Aid Of Artificial Intelligence Research Articles

Dynamics of an AI-Driven Mathematical Model of Some Learning Theories

Objectives. This paper delves into the intersection of mathematical modelling and learning theory, exploring how mathematical frameworks have been instrumental in advancing our understanding of educational processes and pedagogical practices with the aid of Artificial Intelligence (AI). Material and methods. Advancing learning theories through mathematical modelling offers several significant benefits and opportunities. This is because mathematical models provide a precise and systematic framework for representing complex educational phenomena, allowing researchers to formalize theoretical concepts, relationships, and mechanisms in a clear and rigorous manner. Results and Conclusions. This paper focused on such learning theories as cooperative learning theory, Maslow motivation theory, and social learning theory. By employing mathematical models, researchers have been able to dissect complex educational phenomena, elucidate underlying mechanisms, and predict outcomes with a level of precision that traditional qualitative approaches often struggle to achieve.

Scope and caveats: Artificial intelligence in gastroenterology

The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.

Comprehensive Review on the Virulence Factors and Therapeutic Strategies with the Aid of Artificial Intelligence against Mucormycosis.

Mucormycosis, a rare but deadly fungal infection, was an epidemic during the COVID-19 pandemic. The rise in cases (COVID-19-associated mucormycosis, CAM) is attributed to excessive steroid and antibiotic use, poor hospital hygiene, and crowded settings. Major contributing factors include diabetes and weakened immune systems. The main manifesting forms of CAM─cutaneous, pulmonary, and the deadliest, rhinocerebral─and disseminated infections elevated mortality rates to 85%. Recent focus lies on small-molecule inhibitors due to their advantages over standard treatments like surgery and liposomal amphotericin B (which carry several long-term adverse effects), offering potential central nervous system penetration, diverse targets, and simpler dosing owing to their small size, rendering the ability to traverse the blood-brain barrier via passive diffusion facilitated by the phospholipid membrane. Adaptation and versatility in mucormycosis are facilitated by a multitude of virulence factors, enabling the pathogen to dynamically respond to various environmental stressors. A comprehensive understanding of these virulence mechanisms is imperative for devising effective therapeutic interventions against this highly opportunistic pathogen that thrives in immunocompromised individuals through its angio-invasive nature. Hence, this Review delineates the principal virulence factors of mucormycosis, the mechanisms it employs to persist in challenging host environments, and the current progress in developing small-molecule inhibitors against them.

History of Education Meets Digital Humanities: A Field-Specific Finding Aid to Review Past and Present Research

ABSTRACT Research in the field of History of Education has experienced a remarkable increase in recent decades. Resulting publications are referenced in generalist databases that do not catalogue academic works according to the specific characteristics of History of Education. Seeking to give response to this bibliographic gap, we are developing a database catered for historians of education that aims to map out present, past, and future research. Conceived within the framework of Digital Humanities/Digital History, Hecumen is being designed, with the aid of Artificial Intelligence, as an open access finding aid that permits (1) conducting specific and multilevel complex engine searches, (2) having a panoramic view of publications; (3) mapping out relevant/missing areas of research, and, ultimately, (4) keeping up to date with the research produced by historians of education. This paper presents, contextualises, and problematises Hecumen – a digital tool that will facilitate and boost History of Education research.

The impact of artificial intelligence on medical diagnostics: A letter to the editor

Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing. (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---Continue

“They created segregation with the economy”: Using AI for a student-driven inquiry into redlining in the social studies classroom

ABSTRACT This article investigates students’ engagement with a historical inquiry into redlining—a practice of discriminatory lending that originated in the 1930s as part of the New Deal. The authors developed and implemented a week-long curricular intervention for high school sophomores using StoryQ—an Artificial Intelligence (AI) textual modeling platform designed for high school students without technical expertise—to examine hundreds of neighborhood descriptions produced for the Home Owners Loan Corporation’s “residential security maps” in the late 1930s. In this article, we ask: What kinds of historical and present-day racial awareness do high school students demonstrate through instruction focused on AI-assisted analysis of patterns in redlining? Analyzing field notes, interviews, and student-generated digital work showed that many students were drawn to structural explanations of racism and worked to unpack the way primary sources presented Whiteness through “coded language.” We argue that it is not only possible for teachers to construct historical inquiries that aim to identify patterns in a large set of primary sources with the aid of AI, but this approach to inquiry offers students an important avenue to engage with alternatives to individual conceptions of racial oppression.

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Presented to Kepler Ulianov Proton Tree, A Way to Distribute Protons and Neutrons Within the Atomic Nucleus. First part: From Hydrogen to Krypton

This work presents a novel approach to understanding the formation and structure of the 36 smallest known atomic isotopes, ranging from hydrogen to krypton, through the lens of the KUPT model. Focusing on the Small Known Isotope (SKI) of each atom, this study meticulously examines the distribution of protons across five nuclear layers (1E, 2T, 3T, 4E, and 4C), constructing a proton tree that spans from hydrogen to krypton atoms. With the aid of Artificial Intelligence, specifically Chat GPT-4, an in-depth analysis was conducted for each atom within the KUPT model framework. This process involved defining minimum stability rules for the nuclear structure, particularly regarding neutron inclusion, across the model’s first five layers. Remarkably, in 35 out of 36 cases, the KUPT model’s predictions aligned perfectly with the SKI data, underscoring the model’s accuracy and reliability. However, an intriguing discrepancy emerged in the case of the Gallium atom (62Ga), suggesting the possible existence of an unknown, lighter isotope with two or three fewer protons (59Ga or 60Ga). This discrepancy not only highlights the KUPT model’s predictive capability but also points to a fertile ground for further experimental investigation, aiming to uncover potentially undiscovered isotope forms of Gallium.

Artificial Intelligence in Hand Surgery - How Generative AI is Transforming the Hand Surgery Landscape.

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft's collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery. Level of Evidence: Level V (Diagnostic).

Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS

Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS

Integration and transformation: The impact and applications of artificial intelligence in the financial sector

This paper explores the intersection of artificial intelligence (AI) and the financial sector, showcasing their transformative synergy. The integration of AI into finance has led to pioneering advancements like robo-advisors and AI-driven risk assessment methods. These innovations reshape investment strategies and risk management, ushering in a new era of financial operations. The study's focal question examines how AI recalibrates investment management, risk assessment, and fraud prevention in finance. The paper comprises sections on AI's impact on investment management, risk assessment, and fraud detection, detailing how robo-advisors provide personalized portfolio recommendations, AI aids risk identification and management, and transaction surveillance benefits from AI-powered fraud detection. Ethical, regulatory, and accountability considerations are discussed, reflecting AI's transformative influence on traditional financial paradigms. The application of AI in transaction detection and its role in enhancing portfolio recommendations, risk management, and automated trading are examined. While AI holds potential, its limitations such as data quality, model risks, and ethical concerns must be addressed. Regulatory oversight is crucial to ensure responsible AI implementation, fostering a balance between technological progress and financial stability. This paper underscores the intricate relationship between AI and finance, portraying AI's capacity to reshape the financial landscape and drive innovation

The Future of Cystic Fibrosis Care: Exploring AI's Impact on Detection and Therapy

Abstract: Cystic Fibrosis (CF) is a fatal hereditary condition marked by thicker mucus production, which can cause problems with the digestive and respiratory systems. The quality of life and survival rates of CF patients can be improved by early identification and individualized therapy measures. With an emphasis on its applications in diagnosis and therapy, this paper investigates how Artificial Intelligence (AI) is transforming the management of Cystic Fibrosis (CF). AI-powered algorithms are revolutionizing CF diagnosis by utilizing huge genetic, clinical, and imaging data databases. In order to identify CF mutations quickly and precisely, machine learning methods evaluate genomic profiles. Furthermore, AI-driven imaging analysis helps to identify lung and gastrointestinal issues linked to cystic fibrosis early and allows for prompt treatment. Additionally, AI aids in individualized CF therapy by anticipating how patients will react to already available medications and enabling customized treatment regimens. Drug repurposing algorithms find prospective candidates from already-approved drugs, advancing treatment choices. Additionally, AI supports the optimization of pharmacological combinations, enhancing therapeutic results while minimizing side effects. AI also helps with patient stratification by connecting people with CF mutations to therapies that are best for their genetic profiles. Improved treatment effectiveness is promised by this tailored strategy. The transformational potential of artificial intelligence (AI) in the field of cystic fibrosis is highlighted in this review, from early identification to individualized medication, bringing hope for better patient outcomes, and eventually prolonging the lives of people with this difficult ailment.

Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists.

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.

Advancing total knee replacement surgery Assessment with Wearable Sensors and AI: A Case Study

Background: India has a higher prevalence of about 20-24%.of people suffering from osteoarthiritis. Due tothe load effect, forces between two and three times body weight are transmitted across the knee joint during anormal stride, which accounts for the higher risk of OA.A Qualitative Analysis of Decision-Making for Total KneeReplacement in Patients with Osteoarthritis. A cutting-edge wearable sensor system called Fitknees deliversa complex arrangement of motion sensors that are carefully placed on the lower limb. These sensors outlinethorough kinematic data that include gait analysis, muscle strength, knee range of motion, and balance metrics.Objective: Utilizing individualised measurements, using wearable sensors and AI tool to assess preoperativeevaluations and postoperative rehabilitation programs.Results: camping between affected and unaffected side with the normative data provided by the AI.Conclusion:advancement in the medical technologies have made it easier for medial professionals to detect and early diagnosethe diseases. Plan a better treatment plan with the aid of artificial intelligence to achieve better results.

Prologue for a “Weak”

This essay offers the first graphic arguments as a prologue to identify a “weak”: a neologism proposed to classify a moment in the Biobío River delta that the authors see as the architectural antithesis to the military fortifications that have historically categorized this body of water as a border, a word that has been rooted in the Chilean colonial national imaginary fundamental to our identity. Methodologically, the authors use graphics to explore the possibilities of a photographic disarticulation of this space, and the creation of alternative imaginaries with the aid of artificial intelligence, giving way to a new “misty” artifact that positions itself against the projective arguments of the established architectural narrative.

Awareness of medical students about the role of artificial intelligence in health care: A survey-based cross-sectional study

Background: Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially by computers. In the recent past, the utility of AI has been tested and proven in many fields like health care, education, finance, defense, etc. AI has received so much attention these days and is described as the new electricity and the fourth industrial revolution. It is anticipated that AI’s role will be crucial and imminent soon in different fields, including the health sector. The medical graduates need to update their knowledge and keep pace with the new developments in technology. Given this, our study was designed to evaluate the awareness and perception of medical students regarding the basic concepts of AI and its implications in health care. Aim and Objective: The aim and objective of the study are to assess the knowledge and perceptions of medical students about AI at a tertiary care teaching hospital. Materials and Methods: The study was a questionnaire-based, cross-sectional study. A prevalidated, self-made questionnaire was distributed to 264 medical students of all professional years through Google Forms. The student’s knowledge, opinions, and perceptions of AI were evaluated. The analysis of the collected data was done with an Excel sheet and using descriptive statistical measures such as mean and percentages. Results: Our study revealed that the majority of the students (79.16%) were under the impression that AI decreases errors in clinical practice. 86% of participants agreed that AI aids doctors in making accurate decisions in the diagnosis and treatment of different conditions. Around 72% of the students said that AI guides the patient to choose the right medical care. Onethird of the participants thought that AI hampers the relationship between patient and doctor by breaking trust between them. A few subsets of participants expressed concerns over ethical issues, confidentiality, empathy, and sympathy, which are considered to be the pillars of medical practice. Conclusions: Our study concluded that medical students at Mamatha Academy of Medical Sciences, Hyderabad, have positive perceptions and good awareness about AI and its use in health-care practice, but they still need a better understanding of AI.

Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence.

This narrative review examines the evolving landscape of artificial intelligence (AI) integration in skull base surgery, exploring its multifaceted applications and impact on various aspects of patient care. Extensive literature review was conducted to gather insights into the role of AI in skull base surgery. Key aspects such as diagnosis, image analysis, surgical planning, navigation, predictive analytics, clinical decision-making, postoperative care, rehabilitation, and virtual simulations were explored. Studies were sourced from PubMed using keyword search strategy for relevant headings, sub-headings and cross-referencing. AI enhances early diagnosis through diagnostic algorithms that guide investigations based on clinical and radiological data. AI-driven image analysis enables accurate segmentation of intricate structures and extraction of radiomics data, optimizing preoperative planning and predicting treatment response. In surgical planning, AI aids in identifying critical structures, leading to precise interventions. Real-time AI-based navigation offers adaptive guidance, enhancing surgical accuracy and safety. Predictive analytics empower risk assessment, treatment planning, and outcome prediction. AI-driven clinical decision support systems optimize resource allocation and support shared decision-making. Postoperative care benefits from AI's monitoring capabilities and personalized rehabilitation protocols. Virtual simulations powered by AI expedite skill development and decision-making in complex procedures. AI contributes to accurate diagnosis, surgical planning, navigation, predictive analysis, and postoperative care. Ethical considerations and data quality assurance are essential, ensuring responsible AI implementation. While AI serves as a valuable complement to clinical expertise, its potential to enhance decision-making, precision, and efficiency in skull base surgery is evident.

Attitude, Awareness, and Understanding of Artificial Intelligence AI among Medical and Dental Students in Jordan: A Cross-Sectional Study

BACKGROUND: The rapid growth of AI has transformed various sectors, including healthcare. AI aids medical diagnoses and treatment planning, but challenges such as privacy and ethical concerns exist. Understanding AI is crucial for medical and dental students in Jordan to enhance patient care. This study examines students' attitudes, awareness, and understanding of AI to inform effective education and better healthcare practices. METHODS: A cross-sectional study was conducted in Jordan among medical and dental students from Nov 2022 to Mar 2023. Data were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis tests, and Spearman's correlation. Ethical approval was obtained, and SPSS was used for analysis. RESULTS: The study involved 800 respondents, predominantly female (54.6%) and with an average age of 19.81 years. Most participants were medical students (64.6%), had a national diploma (75.0%), and were in their first or second year of education (73.1%). The majority were single (97.9%), and 75.1% resided in the central region. In terms of technology background, 38.8% had family members in the technology sector, and half used electronic devices for 3-6 hours daily. Over 75% used electronic devices for studying, while 44.1% had taken technology courses, with only 23.5% having specifically studied Artificial Intelligence (AI). Additionally, 46.0% had prior research experience, but only 18.0% had participated in AI-related research. Participants rated their overall technology knowledge at 2.91 ± 1.06 and AI knowledge at 2.43 ± 1.21. The main sources of AI information were social media (35.0%). Regarding AI applications, 28.0% believed it would enhance medical diagnosis, while 24.0% expressed concerns about hacking and cybersecurity. Only 10.0% planned to consistently incorporate AI into their future medical practice. Statistical tests revealed significant differences in awareness of AI based on study field (p-value = 0.034), income (p-value = 0.028), previous technology involvement (p-value = 0.004), ICDL course participation (p-value = 0.02), AI course participation (p-value &lt; 0.001), and programming language learning (p-value = 0.001). Similar variations were observed in understanding AI basic principles, with additional significance related to research course participation (p-value &lt; 0.001). Attitudes toward AI differed only among those with previous technology course involvement (p-value = 0.037). Spearman's correlation indicated a moderate positive association between awareness of AI in medicine and dentistry and understanding of AI basic principles (r: 0.491, p &lt; 0.001). CONCLUSION: The younger generation of medical and dental professionals’ views AI as a collaborator in their practice. Strong interest and active engagement in AI-related courses highlight the importance of integrating AI education into medical curricula for innovative healthcare practices.

AI Take-Over in Literature and Culture: Truth, Post-Truth, and Simulation

In a world that is increasingly lost to narrative building, deep fakes, simulation of realities and dissemination of fake news generated by artificial intelligence (AI), we are moving towards a post-truth era. Our thoughts are being manipulated and twisted with (mis)information for the benefit of people in power; thus, our consent is being manufactured with the aid of AI, resulting in ideological imperialism. In such a scenario, when AI is slowly taking control over the planet and creating our digital replicas by cloning our consciousness, what will our future look like? Humans’ creative pursuits have already predicted such futures in various movies, comics, novels and web series depicting the myriad complications associated with an impending AI takeover. Thus, contextualising today’s scenario within the scope of the future, the paper aims to dissect some popular speculative narratives offered through various tissues of culture, including movies, comics, novels and web series, to comprehend the consequences those narratives generate to grasp the changing relationalities between real and unreal, and truth and post-truth in a world run in codes and simulation.

CD8-Lymphocytic Phenotype Significance in Primary Multiple and Familial Melanoma with Various CDKN2A Mutational Status.

Background and Objectives: In the realm of the rising incidence of cutaneous and mucous melanoma, CDKN2A mutations characterize familial and multiple primary melanoma cases. The involvement of tumor-infiltrating lymphocytes (TILs) is interconnected with survival rates, but may extend even further. The aim of this study is to verify the accuracy of the classical "naked eye" count of CD8-positive T cells comprised within the tumoral population and peritumoral infiltrate versus that obtained via a special software run by the aid of artificial intelligence (AI), used to determine the percentage of CD8-positive TILs. Materials and Methods: The present retrospective cross-sectional study conducted over a period of 5 years (2018-2022) focused on patients diagnosed with mucous and/or cutaneous melanoma, with a positive family history for melanoma, or personal antecedents of primary malignant melanocytic lesions. The 23 selected cases were diagnosed histopathologically, tested for CDKN2A mutations through fluorescent hybridization in situ, and CD8 immunohistochemistry was performed. The included slides were evaluated both manually (naked-eye examination) and automatically (via QuPath platform) for quantifying the CD8-positive TILs. Results: The number of CD8-positive TILs in melanoma samples has been more accurately identified through the use of an AI-mediated software as compared to the human-eye evaluation performed by experimental pathologists. A higher percentage of CD8-positive intratumoral lymphocytes versus stromal lymphocytes was positively associated with more numerous metastatic sites. Conclusions: The CD8 lymphocytic phenotype harbors major significance in the context of familial and multiple primary melanoma and may comprise a cost-effective investigation meant to help in the establishment of melanoma prognosis and response to immunotherapy.