<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1012-2966</journal-id>
<journal-title><![CDATA[Gaceta Médica Boliviana]]></journal-title>
<abbrev-journal-title><![CDATA[Gac Med Bol]]></abbrev-journal-title>
<issn>1012-2966</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Medicina de la Universidad Mayor de San Simón]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1012-29662025000200174</article-id>
<article-id pub-id-type="doi">10.47993/gmb.v48i2.1134</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Inteligencia artificial en el abordaje clínico del cáncer de pulmón en el continuum asistencial: revisión crítica]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial Intelligence in the Clinical Management of Lung Cancer Across the Continuum of Care: A Critical Review of Current Evidence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos-Zaga]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Privada del Norte  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>48</volume>
<numero>2</numero>
<fpage>174</fpage>
<lpage>181</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_arttext&amp;pid=S1012-29662025000200174&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_abstract&amp;pid=S1012-29662025000200174&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_pdf&amp;pid=S1012-29662025000200174&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Objetivos:  analizar el impacto de la inteligencia artificial en el abordaje integral del cáncer pulmonar, evaluando sus aplicaciones diagnósticas, terapéuticas y pronósticas, así como las barreras para su implementación clínica en el periodo 2021-2025.  Métodos:  se realizó una revisión narrativa sobre estudios basados en el uso de algoritmos aplicados a la prevención personalizada, el cribado automatizado, el diagnóstico de precisión y la estratificación pronóstica individualizada.  Resultados:  se identificaron beneficios potenciales como mayor sensibilidad en detección temprana y optimización de decisiones terapéuticas, junto con limitaciones asociadas a opacidad algorítmica, sesgos en datos, falta de validación robusta e inequidad en el acceso.  Conclusiones:  la integración de inteligencia artificial en oncología torácica requiere combinar juicio clínico, capacidad computacional y gobernanza ética para lograr un impacto equitativo y sostenible.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Objective:  to analyze the impact of artificial intelligence on the comprehensive management of lung cancer, assessing its diagnostic, therapeutic, and prognostic applications, as well as the barriers to its clinical implementation during the period 2021-2025.  Methods:  A narrative review was conducted of studies applying algorithms to personalized prevention, automated screening, precision diagnosis, and individualized prognostic stratification.  Results:  Potential benefits identified include greater sensitivity for early detection and optimization of therapeutic decision-making, along with limitations such as algorithmic opacity, data bias, lack of robust validation, and inequitable access.  Conclusions:  The integration of artificial intelligence into thoracic oncology requires the combination of clinical judgment, computational capacity, and ethical governance to achieve and equitable and sustainable impact.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[cáncer de pulmón]]></kwd>
<kwd lng="es"><![CDATA[cribado]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico de precisión]]></kwd>
<kwd lng="es"><![CDATA[equidad sanitaria]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="en"><![CDATA[lung cancer]]></kwd>
<kwd lng="en"><![CDATA[screening]]></kwd>
<kwd lng="en"><![CDATA[precision medicine]]></kwd>
<kwd lng="en"><![CDATA[health equity]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bray]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Ferlay]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Soerjomataram]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Siegel]]></surname>
<given-names><![CDATA[RL]]></given-names>
</name>
<name>
<surname><![CDATA[Torre]]></surname>
<given-names><![CDATA[LA]]></given-names>
</name>
<name>
<surname><![CDATA[Jemal]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries]]></article-title>
<source><![CDATA[CA Cancer J Clin]]></source>
<year>2018</year>
<volume>68</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>394-424</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zarinshenas]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Amini]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Mambetsariev]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Abuali]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Fricke]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Ladbury]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Assessment of barriers and challenges to screening, diagnosis, and biomarker testing in early-stage lung cancer]]></article-title>
<source><![CDATA[Cancers (Basel)]]></source>
<year>2023</year>
<volume>15</volume>
<numero>5</numero>
<issue>5</issue>
</nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Attili]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Del Re]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Guerini-Rocco]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Crucitta]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Pisapia]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Pepe]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The role of molecular heterogeneity targeting resistance mechanisms to lung cancer therapies]]></article-title>
<source><![CDATA[Expert Rev Mol Diagn]]></source>
<year>2021</year>
<volume>21</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>757-66</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lim]]></surname>
<given-names><![CDATA[ZF]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[PC]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy]]></article-title>
<source><![CDATA[J Hematol Oncol]]></source>
<year>2019</year>
<volume>12</volume>
</nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Bao]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Xue]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Advances in molecular pathology and therapy of non-small cell lung cancer]]></article-title>
<source><![CDATA[Signal Transduct Target Ther]]></source>
<year>2025</year>
<volume>10</volume>
</nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cognigni]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Toscani]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[D'Agnelli]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Pecci]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Righi]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Berardi]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Molecular heterogeneity of small cell lung cancer and new therapeutic possibilities: a narrative review of the literature]]></article-title>
<source><![CDATA[Transl Lung Cancer Res]]></source>
<year>2025</year>
<volume>14</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A narrative review of tumor heterogeneity and challenges to tumor drug therapy]]></article-title>
<source><![CDATA[Ann Transl Med]]></source>
<year>2021</year>
<volume>9</volume>
<numero>16</numero>
<issue>16</issue>
</nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kanan]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Alharbi]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Alotaibi]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Almasuood]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Aljoaid]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Alharbi]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[AI-driven models for diagnosing and predicting outcomes in lung cancer: a systematic review and meta-analysis]]></article-title>
<source><![CDATA[Cancers (Basel)]]></source>
<year>2024</year>
<volume>16</volume>
<numero>3</numero>
<issue>3</issue>
</nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Bai]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The value of artificial intelligence in the diagnosis of lung cancer: a systematic review and meta-analysis]]></article-title>
<source><![CDATA[PLoS One]]></source>
<year>2023</year>
<volume>18</volume>
<numero>3</numero>
<issue>3</issue>
</nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Rong]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[DM]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Zhan]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images]]></article-title>
<source><![CDATA[Nat Commun]]></source>
<year>2023</year>
<volume>14</volume>
</nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alkhathami]]></surname>
<given-names><![CDATA[MG]]></given-names>
</name>
<name>
<surname><![CDATA[Advani]]></surname>
<given-names><![CDATA[SM]]></given-names>
</name>
<name>
<surname><![CDATA[Abalkhail]]></surname>
<given-names><![CDATA[AA]]></given-names>
</name>
<name>
<surname><![CDATA[Alkhathami]]></surname>
<given-names><![CDATA[FM]]></given-names>
</name>
<name>
<surname><![CDATA[Alshehri]]></surname>
<given-names><![CDATA[MK]]></given-names>
</name>
<name>
<surname><![CDATA[Albeashy]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prevalence and mortality of lung comorbidities among patients with COVID-19: a systematic review and meta-analysis]]></article-title>
<source><![CDATA[Respir Med]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mercadante]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Masedu]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Valenti]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Aielli]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Breakthrough pain in patients with lung cancer. A secondary analysis of IOPS MS study]]></article-title>
<source><![CDATA[J Clin Med]]></source>
<year>2020</year>
<volume>9</volume>
<numero>5</numero>
<issue>5</issue>
</nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Lei]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prevalence of lung cancer in chronic obstructive pulmonary disease: a systematic review and meta-analysis]]></article-title>
<source><![CDATA[Front Oncol]]></source>
<year>2022</year>
<volume>12</volume>
</nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shen]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Crothers]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Kross]]></surname>
<given-names><![CDATA[EK]]></given-names>
</name>
<name>
<surname><![CDATA[Petersen]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Melzer]]></surname>
<given-names><![CDATA[AC]]></given-names>
</name>
<name>
<surname><![CDATA[Triplette]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Provision of smoking cessation resources in the context of in-person shared decision-making for lung cancer screening]]></article-title>
<source><![CDATA[Chest]]></source>
<year>2021</year>
<volume>160</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>765-75</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Niu]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules]]></article-title>
<source><![CDATA[EJNMMI Res]]></source>
<year>2020</year>
<volume>10</volume>
</nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[AW]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
<name>
<surname><![CDATA[Cohen]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Roshkovan]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Kontos]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Radiomic phenotypes of the background lung parenchyma from F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Astley]]></surname>
<given-names><![CDATA[SM]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
</person-group>
<source><![CDATA[Medical Imaging 2024: Computer-Aided Diagnosis]]></source>
<year>2024</year>
<conf-name><![CDATA[ Medical Imaging 2024: Computer-Aided Diagnosis]]></conf-name>
<conf-loc> </conf-loc>
<page-range>102</page-range><publisher-loc><![CDATA[Bellingham ]]></publisher-loc>
<publisher-name><![CDATA[SPIE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Exploratory study of a CT radiomics model for the classification of small cell lung cancer and non-small-cell lung cancer]]></article-title>
<source><![CDATA[Front Oncol]]></source>
<year>2020</year>
<volume>10</volume>
</nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[El Ayachy]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Giraud]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Giraud]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Durdux]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Giraud]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Burgun]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The role of radiomics in lung cancer: from screening to treatment and follow-up]]></article-title>
<source><![CDATA[Front Oncol]]></source>
<year>2021</year>
<volume>11</volume>
</nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tunali]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Gray]]></surname>
<given-names><![CDATA[JE]]></given-names>
</name>
<name>
<surname><![CDATA[Katsoulakis]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Eschrich]]></surname>
<given-names><![CDATA[SA]]></given-names>
</name>
<name>
<surname><![CDATA[Saller]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Hypoxia-related radiomics and immunotherapy response: a multicohort study of non-small cell lung cancer]]></article-title>
<source><![CDATA[JNCI Cancer Spectr]]></source>
<year>2021</year>
<volume>5</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tian]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Increasing trends of polypharmacy and potentially inappropriate medication use in older lung cancer patients in China: a repeated cross-sectional study]]></article-title>
<source><![CDATA[Front Pharmacol]]></source>
<year>2022</year>
<volume>13</volume>
</nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[YJ]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[FZ]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[SC]]></given-names>
</name>
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[EK]]></given-names>
</name>
<name>
<surname><![CDATA[Liang]]></surname>
<given-names><![CDATA[CH]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Radiomics in early lung cancer diagnosis: from diagnosis to clinical decision support and education]]></article-title>
<source><![CDATA[Diagnostics (Basel)]]></source>
<year>2022</year>
<volume>12</volume>
<numero>5</numero>
<issue>5</issue>
</nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Haghighi]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Horng]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Noël]]></surname>
<given-names><![CDATA[PB]]></given-names>
</name>
<name>
<surname><![CDATA[Cohen]]></surname>
<given-names><![CDATA[EA]]></given-names>
</name>
<name>
<surname><![CDATA[Pantalone]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Vachani]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort]]></article-title>
<source><![CDATA[Sci Rep]]></source>
<year>2023</year>
<volume>13</volume>
</nlm-citation>
</ref>
<ref id="B23">
<label>23</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rinaldi]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Guerini Rocco]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Spitaleri]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Raimondi]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Attili]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Ranghiero]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Association between contrast-enhanced computed tomography radiomic features, genomic alterations and prognosis in advanced lung adenocarcinoma patients]]></article-title>
<source><![CDATA[Cancers (Basel)]]></source>
<year>2023</year>
<volume>15</volume>
<numero>18</numero>
<issue>18</issue>
</nlm-citation>
</ref>
<ref id="B24">
<label>24</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dercle]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Fronheiser]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Hayes]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Leung]]></surname>
<given-names><![CDATA[DK]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics]]></article-title>
<source><![CDATA[Clin Cancer Res]]></source>
<year>2020</year>
<volume>26</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>2151-62</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Mao]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients]]></article-title>
<source><![CDATA[Front Oncol]]></source>
<year>2020</year>
<volume>10</volume>
</nlm-citation>
</ref>
<ref id="B26">
<label>26</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Jochems]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Refaee]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Ibrahim]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Sanduleanu]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Structural and functional radiomics for lung cancer]]></article-title>
<source><![CDATA[Eur J Nucl Med Mol Imaging]]></source>
<year>2021</year>
<volume>48</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>3961-74</page-range></nlm-citation>
</ref>
<ref id="B27">
<label>27</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[HT]]></given-names>
</name>
<name>
<surname><![CDATA[Hu]]></surname>
<given-names><![CDATA[WL]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[JJ]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[PZ]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[JJ]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The application of radiomics in esophageal cancer: predicting the response after neoadjuvant therapy]]></article-title>
<source><![CDATA[Front Oncol]]></source>
<year>2023</year>
<volume>13</volume>
</nlm-citation>
</ref>
<ref id="B28">
<label>28</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Le]]></surname>
<given-names><![CDATA[NQK]]></given-names>
</name>
<name>
<surname><![CDATA[Kha]]></surname>
<given-names><![CDATA[QH]]></given-names>
</name>
<name>
<surname><![CDATA[Nguyen]]></surname>
<given-names><![CDATA[VH]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[YC]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[SJ]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[CY]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer]]></article-title>
<source><![CDATA[Int J Mol Sci]]></source>
<year>2021</year>
<volume>22</volume>
<numero>17</numero>
<issue>17</issue>
</nlm-citation>
</ref>
<ref id="B29">
<label>29</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Lerousseau]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Briend-Diop]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Routier]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Roy]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Henry]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy]]></article-title>
<source><![CDATA[J Immunother Cancer]]></source>
<year>2022</year>
<volume>10</volume>
<numero>10</numero>
<issue>10</issue>
</nlm-citation>
</ref>
<ref id="B30">
<label>30</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Morley]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Machado]]></surname>
<given-names><![CDATA[CCV]]></given-names>
</name>
<name>
<surname><![CDATA[Burr]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Cowls]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Joshi]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Taddeo]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The ethics of AI in health care: a mapping review]]></article-title>
<source><![CDATA[Soc Sci Med]]></source>
<year>2020</year>
<volume>260</volume>
</nlm-citation>
</ref>
<ref id="B31">
<label>31</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Iqbal]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Jahangir]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Mashkoor]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Sultana]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Mehmood]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Ashraf]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The future of artificial intelligence in neurosurgery: a narrative review]]></article-title>
<source><![CDATA[Surg Neurol Int]]></source>
<year>2022</year>
<volume>13</volume>
</nlm-citation>
</ref>
<ref id="B32">
<label>32</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Arigbabu]]></surname>
<given-names><![CDATA[AT]]></given-names>
</name>
<name>
<surname><![CDATA[Olaniyi]]></surname>
<given-names><![CDATA[OO]]></given-names>
</name>
<name>
<surname><![CDATA[Adigwe]]></surname>
<given-names><![CDATA[CS]]></given-names>
</name>
<name>
<surname><![CDATA[Adebiyi]]></surname>
<given-names><![CDATA[OO]]></given-names>
</name>
<name>
<surname><![CDATA[Ajayi]]></surname>
<given-names><![CDATA[SA]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Data governance in AI-enabled healthcare systems: a case of the Project Nightingale]]></article-title>
<source><![CDATA[Am J Res Comput Sci]]></source>
<year>2024</year>
<volume>17</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>85-107</page-range></nlm-citation>
</ref>
<ref id="B33">
<label>33</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cavique]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Implications of causality in artificial intelligence]]></article-title>
<source><![CDATA[Front Artif Intell]]></source>
<year>2024</year>
<volume>7</volume>
</nlm-citation>
</ref>
<ref id="B34">
<label>34</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sengupta]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence in diagnostic dermatology: challenges and the way forward]]></article-title>
<source><![CDATA[Indian Dermatol Online J]]></source>
<year>2023</year>
<volume>14</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>782-7</page-range></nlm-citation>
</ref>
<ref id="B35">
<label>35</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mwogosi]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ethical and privacy challenges of integrating generative AI into EHR systems in Tanzania: a scoping review with a policy perspective]]></article-title>
<source><![CDATA[Digit Health]]></source>
<year>2025</year>
<volume>11</volume>
</nlm-citation>
</ref>
<ref id="B36">
<label>36</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alfaraj]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Nagai]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[AlQallaf]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[WS]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Race to the moon or the bottom? Applications, performance, and ethical considerations of artificial intelligence in prosthodontics and implant dentistry]]></article-title>
<source><![CDATA[Dent J (Basel)]]></source>
<year>2024</year>
<volume>13</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B37">
<label>37</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tavory]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Regulating AI in mental health: ethics of care perspective]]></article-title>
<source><![CDATA[JMIR Ment Health]]></source>
<year>2024</year>
<volume>11</volume>
</nlm-citation>
</ref>
<ref id="B38">
<label>38</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ricciardi Celsi]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The dilemma of rapid AI advancements: striking a balance between innovation and regulation by pursuing risk-aware value creation]]></article-title>
<source><![CDATA[Information (Basel)]]></source>
<year>2023</year>
<volume>14</volume>
<numero>12</numero>
<issue>12</issue>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
