Artificial Intelligence Research Articles

Generative Artificial Intelligence, With Constrained Information, Outperforms Pre-Doctoral Student Average on Oral Pathology Differential Diagnosis Questions.

Artificial intelligence (AI) technologies have seen rapid advancement and are increasingly used in healthcare fields, including clinical diagnostics and dental education. Despite their growing prominence, their effectiveness in assisting clinical decision-making in dental education remains under-explored. This study examined the performance of Generative AI in generating a clinical impression for oral pathology cases relative to dental students. The aim of this experiment was to assess the diagnostic accuracy and potential difference of Generative AI in clinical oral pathology compared to that of Doctor of Dental Surgery (DDS) students. A clinical oral pathology differential diagnosis exam was administered to both an AI model and DDS students. The AI model received limited information about each case, while the DDS students were provided with standard case details and a multiple-choice selection. The accuracy and statistical significance between both groups were compared and evaluated. The AI model displayed higher diagnostic accuracy compared to the students, 95.65% to 78.92%, respectively, and the difference in groups was statistically significant. The findings suggest that Generative AI has the potential to be a valuable tool in clinical oral pathology, even when provided with minimal case information. Its superior diagnostic performance compared to DDS students highlights prospective benefits of incorporating AI into dental education and specifically in helping students formulate clinical impressions.

Do job candidates fake to AI? An impression management theory perspective business vs ethical case of DEI and intention to fake in job interview

Purpose This study, leveraging impression management theory, aims to evaluate if job candidates are more likely to fake interviews when interviewed by artificial intelligence (AI) under the moderating influence of their personality and the issuance of a deception warning. Design/methodology/approach This study used MANCOVA, followed by mediation and moderated mediation analyses, to test the hypotheses. Findings This study’s findings suggest that the presence of AI as interviewing agents increases job applicants’ intention to fake job interviews, with the perceived ability of AI vs humans to detect fakeness as a mediator. The conscientiousness of job applicants and warning issuance by the Chief HR officer or CEO moderates the relationship. Originality/value With the advancement of information technology tools such as AI, job interviews in firms are taken over by AI more than humans. This study pioneers research into the potential for increased faking behaviour by job candidates when interacting with AI interviewers. The current study is also one of the pioneering studies, shining a light on the misuse of IT systems in human resource management practices of organisations.

Understanding Transient Left Ventricular Ejection Fraction Reduction During Atrial Fibrillation With Artificial Intelligence.

Atrial fibrillation (AF) can cause a reduction in left ventricular ejection fraction (LVEF) that resolves rapidly upon restoration of sinus rhythm. We used artificial intelligence to understand (1) how often transient LVEF reduction during AF is from mismeasurement due to AF's beat-to-beat variability and (2) whether true transient AF-LVEF reduction has prognostic significance. In this observational study, we analyzed all patients at a large academic center with a transthoracic echocardiogram in AF and subsequent transthoracic echocardiogram in sinus rhythm within 90 days. We classified patients by their clinically reported LVEFs: no AF-LVEF reduction, transient AF-LVEF reduction that recovered after conversion to sinus rhythm, or persistent AF-LVEF reduction that did not recover. We evaluated how automated multicycle AF-LVEF measurement using a validated artificial intelligence algorithm affected AF-LVEF and reclassified patients. We used Fine-Gray hazard modeling to analyze 1-year heart failure hospitalization risk. In 810 patients (mean age 74.1 years, 34.3% female), 459 (56.7%) had no reduced AF-LVEF, 71 (8.8%) had transient AF-LVEF reduction, and 280 (34.6%) had persistent AF-LVEF reduction. In the group with transient AF-LVEF reduction, LVEF increased by 19.5% (95% CI, 12.0%-22.1%) upon conversion to sinus rhythm. AI reassessment increased AF-LVEF by 8.2% (95% CI, 6.0%-10.4%), reclassifying 20 (28.2%) patients as no longer having reduced AF-LVEF. The group with transient AF-LVEF reduction, as determined by AI, had significantly higher 1-year heart failure hospitalization risk (hazard ratio, 2.28 [95% CI, 1.23-4.21], P=0.003). Artificial intelligence may decrease misdiagnosis of reduced LVEF during AF and more accurately identify true transient AF-LVEF reduction, a potentially high-risk phenotype.

Artificial Intelligence in Sincalide-Stimulated Cholescintigraphy: A Pilot Study.

Sincalide-stimulated cholescintigraphy (SSC) calculates the gallbladder ejection fraction (GBEF) to diagnose functional gallbladder disorder. Currently, artificial intelligence (AI)-driven workflows that integrate real-time image processing and organ function calculation remain unexplored in nuclear medicine practice. This pilot study explored an AI-based application for gallbladder radioactivity tracking. We retrospectively analyzed 20 SSC exams, categorized into 10 easy and 10 challenging cases. Two human operators (H1 and H2) independently annotated the gallbladder regions of interest manually over the course of the 60-minute SSC. A U-Net-based deep learning model was developed to automatically segment gallbladder masks, and a 10-fold cross-validation was performed for both easy and challenging cases. The AI-generated masks were compared with human-annotated ones, with Dice similarity coefficients (DICE) used to assess agreement. AI achieved an average DICE of 0.746 against H1 and 0.676 against H2, performing better in easy cases (0.781) than in challenging ones (0.641). Visual inspection showed AI was prone to errors with patient motion or low-count activity. This study highlights AI's potential in real-time gallbladder tracking and GBEF calculation during SSC. AI-enabled real-time evaluation of nuclear imaging data holds promise for advancing clinical workflows by providing instantaneous organ function assessments and feedback to technologists. This AI-enabled workflow could enhance diagnostic efficiency, reduce scan duration, and improve patient comfort by alleviating symptoms associated with SSC, such as abdominal discomfort due to sincalide administration.

Influence of artificial intelligence on consumers’ lifestyle product choices and the key to driving sustainable behaviour

Purpose The purpose of this study is to determine the impact of artificial intelligence (AI) experience on sustainable behaviour with the mediating roles of perceived value, customer engagement and purchase intention. Design/methodology/approach A quantitative survey was done with 421 young consumers in the Delhi-NCR region. Data were analysed using SPSS and AMOS using the PLS-SEM model, which included descriptive analysis, reliability and validity checks, model fitness assessment and hypothesis testing. Findings The study found a significant impact of AI experience on perceived value and a significant role of perceived value as a mediating variable in the impact of perceived value and customer engagement on sustainable behaviour. Finally, while the study also found a presence of the mediating role played by perceived value in the impact of AI experience on purchase intention, it did not find any impact of AI experience on customer engagement. Research limitations/implications The findings could help organisations promote sustainability through AI-driven initiatives that correspond with young consumers’ inclinations. Furthermore, academicians can use the findings to further explore the dynamics of these relationships, with the additional impact of demographic factors such as age and gender as moderators. Originality/value AI is rapidly affecting consumer purchasing decisions, particularly for lifestyle products. It can be concluded now that AI can help organisations promote sustainability through AI-driven initiatives that correspond with young consumers’ inclinations. Furthermore, this study promotes improved understanding among academicians who can use the findings to further explore the dynamics of these relationships, with the additional impact of demographic factors such as age and gender as moderators.

Who’s the better mentor? How AI vs human supervisor developmental feedback influences feedback acceptance

PurposeWith advancements in machine learning and large language models, artificial intelligence (AI) is increasingly assuming an autonomous supervisory role in process governance. However, prior research in business process management (BPM) has not sufficiently examined how AI influences employee outcomes in this context. Addressing this critical gap, our study explores when – and why – employees accept developmental feedback from AI supervisors compared to human supervisors.Design/methodology/approachTwo experimental studies were carried out. Study 1 was a 2 × 2 scenario-based experiment. Study 2 adopted a quasi-experimental design to augment external validity and generalizability.FindingsThis study found that for employees with strong beliefs in anthropocentrism, AI developmental feedback decreases their perception of the supervisor’s agency as well as their perception of the supervisor’s experience, ultimately leading to reduced feedback acceptance.Originality/valueThis study sheds light on the underexplored topic of AI-involved process governance within BPM and reveals the potential drawbacks of AI-generated developmental feedback. Additionally, by incorporating the concept of perceived mind into the mechanism of developmental feedback and feedback acceptance, this study expands the literature on mind perception theory and its operational boundaries.

Are humans still necessary? Expanding the discussion

The rise of automation, artificial intelligence (AI), and autonomous systems raises important questions about the future role of humans and the field of human factors/ergonomics in workplaces. This paper builds on Dr. Peter Hancock’s 2023 ‘Are Humans Still Necessary?’ article published in the Ergonomics journal. Using a multi-method approach that included a debate, opinion polling, roundtable discussions, and AI queries, the current effort examined the necessity of human involvement in future work environments. Debate team members presented arguments for and against the need for human workers, considering human factors, technology, and socioeconomic factors. Observations indicate that while AI may handle routine tasks, humans will likely remain essential for complex decision making, creativity, and ethical considerations. The paper advocates for viewing workplace dynamics as collaborative human-AI partnerships rather than competition, highlighting the need for a transdisciplinary approach in which human factors/ergonomics professionals play a vital role in enhancing these relationships.

Artificial intelligence for chronic total occlusion percutaneous coronary interventions.

Artificial intelligence (AI) has become pivotal in advancing medical care, particularly in interventional cardiology. Recent AI developments have proven effective in guiding advanced procedures and complex decisions. The authors review the latest AI-based innovations in the diagnosis of chronic total occlusions (CTO) and in determining the probability of success of CTO percutaneous coronary intervention (PCI). Neural networks and deep learning strategies were the most commonly used algorithms, and the models were trained and deployed using a variety of data types, such as clinical parameters and imaging. AI holds great promise in facilitating CTO PCI.

Empowering Future Dentists: A Comprehensive Mixed-Methods Exploration of Artificial Intelligence in Personalizing Year 3 Clinical Dental Practice Education.

Dental education often struggles to bridge the gap between theoretical knowledge and clinical application. Traditional teaching methods may fail to meet individual learning needs, potentially impacting student performance and confidence. Artificial intelligence (AI)-driven platforms like Gemini offer personalized learning pathways and real-time feedback, which could enhance educational outcomes. This study investigates the integration of Gemini to personalize Year 3 dental students' learning experience. The study assesses its impact on formative and summative assessments, as well as the experiences of students and faculty. An explanatory sequential mixed-methods design was used, involving 46 Year 3 BDS students and six faculty members. Quantitative data were collected through pre- and post-implementation assessments, while semi-structured interviews provided qualitative insights into user experiences. Quantitative analysis revealed a 15% mean increase in formative assessment scores post-Gemini integration (p < 0.05, Cohen's d = 0.7), with the largest observed gains in modified essay questions (MEQs). Summative assessments showed a 5% increase, though this difference was not statistically significant (p = 0.08, Cohen's d = 0.3). A strong positive correlation (r = 0.62; p < 0.01) was found between Gemini usage and student performance. Thematic analysis of interview data identified key themes, including initial technical challenges, increased student engagement, the value of personalized feedback, and suggestions for expanding the platform's use to other modules. Formative assessment gains were statistically significant (p < 0.05), reinforcing the effectiveness of AI-driven adaptive learning. However, the 5% increase in summative assessments was not statistically significant (p = 0.08), suggesting that AI platforms alone may not fully address the demands of high-stakes evaluations. This underscores the need for complementary educational strategies, such as simulation-based learning and structured clinical discussions. The study highlights the importance of thorough faculty training for the effective integration of AI tools. Gemini demonstrated potential in enhancing formative learning and student engagement, indicating its broader applicability in dental education.

The Wicked Nature of AGI

Artificial general intelligence (AGI) represents an unprecedented ambition within the field of technology, aiming to create systems capable of matching or surpassing human abilities across multiple domains. Unlike Artificial Narrow Intelligence (ANI), AGI is anticipated to operate without task-specific limitations and predefined purposes, raising complex, pressing issues surrounding autonomy, control and potential societal impact. This article applies Rittel and Webber’s wicked problem theory to critically examine AGI governance, categorising AGI within the ten characteristics of wicked problems. The absence of a definitive formulation, its unstoppable potential evolution, the subjective and context-dependent nature of its solutions, the irreversibility of interventions and the multiplicity of stakeholder perspectives all underscore the inadequacy of existing governance paradigms. In response, this article advocates for dynamic, iterative and flexible governance frameworks that acknowledge AGI’s ontic uniqueness and potential for autonomous evolution. Rather than treating AGI as a distant or hypothetical concern, this analysis argues for a multidimensional, forward-looking governance model that recognises AGI as an urgent and inherently wicked problem.

Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet; therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min–max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole‐rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad‐CAM is employed as an XAI technique to enhance transparency by providing human-understandable insights into their decision-making processes. A broad array of experiments using the LXAIOA-ADPCM technique is performed under the Dementia Prediction dataset. The simulation validation of the LXAIOA-ADPCM technique portrayed a superior accuracy output of 95.71% over existing models.

Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis

The rapid spread of SARS-CoV-2 has highlighted the need for intelligent methodologies in COVID-19 diagnosis. Clinicians face significant challenges due to the virus’s fast transmission rate and the lack of reliable diagnostic tools. Although artificial intelligence (AI) has improved image processing, conventional approaches still rely on centralized data storage and training. This reliance increases complexity and raises privacy concerns, which hinder global data exchange. Therefore, it is essential to develop collaborative models that balance accuracy with privacy protection. This research presents a novel framework that combines blockchain technology with a combined learning paradigm to ensure secure data distribution and reduced complexity. The proposed Combined Learning Collective Deep Learning Blockchain Model (CLCD-Block) aggregates data from multiple institutions and leverages a hybrid capsule learning network for accurate predictions. Extensive testing with lung CT images demonstrates that the model outperforms existing models, achieving an accuracy exceeding 97%. Specifically, on four benchmark datasets, CLCD-Block achieved up to 98.79% Precision, 98.84% Recall, 98.79% Specificity, 98.81% F1-Score, and 98.71% Accuracy, showcasing its superior diagnostic capability. Designed for COVID-19 diagnosis, the CLCD-Block framework is adaptable to other applications, integrating AI, decentralized training, privacy protection, and secure blockchain collaboration. It addresses challenges in diagnosing chronic diseases, facilitates cross-institutional research and monitors infectious outbreaks. Future work will focus on enhancing scalability, optimizing real-time performance and adapting the model for broader healthcare datasets.

Recent developments at the intersection of AI and theoretical physics

While machine learning is nowadays used in essentially all natural sciences, the timeline of adaptation in the different disciplines, and the subfields within each discipline, varies widely. Theoretical Physics and Mathematics are among the latest adopters of machine learning techniques, which is in part due to differing requirements that these fields have as compared to others. In this article, we discuss recent developments in artificial intelligence that are specifically geared towards application and scientific discovery in theoretical and mathematical physics. Along the way, we point out other phenomena at the intersection of Physics and AI, including chaos theory, phase transitions, spin glasses, symmetries, classical and statistical mechanics, and quantum theory.

AI-IoT based smart agriculture pivot for plant diseases detection and treatment

There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively.

Bridging chemistry and artificial intelligence by a reaction description language

Bridging chemistry and artificial intelligence by a reaction description language

Evaluation of an artificial intelligence noise reduction tool for conventional X-ray imaging - a visual grading study of pediatric chest examinations at different radiation dose levels using anthropomorphic phantoms.

Noise reduction tools developed with artificial intelligence (AI) may be implemented to improve image quality and reduce radiation dose, which is of special interest in the more radiosensitive pediatric population. The aim of the present study was to examine the effect of the AI-based intelligent noise reduction (INR) on image quality at different dose levels in pediatric chest radiography. Anteroposterior and lateral images of two anthropomorphic phantoms were acquired with both standard noise reduction and INR at different dose levels. In total, 300 anteroposterior and 420 lateral images were included. Image quality was evaluated by three experienced pediatric radiologists. Gradings were analyzed with visual grading characteristics (VGC) resulting in area under the VGC curve (AUCVGC) values and associated confidence intervals (CI). Image quality of different anatomical structures and overall clinical image quality were statistically significantly better in the anteroposterior INR images than in the corresponding standard noise reduced images at each dose level. Compared with reference anteroposterior images at a dose level of 100% with standard noise reduction, the image quality of the anteroposterior INR images was graded as significantly better at dose levels of ≥ 80%. Statistical significance was also achieved at lower dose levels for some structures. The assessments of the lateral images showed similar trends but with fewer significant results. The results of the present study indicate that the AI-based INR may potentially be used to improve image quality at a specific dose level or to reduce dose and maintain the image quality in pediatric chest radiography.

Comparing Diagnostic Accuracy of ChatGPT to Clinical Diagnosis in General Surgery Consults: A Quantitative Analysis of Disease Diagnosis.

This study addressed the challenge of providing accurate and timely medical diagnostics in military health care settings with limited access to advanced diagnostic tools, such as those encountered in austere environments, remote locations, or during large-scale combat operations. The primary objective was to evaluate the utility of ChatGPT, an artificial intelligence (AI) language model, as a support tool for health care providers in clinical decision-making and early diagnosis. The research used an observational cross-sectional cohort design and exploratory predictive techniques. The methodology involved collecting and analyzing data from clinical scenarios based on common general surgery diagnoses-acute appendicitis, acute cholecystitis, and diverticulitis. These scenarios incorporated age, gender, symptoms, vital signs, physical exam findings, laboratory values, medical and surgical histories, and current medication regimens as data inputs. All collected data were entered into a table for each diagnosis. These tables were then used for scenario creation, with scenarios written to reflect typical patient presentations for each diagnosis. Finally, each scenario was entered into ChatGPT (version 3.5) individually, with ChatGPT then being asked to provide the leading diagnosis for the condition based on the provided information. The output from ChatGPT was then compared to the expected diagnosis to assess the accuracy. A statistically significant difference between ChatGPT's diagnostic outcomes and clinical diagnoses for acute cholecystitis and diverticulitis was observed, with ChatGPT demonstrating inferior accuracy in controlled test scenarios. A secondary outcome analysis looked at the relationship between specific symptoms and diagnosis. The presence of these symptoms in incorrect diagnoses indicates that they may adversely impact ChatGPT's diagnostic decision-making, resulting in a higher likelihood of misdiagnosis. These results highlight AI's potential as a diagnostic support tool but underscore the importance of continued research to evaluate its performance in more complex and varied clinical scenarios. In summary, this study evaluated the diagnostic accuracy of ChatGPT in identifying three common surgical conditions (acute appendicitis, acute cholecystitis, and diverticulitis) using comprehensive patient data, including age, gender, medical history, medications, symptoms, vital signs, physical exam findings, and basic laboratory results. The hypothesis was that ChatGPT might display slightly lower accuracy rates than clinical diagnoses made by medical providers. The statistical analysis, which included Fisher's exact test, revealed a significant difference between ChatGPT's diagnostic outcomes and clinical diagnoses, particularly in acute cholecystitis and diverticulitis cases. Therefore, we reject the null hypothesis, as the results indicated that ChatGPT's diagnostic accuracy significantly differs from clinical diagnostics in the presented scenarios. However, ChatGPT's overall high accuracy suggests that it can reliably support clinicians, especially in environments where diagnostic resources are limited, and can serve as a valuable tool in military medicine.

Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.

Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles. International online Delphi study. International experts in medicine and artificial intelligence. The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research). The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool's role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions. GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.

AI-enabled smartwatch user satisfaction among patients with cardiovascular diseases

Purpose This study aims to examine the determinants of smartwatch user satisfaction health monitoring among patients with cardiovascular diseases by using the stimulus–organism–response and the unified theory of acceptance and use of technology. Design/methodology/approach The research used a self-administered online questionnaire collecting 444 valid responses. Structural equation modeling and artificial neural networks were used to analyze the data using AMOS and SPSS software. The model is collated from the literature. Findings The result indicated that performance expectancy, effort expectancy and hedonic motivations directly impact smartwatch satisfaction. In addition to the noted constructs, price value and facilitating conditions were found to indirectly impact satisfaction through trust in artificial intelligence (AI)-enabled devices. Moreover, the findings revealed that social influence and habit directly and indirectly impact smartwatch satisfaction, among patients with cardiovascular diseases, through cyberchondria. Originality/value This study contributes to the literature and practitioners in the healthcare sector by focusing the scope of the research on patients with cardiovascular diseases and AI-enabled smartwatches. The findings provide a pathway for marketers to facilitate health monitoring for heart disease patients. Applying the model can enhance the quality of health monitoring while lowering the costs of the process.

EMERGING TRENDS OF RISK MANAGEMENT IN THE INVESTMENT SECTOR IN INDIA: A REVIEW PAPER

This paper explores the evolving landscape of risk management in India's investment sector, focusing on emerging strategies, technologies, and frameworks used to mitigate financial uncertainties. With rapid digitization, increased regulatory scrutiny, and rising ESG (Environmental, Social, Governance) awareness, the traditional approaches to risk management are undergoing a significant transformation. Drawing from secondary sources, surveys, and institutional practices, the review identifies the primary tools, techniques, and challenges shaping modern investment risk practices. The paper also outlines research gaps and suggests future directions for more robust and adaptive risk frameworks. Key findings highlight the importance of integrating AI, real-time analytics, and sustainable governance models for investor confidence and market stability. By identifying strengths and shortcomings in current practices, the paper aims to provide a comprehensive perspective on the sector's readiness to manage risk proactively rather than reactively. Also, it highlights research gaps related to the uneven adoption of risk technologies and the limited empirical analysis of behavioral risk dynamics. The findings suggest that India's financial ecosystem is transitioning toward a more adaptive, resilient, and transparent model of risk governance, yet requires greater institutional collaboration and innovation to withstand future disruptions. Keywords: Risk Management, Investment Sector, India, Artificial Intelligence, ESG Integration, Financial Technology, Regulatory Compliance, Cybersecurity, Behavioral Finance, Emerging Risks.