What’s new in Academic International Medicine: Artificial intelligence in medical education – A once-in-a-century opportunity to achieve rapid global parity

International Journal of Academic Medicine, Journal Year: 2024, Volume and Issue: 10(2), P. 43 - 46

Published: April 1, 2024

The rapid evolution and mainstream adoption of artificial intelligence (AI) machine learning (ML) capabilities have the potential to transform health-care environment in many, often unexpected ways.[1,2] While high-income economies are well positioned benefit from improved efficiencies greater productivity an AI-enabled workforce,[3,4] there also challenges related not always orderly implementations AI.[3,5] Due inherent structural differences between regions (HIRs) low- middle-income (LMIRs) around globe, considerations specific LMIR-centric AI only unique different but rich with once-in-a-generation transformational opportunities. When it comes AI/ML, medical education (ME) is likely be one most impacted areas broadly defined "health-care" across planet specifically within LMIRs.[6-8] Potential impacts may both positive negative, depending on aspect ME affected. revolution LMIRs result what might call a "generational leap" – dissimilar very (and highly transformational) cellular telephony world where traditional "cable-based" Internet were feasible, too costly, or restricted residents.[8-10] Within short period time, large segments population gained access general all downstream benefits thereof.[11-13] Coincidentally, much forms important element future development, including ability rapidly deploy high-quality, AI-based educational platforms, focus creating synergies closing resource gaps that previously considered insurmountable.[14] Immediate questions arise context this change, "why should we do this?"; "who stands who lose?"; "how does ecosystem affect components ME, current stakeholders?" There are, course, many other actual implementation, resources required, granular aspects any deployments AI-enabled/aided/facilitated LMIRs. That said, let us three main concerns stated above sequential manner. first question, this?," answered relatively easily. From purely pragmatic standpoint, availability AI-enabled/enhanced such currently available would truly transformational.[15-17] Where opportunities simply did exist, high-quality drastically improve public health equation; here include workforce patient care, provider retention availability.[18,19] This, turn, could translate into sustainable quality-of-life improvements for billions people planet. Beyond these basic considerations, AI-enabled/facilitated systems treat more patients enhanced overall safety quality, significantly throughput at lower cost, job satisfaction.[17,20,21] In addition, efficiency enhancements inherently synergistic emerging field precision medicine personalized care.[22,23] One can speculate setting LMIRs, developments create literal "technological "severely resources" "precision medicine" time (assuming implementation favorable). terms participants gain AI-based/enhanced list long encompasses multiple stakeholder types entire society.[24,25] Obvious primary beneficiaries system take place locally will local communities.[26,27] Here, harness frameworks, augmented by Internet-based video conferencing virtual visits, theoretically real increases expertise) availability. Medical students subsequently graduate (GME) trainees empowered enabled act locally, participate intimately building strengthening capacity communities, less reliant external deliver equivalent better care.[28-31] Equally important, their families education. assurance delivered applied promises affordability training, traditionally those overcome various nonacademic constraints (e.g., travel, tuition costs, personal professional networking limitations). paradigm; however, due space restrictions editorial, superficial exploration possible. Given positives proposed paradigm, naturally wonders, lose this?" Certainly, established structures, especially if insufficiently flexible, put under severe stress. Without adequate support and/or effective regulatory easy existing talent base relevance erode over something avoided. tremendous amount investment infrastructure, critical ensure smooth transition, possibly leading further growth regional institutions.[32-35] A corresponding transition optimally utilized while new, AI-centric being developed implemented responsibly. At societal level, emergence pose difficult challenge structures responsible allocating scarce resources. Such centralized/traditional gatekeeper organizations adversely affected decentralized approach majority administrative oversight operations conducted locally.[36-39] Of great importance, AI-assisted/facilitated models must operate narrowly strictly controlled framework knowledge verification, propagation, re-evaluation, so-called black-box language (LLMs) which lack source attribution transparency.[40,41] Consequently, inputs pre-vetted carefully, importantly, outdated information periodically purged models. Finally, systematic biases LLM sources/data suboptimal, inefficient, potentially harmful model outputs without appropriate ongoing providing incorrect answers advice, out context, fictitious answers).[42-44] Briefly discussed above, effect decentralization centralized cannot overstated. high complexity our regardless level income, geographic location, resource-based new reforms based status quo need researched, intricately planned, masterfully executed.[45-47] Although successful "overnight changes" even drastic ones possible, as shown during coronavirus 2019 (COVID-19) pandemic, associated experiences necessarily apply discussion.[48,49] collaboratives, consisting community thought leaders, acting micro/local coordinating stakeholders macro/global comprehensively consider options select actions best address needs, culturally acceptable solutions. historical experiences, technology platforms intended represent hastily adapted solutions primarily designed HIRs HIR environments.[50-53] general, consultative presence, local/community level. Intentional empowerment stakeholders, governments, students/trainees, patient-facing personnel way roll-outs/implementations, long-term sustainability, strengthening, ultimately safe/unbiased delivery optimized care. become cornerstones intentional empowerment. Another consideration parallel development competent educators using similar AI-driven approaches. work remains done before durable feasible fundamental blocks already place. It up collective academic international solutions, predominantly drivers inputs, thoughtfully relevant practice. ultimate prize tempting accelerated attainment global parity care realized through technologically driven "quantum observed after mass introduction risks, promising prospect outweigh downsides. full promise keep way! Ethical conduct research authors verify preparatory activities Editorial meet institutional standards ethical research. This article contain data studies involving human participants. declare require Institutional Review Board/Ethics Board approval.

Language: Английский

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Introduction

Published: Jan. 1, 2024

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SecureTransfer: A Transfer Learning Based Poison Attack Detection in ML Systems

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(7)

Published: Jan. 1, 2024

Critical systems are increasingly being integrated with machine learning (ML) models, which exposes them to a range of adversarial attacks.The vulnerability hostile attacks has drawn lot attention in recent years. When harmful input is added the training set, it can lead poison attacks, seriously impair model performance and threaten system security. Poison pose serious risk since they involve injection malicious data into set by adversaries, influences model's during inference. It's necessary identify these order preserve reliability security systems. A novel method based on transfer proposed poisoning systems.The methodology for generating initially created later implemented using techniques. Here, poisonous detected pre-trained VGG16 model. This also be used distributed Machine scattered computation across several nodes. Benchmark datasets evaluate this strategy prove effectiveness method. Some real-time applications, advantages, limitations future work discussed here.

Language: Английский

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Towards Efficient Heuristic Graph Edge Coloring

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 360 - 375

Published: Jan. 1, 2024

Language: Английский

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B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain

Journal of Parallel and Distributed Computing, Journal Year: 2024, Volume and Issue: unknown, P. 104978 - 104978

Published: Sept. 1, 2024

Language: Английский

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