unintendedly on-the-ground help of actionable intelligence
may facilitate the investigation of cybercrime with greater efficiency and effectiveness. The case studies
discussed above exemplify the use of such tools in faster and more effective identification of phishing campaigns,
dark web activities, and coordinated cyberattacks than the traditional methods. Since the shift is from a reactive
model to a more proactive one, organizations and law enforcement agencies can act to neutralize threats before
they gather more impetus, thereby inflicting further damage to finances, business reputation, and society in
general. AI-driven OSINT is rich in benefits, but adoption challenges are not to be put aside. The ethical issues,
including data privacy and the potential misuse of surveillance technologies, require strict adherence to
regulatory frameworks like GDPR and CCPA. Technical issues include algorithmic bias and resource
intensiveness. Finally, since cybercriminals, too, will evolve to avoid their detection, the cybersecurity
professional community shall continue to innovate and work together. To realize the full benefits of AI-enabled
OSINT in the future, AI algorithms will have to be matured, ethical AI developments put in place, and
partnerships formed across sectors. Governments, private organizations, and academic institutions should
work together to formulate standard practices, share threat intelligence, and advocate for responsible usage. AI
systems, in particular, will need to show transparency and accountability in order to gain public trust and
acceptance for use in law. AI-enabled OSINT is revolution forensics never witnessed in the past. It enables
investigators to be far wiser in the increasingly intricate and dynamic cyber threat landscape. Challenges are
reality yet, they are far too few to weigh against opportunities in deploying AI for protecting individuals,
organizations, and critical infrastructures against a heavily growing menace of cybercrime. Ethical innovation
and overcoming implementation barriers to this must make it an indispensable pillar of any modern-day
cybersecurity effort.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The datasets and supporting analytical results generated and/or analyzed during this study are available from
the corresponding author upon reasonable request.
Conflict of Interest
There is no conflict of interest.
Artificial Intelligence (AI) Use Disclosure
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the
writing or editing of the manuscript and no images were manipulated using AI. All technical content, analysis,
and interpretations were independently developed and verified by the authors.
Supporting Information
Not applicable.
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