When AI Finally Beats the Experts: DeepRare and the End of the Diagnostic Odyssey

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730582  61stabilfr·wuedophcgmx
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A new AI system published in Nature has achieved what many thought impossible: diagnosing rare diseases more accurately than experienced physicians. DeepRare, developed by researchers led by Zhao et al., demonstrates 64.4% top-1 diagnostic accuracy compared to 54.6% for human experts with over a decade of clinical experience. Tested across 6,401 cases spanning 2,919 diseases, the system provide...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18730582 61stabilfr·wuedophcgmx
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OpenAI vs Anthropic vs Google: Enterprise Provider Comparison 2026

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730535  41stabilfr·wuedophcgmx
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The enterprise AI landscape in 2026 presents organizations with a critical strategic decision: which large language model (LLM) provider should anchor their AI infrastructure? This comparative analysis examines the three dominant commercial providers—OpenAI, Anthropic, and Google—across dimensions of pricing, performance, enterprise features, technical capabilities, and total cost of ownership....

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730535 41stabilfr·wuedophcgmx
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Security Investment — Adversarial Attack Prevention

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730508  75stabilfr·wuedophcgmx
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Adversarial attacks represent a critical security threat to machine learning systems, with global estimated losses reaching approximately $6 trillion in 2021—double the costs recorded in previous years. This article presents a comprehensive economic framework for evaluating security investments in adversarial attack prevention, analyzing the cost-benefit tradeoffs of defense mechanisms includin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18730508 75stabilfr·wuedophcgmx
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Capturing AI Requirements: Beyond Functional Specifications

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18730498  77stabilfr·wuedophcgmx
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Traditional requirements engineering approaches, developed for deterministic software systems, prove inadequate when applied to AI systems characterized by learning, uncertainty, and emergent behavior. This article examines the unique challenges of capturing requirements for AI systems and proposes a structured framework that extends beyond conventional functional specifications. We explore beh...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18730498 77stabilfr·wuedophcgmx
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The Anticipation Gap: Research Transitions Academia Refuses to Make

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18726155  62stabilfr·wuedophcgmx
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This analysis identifies critical research transitions that academic foresight literature systematically avoids despite their urgent practical necessity. While academia has built extensive frameworks around scenario planning, Delphi methods, and horizon scanning, a persistent gap exists between what researchers study and what practitioners need—with nearly 90% of notable AI models in 2024 comin...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18726155 62stabilfr·wuedophcgmx
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[s]Reviewed Sources35%○≥80% from editorially reviewed sources
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Chapter 14: Grand Conclusion — The Future of Intelligent Data Analysis

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18725779  78stabilfr·wuedophcgmx
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Score = Ref Trust (75 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

This concluding chapter synthesizes insights from fourteen chapters of data mining taxonomy and analysis, projecting the field's trajectory toward 2030 and beyond. We present a comprehensive taxonomy of future research directions organized across five dimensions: theoretical foundations, algorithmic innovation, application domains, ethical considerations, and sociotechnical integration. Drawing...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18725779 78stabilfr·wuedophcgmx
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Chapter 13: Emerging Frontiers in Data Mining (2024-2026)

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18725775  72stabilfr·wuedophcgmx
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[r]References98 refs✓Minimum 10 references required
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Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

This chapter surveys cutting-edge data mining techniques emerging between 2024-2026, distinguishing transformative innovations from incremental improvements. We examine five frontier areas: (1) AutoML systems achieving expert-level performance through neural architecture search and meta-learning, (2) foundation models for tabular data adapting large language model techniques to structured datas...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18725775 72stabilfr·wuedophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
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[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access68%○≥80% are freely accessible
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Chapter 12: Cross-Domain Synthesis — Universal Patterns in Data Mining

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18725773  80stabilfr·wuedophcgmx
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[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References44 refs✓Minimum 10 references required
[w]Words [REQ]3,140✓Minimum 1,000 words of article body. Current: 3,140
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]42✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 42
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725773
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

This chapter synthesizes patterns and principles across all data mining domains explored in previous chapters, identifying universal challenges, transferable solutions, and recurring research gaps. We analyze commonalities between finance, healthcare, manufacturing, retail, and telecommunications applications, demonstrating that despite domain-specific nuances, data mining confronts a remarkabl...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18725773 80stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources75%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References44 refs✓Minimum 10 references required
[w]Words [REQ]3,140✓Minimum 1,000 words of article body. Current: 3,140
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]42✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 42
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725773
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness0%○≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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The Future of Anticipatory Intelligence: Beyond the Hype Cycle

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18725744  70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed63%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access69%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,234✓Minimum 1,000 words of article body. Current: 1,234
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]11✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725744
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness13%○≥60% of references from 2025–2026. Current: 13%
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[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

After thirteen articles dissecting anticipatory intelligence—its gaps, priorities, and emerging solutions—we arrive at the question that matters: where is this field actually headed? Not where we wish it would go or what the grant proposals promise, but what the evidence suggests is likely. The answer is sobering, pragmatic, and perhaps more interesting than the typical visionary conclusions. A...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18725744 70stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources25%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI19%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed63%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access69%○≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]1,234✓Minimum 1,000 words of article body. Current: 1,234
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]11✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 11
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725744
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness13%○≥60% of references from 2025–2026. Current: 13%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Emerging Solutions and Research Directions: Beyond the Current Paradigm

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18725742  72stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed58%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]2,424✓Minimum 1,000 words of article body. Current: 2,424
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725742
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Having identified the critical gaps in anticipatory intelligence and prioritized them by tractability and impact, we now survey the emerging technical approaches that might actually close these gaps. Spoiler: most won't. The literature is heavy on incremental refinements and light on paradigm shifts, though a few promising directions warrant serious attention. This article evaluates recent adva...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18725742 72stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources56%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed58%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access44%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]2,424✓Minimum 1,000 words of article body. Current: 2,424
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]33✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 33
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18725742
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness3%○≥60% of references from 2025–2026. Current: 3%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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