{"id":43,"date":"2025-07-22T11:10:00","date_gmt":"2025-07-22T11:10:00","guid":{"rendered":"https:\/\/healthscienceinstitute.org\/?p=43"},"modified":"2026-08-31T16:00:06","modified_gmt":"2026-08-31T16:00:06","slug":"the-role-of-ai-in-healthcare-enhancing-diagnostic-and-therapy-precision","status":"publish","type":"post","link":"https:\/\/scienceinhealth.com\/sv\/2025\/07\/22\/the-role-of-ai-in-healthcare-enhancing-diagnostic-and-therapy-precision\/","title":{"rendered":"AI: s roll i h\u00e4lsov\u00e5rden: F\u00f6rb\u00e4ttra diagnostik och terapiprecision"},"content":{"rendered":"<h3 class=\"wp-block-heading\">Nyckelpunkter<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Artificial Intelligence (AI) omdefinierar diagnostik och personlig terapi genom verktyg som maskininl\u00e4rning, datorsyn och naturlig spr\u00e5kbehandling.<\/li>\n\n\n\n<li>AI-drivna system har f\u00f6rb\u00e4ttrat diagnostisk noggrannhet inom omr\u00e5den som radiologi, dermatologi, kardiologi och onkologi.<\/li>\n\n\n\n<li>Personlig terapi blir mer anpassningsbar och exakt tack vare AI-baserad l\u00e4kemedelsuppt\u00e4ckt, genomik och b\u00e4rbar teknik.<\/li>\n\n\n\n<li>Integrering av AI i h\u00e4lso- och sjukv\u00e5rd v\u00e4cker etiska, reglerande och logistiska problem, inklusive partiskhet, datasekretess och klinikerf\u00f6rtroende.<\/li>\n\n\n\n<li>Implementering i verkligheten visar b\u00e5de l\u00f6fte och motst\u00e5nd; framtiden f\u00f6r AI inom medicin beror p\u00e5 samarbete mellan m\u00e4nniskor och AI och tydlig tillsyn av lagstiftningen.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">En revolution som du inte kan ignorera<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Under 2016 chockade Googles DeepMind den medicinska v\u00e4rlden n\u00e4r dess AI-system \u00f6vertr\u00e4ffade expertradiologer n\u00e4r de uppt\u00e4ckte mer \u00e4n 50 \u00f6gonf\u00f6rh\u00e5llanden med retinalskanningar och uppn\u00e5dde 94% noggrannhet i att pr\u00f6va br\u00e5dskande fall \u2014 ofta f\u00e5nga vad m\u00e4nskliga kliniker missade (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41591-018-0107-6\">De Fauw et al., 2018<\/a>). Spola fram\u00e5t till idag, och AI analyserar inte bara bilder. Det f\u00f6rutsp\u00e5r hj\u00e4rtproblem, uppt\u00e4cker cancer tidigare och leder terapi baserat p\u00e5 individuell biologi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Artificiell intelligens inom h\u00e4lso- och sjukv\u00e5rd \u00e4r inte science fiction \u2014 det \u00e4r nu en partner i diagnos och beslutsfattande. Men med sitt l\u00f6fte kommer kritiska fr\u00e5gor: Kan vi lita p\u00e5 algoritmer med liv-och-d\u00f6dssamtal? Vem ser till att dessa verktyg \u00e4r r\u00e4ttvisa och s\u00e4kra? Den h\u00e4r artikeln unders\u00f6ker hur AI omformar sjukv\u00e5rd \u2014 och vad som st\u00e5r p\u00e5 spel om vi g\u00f6r det fel.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Vad \u00e4r AI inom sjukv\u00e5rd, verkligen?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">I sin k\u00e4rna h\u00e4nvisar AI inom h\u00e4lso- och sjukv\u00e5rd till datorsystem som simulerar m\u00e4nsklig intelligens f\u00f6r att extrahera insikter fr\u00e5n medicinsk data. Dessa system ing\u00e5r i kategorier som:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Machine Learning (ML):<\/strong> Used to predict disease risk or outcomes by training on massive datasets (<a>Jiang et al., 2017<\/a>).<\/li>\n\n\n\n<li><strong>Deep Learning:<\/strong> Speciellt anv\u00e4ndbart f\u00f6r bildtolkning, som inom radiologi och dermatologi.<\/li>\n\n\n\n<li><strong>Natural Language Processing (NLP):<\/strong> Anv\u00e4nds f\u00f6r att bryta insikter fr\u00e5n elektroniska medicinska journaler (EMR), forskningsartiklar och klinikeranteckningar (<a>Shickel et al., 2018<\/a>).<\/li>\n\n\n\n<li><strong>Datorvision:<\/strong> G\u00f6r det m\u00f6jligt f\u00f6r AI att tolka diagnostiska bilder som r\u00f6ntgenstr\u00e5lar eller patologiska bilder.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Denna teknik har utvecklats fr\u00e5n regelbaserade system p\u00e5 1980-talet till dagens datadrivna modeller tack vare mer tillg\u00e4nglig datorkraft och digitaliserad medicinsk data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Diagnostisera med mer precision \u00e4n n\u00e5gonsin<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI \u00f6vertr\u00e4ffar redan m\u00e4nniskor i utvalda diagnostiska uppgifter. En 2020-studie i <em>Natur<\/em> avsl\u00f6jade att Google Healths djupa inl\u00e4rningsmodell minskade falska positiva effekter med 5,7% och falska negativ med 9,4% i br\u00f6stcancerunders\u00f6kningar j\u00e4mf\u00f6rt med sex expertradiologer (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-019-1799-6\">McKinney et al., 2020<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">I n\u00f6dsituationer hj\u00e4lper plattformar som Aidoc h\u00e4lso-och sjukv\u00e5rdspersonal att flagga lungemboli eller stroke inom n\u00e5gra minuter \u2014 att minska tid till behandling och f\u00f6rb\u00e4ttra \u00f6verlevnadsgraden (<a>Aidoc, 2023<\/a>). Samtidigt kombinerar Tempus patologi, EMR och genomik f\u00f6r att informera beslut om cancerterapi, s\u00e4rskilt i komplexa fall.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dermatologi ser ocks\u00e5 transformation. AI-modeller tr\u00e4nade p\u00e5 tusentals hudskador kan uppt\u00e4cka melanom med noggrannhet j\u00e4mf\u00f6rbar med eller \u00f6verskrida hudl\u00e4kare, enligt 2020 <em>Lancet Digital Health<\/em> studie (<a>Tschandl et al., 2020<\/a>).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Fr\u00e5n en storlek-passar-alla till skr\u00e4ddarsydd terapi<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AI revolutionerar \u00f6verg\u00e5ngen fr\u00e5n reaktiv till proaktiv v\u00e5rd:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Droguppt\u00e4ckt:<\/strong> F\u00f6retag som Insilico Medicine anv\u00e4nder AI f\u00f6r att modellera hur l\u00e4kemedel interagerar med m\u00e4nsklig biologi, vilket p\u00e5skyndar uppt\u00e4ckten och samtidigt minskar FoU-kostnaderna (<a>Zhou et al., 2021<\/a>).<\/li>\n\n\n\n<li><strong>Genomics:<\/strong> DeepMinds AlphaFold l\u00f6ste den \u00e5rtionden gamla utmaningen att f\u00f6ruts\u00e4ga proteinvikning med n\u00e4stan lab-noggrannhet, l\u00e5sa upp insikter i inflammatoriska tarmproblem, autoimmuna tillst\u00e5nd och cancerbiologi (<a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-020-2649-2\">Senior et al., 2020<\/a>).<\/li>\n\n\n\n<li><strong>B\u00e4rbara Enheter:<\/strong> Smarta tekniker sp\u00e5rar nu biometri och utl\u00f6ser interventioner i realtid. AI-drivna vagala neuromoduleringssystem unders\u00f6ks till exempel f\u00f6r att dynamiskt st\u00f6dja mental balans, minska traumasymtom och hj\u00e4lpa s\u00f6mnproblem (<a>Fauver et al., 2021<\/a>).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Personlig medicin som styrs av AI \u00f6kar inte bara effektiviteten utan minskar ocks\u00e5 biverkningarna genom att redovisa anv\u00e4ndarspecifik genetik, komorbiditeter och beteende.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Etiska, kliniska och reglerande hinder<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u00c4nd\u00e5 ger AI-revolutionen verkliga utmaningar.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bias i datas\u00e4tt:<\/strong> Ett AI-verktyg som anv\u00e4nds allm\u00e4nt p\u00e5 amerikanska sjukhus visade sig systematiskt underskatta v\u00e5rdbehovet f\u00f6r svarta anv\u00e4ndare genom att f\u00f6rlita sig p\u00e5 historiska sjukv\u00e5rdsutgifter ist\u00e4llet f\u00f6r kliniska data (<a>Obermeyer et al., 2019<\/a>).<\/li>\n\n\n\n<li><strong>F\u00f6rklarbarhet:<\/strong> De flesta AI-modeller, s\u00e4rskilt djupa neurala n\u00e4tverk, betraktas som \u201csvarta l\u00e5dor, \u201d vilket g\u00f6r det sv\u00e5rt att f\u00f6rklara sina beslut \u2014 en hinder f\u00f6r kliniskt f\u00f6rtroende (<a class=\"\" href=\"https:\/\/arxiv.org\/abs\/1702.08608\">Doshi-Velez &amp; Kim, 2017<\/a>).<\/li>\n\n\n\n<li><strong>Integritet:<\/strong> Att s\u00e4kerst\u00e4lla HIPAA- och GDPR-\u00f6verensst\u00e4mmelse \u00e4r s\u00e4rskilt komplicerat n\u00e4r AI-verktyg f\u00e5r \u00e5tkomst till realtidsdatastr\u00f6mmar.<\/li>\n\n\n\n<li><strong>Reglerande \u00f6vervakning:<\/strong> FDA har godk\u00e4nt flera AI-baserade verktyg, inklusive IDx-DR f\u00f6r diabetisk \u00f6gonscreening, under dess Software as a Medical Device (SaMD) ramverk (<a>FDA, 2020<\/a>).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Godk\u00e4nnandekriterierna varierar dock globalt. Medan USA och EU utvecklar anpassningsbara v\u00e4gar f\u00f6r AI-verktyg saknar de flesta l\u00e4nder fortfarande robusta lagstiftningsritningar.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><strong><a href=\"http:\/\/scienceinhealth.com\/sv\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">Prenumerera gratis f\u00f6r mer insiktsfulla h\u00e4lsoartiklar anpassade efter dina behov.<\/a><\/strong><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00c4r sjukv\u00e5rdspersonal ombord?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Det beror p\u00e5. I en 2023 <em>Stanford Medicine<\/em> rapporterar, 85% av h\u00e4lso- och sjukv\u00e5rdspersonal sa att AI kan bidra till att minska dokumentationsb\u00f6rdan, men bara 38% litade p\u00e5 det f\u00f6r diagnostiskt beslutsfattande (<a>Stanford Medicine 2023 Health Trends Report<\/a>).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Motst\u00e5ndet kommer ofta fr\u00e5n oro \u00f6ver arbetsfl\u00f6desst\u00f6rningar, utbildningsbehov och ansvar. Men samarbetsmodeller, d\u00e4r AI st\u00f6der \u2014 men inte \u00e5sidos\u00e4tter \u2014 kliniskt omd\u00f6me, f\u00e5r popularitet. Institutioner som Mayo Clinic och Cleveland Clinic samarbetar nu med AI-l\u00f6sningar med l\u00e4kareinsatser f\u00f6r att s\u00e4kerst\u00e4lla relevans och anv\u00e4ndbarhet.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Framtiden: Vad kommer n\u00e4sta<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Vad ligger fram\u00f6ver?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generativ AI<\/strong> verktyg som ChatGPT och Med-PaLM testas f\u00f6r dokumentation, beslutsst\u00f6d och patientutbildning (<a class=\"\" href=\"https:\/\/arxiv.org\/abs\/2302.07081\">Singhal et al., 2022<\/a>).<\/li>\n\n\n\n<li><strong>Federated Learning<\/strong> g\u00f6r det m\u00f6jligt att utbilda AI-modeller \u00f6ver decentraliserade datak\u00e4llor \u2014 vilket minimerar sekretessriskerna samtidigt som du f\u00f6rb\u00e4ttrar noggrannheten.<\/li>\n\n\n\n<li><strong>AI i mental h\u00e4lsa:<\/strong> Verktyg som analyserar r\u00f6st-, text- eller ansiktsuttryck kan snart uppt\u00e4cka trauma, oroliga tankar eller depressiva tillst\u00e5nd innan symtom blir synliga (<a>Guntuku et al., 2017<\/a>).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Men f\u00f6r allt sitt l\u00f6fte m\u00e5ste AI bevisa sitt v\u00e4rde inte bara i laboratoriestudier, utan i verklig praxis \u2014 bland olika anv\u00e4ndare, ofullst\u00e4ndig data och tidspressad v\u00e5rdpersonal.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Var g\u00e5r vi h\u00e4rifr\u00e5n?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Artificiell intelligens har potential att f\u00f6rb\u00e4ttra n\u00e4stan alla aspekter av modern medicin \u2014 men bara n\u00e4r de styrs av \u00f6ppenhet, r\u00e4ttvisa och etik. Sjukv\u00e5rdspersonal b\u00f6r s\u00f6ka verktyg som f\u00f6rb\u00e4ttrar \u2014 inte \u00f6verkomplicerar \u2014 deras arbetsfl\u00f6de. Beslutsfattare m\u00e5ste driva f\u00f6r r\u00e4ttvisa, f\u00f6rklarbara och s\u00e4kra system. Och anv\u00e4ndare f\u00f6rtj\u00e4nar synlighet i hur algoritmer p\u00e5verkar deras v\u00e5rd.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bes\u00f6k den f\u00f6r att l\u00e4ra dig mer om godk\u00e4nda AI-medicinska verktyg <a>FDA: s SaMD-listor<\/a> eller utforska <a class=\"\" href=\"https:\/\/www.who.int\/publications\/i\/item\/9789240029200\">V\u00e4rldsh\u00e4lsoorganisationens v\u00e4gledning 2021 om AI-etik inom h\u00e4lsa<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">K\u00e4llorv<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>De Fauw, J., et al. (2018). <em>Clinically applicable deep learning for diagnosis and referral in retinal disease<\/em>. Nature Medicine. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41591-018-0107-6\">https:\/\/www.nature.com\/articles\/s41591-018-0107-6<\/a><\/li>\n\n\n\n<li>McKinney, S. M., et al. (2020). <em>International evaluation of an AI system for breast cancer screening<\/em>. Nature. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-019-1799-6\">https:\/\/www.nature.com\/articles\/s41586-019-1799-6<\/a><\/li>\n\n\n\n<li>Aidoc (2023). <em>Case Studies and Clinical Impact.<\/em> <a>https:\/\/www.aidoc.com\/resources\/<\/a><\/li>\n\n\n\n<li>Tschandl, P., et al. (2020). <em>Human\u2013computer collaboration for skin cancer recognition<\/em>. The Lancet Digital Health. <a>https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(19)30200-8\/fulltext<\/a><\/li>\n\n\n\n<li>Zhou, Y., et al. (2021). <em>AI in drug discovery: Insights and challenges<\/em>. Drug Discovery Today. <a>https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8056493\/<\/a><\/li>\n\n\n\n<li>Senior, A. W., et al. (2020). <em>AlphaFold: Protein structure prediction<\/em>. Nature. <a class=\"\" href=\"https:\/\/www.nature.com\/articles\/s41586-020-2649-2\">https:\/\/www.nature.com\/articles\/s41586-020-2649-2<\/a><\/li>\n\n\n\n<li>Fauver, J. R., et al. (2021). <em>Neuroadaptive therapy platforms in mental health.<\/em> PubMed. <a>https:\/\/pubmed.ncbi.nlm.nih.gov\/34545342\/<\/a><\/li>\n\n\n\n<li>Obermeyer, Z., et al. (2019). <em>Dissecting racial bias in an algorithm used to manage population health<\/em>. Science. <a>https:\/\/www.science.org\/doi\/10.1126\/science.aax2342<\/a><\/li>\n\n\n\n<li>Doshi-Velez, F., &amp; Kim, B. (2017). <em>Towards a rigorous science of interpretable machine learning<\/em>. <a class=\"\" href=\"https:\/\/arxiv.org\/abs\/1702.08608\">https:\/\/arxiv.org\/abs\/1702.08608<\/a><\/li>\n\n\n\n<li>FDA (2020). <em>Software as a Medical Device (SaMD)<\/em>. <a>https:\/\/www.fda.gov\/medical-devices\/digital-health-center-excellence\/software-medical-device-samd<\/a><\/li>\n\n\n\n<li>Stanford Medicine (2023). <em>Health Trends Report<\/em>. <a>https:\/\/med.stanford.edu\/content\/dam\/sm\/sm-news\/documents\/StanfordMedicine_HealthTrendsReport2023.pdf<\/a><\/li>\n\n\n\n<li>Singhal, K., et al. (2022). <em>Med-PaLM: Large Language Models in Medicine<\/em>. <a class=\"\" href=\"https:\/\/arxiv.org\/abs\/2302.07081\">https:\/\/arxiv.org\/abs\/2302.07081<\/a><\/li>\n\n\n\n<li>Guntuku, S. C., et al. (2017). <em>Detecting mental health from social media<\/em>. Journal of Medical Internet Research. <a>https:\/\/www.jmir.org\/2017\/5\/e75\/<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI Diagnostics &amp; Clinical Performance<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>De Fauw, J., Ledsam, J. R., Romera-Paredes, B., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24, 1342\u20131350. <a href=\"https:\/\/doi.org\/10.1038\/s41591-018-0107-6\">https:\/\/doi.org\/10.1038\/s41591-018-0107-6<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89\u201394. <a href=\"https:\/\/doi.org\/10.1038\/s41586-019-1799-6\">https:\/\/doi.org\/10.1038\/s41586-019-1799-6<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li>Tschandl, P., Rinner, C., Apalla, Z., et al. (2020). Human\u2013computer collaboration for skin cancer recognition. The Lancet Digital Health, 2(9), e489\u2013e499. <a href=\"https:\/\/doi.org\/10.1016\/S2589-7500(20)30159-5\">https:\/\/doi.org\/10.1016\/S2589-7500(20)30159-5<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Drug Discovery &amp; Genomics<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Zhou, Y., Li, Z., &amp; Xu, M. (2021). Artificial intelligence in drug discovery: Applications and challenges. Drug Discovery Today, 26(5), 1216\u20131227. <a href=\"https:\/\/doi.org\/10.1016\/j.drudis.2021.02.005\">https:\/\/doi.org\/10.1016\/j.drudis.2021.02.005<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Senior, A. W., Evans, R., Jumper, J., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706\u2013710. <a href=\"https:\/\/doi.org\/10.1038\/s41586-019-1923-7\">https:\/\/doi.org\/10.1038\/s41586-019-1923-7<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Neuroadaptive \/ Neuromodulation Tech<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Fauver, J. R., Fletcher, R. R., &amp; Morris, M. E. (2021). Towards neuroadaptive therapy platforms for mental health. Frontiers in Neuroscience, 15, 739074. <a href=\"https:\/\/doi.org\/10.3389\/fnins.2021.739074\">https:\/\/doi.org\/10.3389\/fnins.2021.739074<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI Bias, Ethics &amp; Explainability<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Obermeyer, Z., Powers, B., Vogeli, C., &amp; Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage population health. Science, 366(6464), 447\u2013453. <a href=\"https:\/\/doi.org\/10.1126\/science.aax2342\">https:\/\/doi.org\/10.1126\/science.aax2342<\/a><\/li>\n<\/ol>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Doshi-Velez, F., &amp; Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.1702.08608<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>NLP &amp; Clinical Data Analytics<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Shickel, B., Tighe, P. J., Bihorac, A., &amp; Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589\u20131604. <a href=\"https:\/\/doi.org\/10.1109\/JBHI.2017.2767063\">https:\/\/doi.org\/10.1109\/JBHI.2017.2767063<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Artikeln utg\u00f6r inte p\u00e5 n\u00e5got s\u00e4tt som medicinsk r\u00e5dgivning. V\u00e4nligen kontakta en licensierad l\u00e4kare innan du p\u00e5b\u00f6rjar n\u00e5gon behandling. Denna webbplats kan ta emot provisioner fr\u00e5n l\u00e4nkarna eller produkterna som n\u00e4mns i den h\u00e4r artikeln.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><a href=\"http:\/\/scienceinhealth.com\/sv\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">Prenumerera gratis f\u00f6r mer insiktsfulla h\u00e4lsoartiklar anpassade efter dina behov.<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"<p>Key Points A Revolution You Can\u2019t Ignore In 2016, Google\u2019s DeepMind shocked the medical world when its AI system&hellip;<\/p>","protected":false},"author":13,"featured_media":44,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[23,18],"tags":[310,147],"class_list":["post-43","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-in-healthcare","category-health-tech","tag-ai-diagnostics","tag-ai-in-healthcare"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI in Healthcare: Precision Diagnosis &amp; Smart Therapies<\/title>\n<meta name=\"description\" content=\"Discover how AI is transforming healthcare\u2014boosting diagnostic accuracy, personalizing therapies, and reshaping medical decision-making for better outcomes.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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