Gespeichert in:
| Titel: | Trends of Artificial Intelligence and Big Data for E-Health |
|---|---|
| Von: |
edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina.
|
| Person: |
Sakly, Houneida.
Yeom, Kristen. Halabi, Safwan. Said, Mourad. Seekins, Jayne. Tagina, Moncef. editor. |
| Körperschaft: | |
| Weitere beteiligte Personen: | , , , , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham :
Springer International Publishing : Imprint: Springer,
2022.
|
| Ausgabe: | 1st ed. 2022. |
| Schriftenreihe: | Integrated Science,
9 |
| Schlagwörter: | |
| Medienzugang: | https://doi.org/10.1007/978-3-031-11199-0 |
| Zusammenfassung: | This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare. |
| Umfang: | 1 Online-Ressource (X, 251 p. 67 illus., 64 illus. in color.) |
| ISBN: | 9783031111990 |
| ISSN: | 2662-947X ; |
Internformat
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | ZDB-2-SBL-978-3-031-11199-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240312141249.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230101s2022 sz | o |||| 0|eng d | ||
| 020 | |a 9783031111990 |9 978-3-031-11199-0 | ||
| 024 | 7 | |a 10.1007/978-3-031-11199-0 |2 doi | |
| 050 | 4 | |a R850.A1-854 | |
| 050 | 4 | |a QH315-320 | |
| 072 | 7 | |a MBGR |2 bicssc | |
| 072 | 7 | |a MED106000 |2 bisacsh | |
| 072 | 7 | |a MBGR |2 thema | |
| 082 | 7 | |a 610.72 |2 23 | |
| 245 | 1 | 0 | |a Trends of Artificial Intelligence and Big Data for E-Health |h [electronic resource] / |c edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina. |
| 250 | |a 1st ed. 2022. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2022. | |
| 300 | |a 1 Online-Ressource (X, 251 p. 67 illus., 64 illus. in color.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Integrated Science, |x 2662-947X ; |v 9 | |
| 505 | 0 | |a 1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data. | |
| 520 | |a This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare. | ||
| 650 | 0 | |a Medicine |x Research. | |
| 650 | 0 | |a Biology |x Research. | |
| 650 | 0 | |a Medical care. | |
| 650 | 0 | |a Quantitative research. | |
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 0 | |a Health services administration. | |
| 650 | 0 | |a Bioinformatics. | |
| 650 | 1 | 4 | |a Biomedical Research. |
| 650 | 2 | 4 | |a Health Care. |
| 650 | 2 | 4 | |a Data Analysis and Big Data. |
| 650 | 2 | 4 | |a Data Science. |
| 650 | 2 | 4 | |a Health Care Management. |
| 650 | 2 | 4 | |a Bioinformatics. |
| 700 | 1 | |a Sakly, Houneida. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Yeom, Kristen. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Halabi, Safwan. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Said, Mourad. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Seekins, Jayne. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Tagina, Moncef. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031111983 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031112003 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031112010 |
| 830 | 0 | |a Integrated Science, |x 2662-947X ; |v 9 | |
| 966 | 4 | 0 | |l DE-355 |p ZDB-2-SBL |q UBG_PDA_SBL |u https://doi.org/10.1007/978-3-031-11199-0 |3 Volltext |
| 912 | |a ZDB-2-SBL | ||
| 912 | |a ZDB-2-SXB | ||
| 950 | |a Biomedical and Life Sciences (SpringerNature-11642) | ||
| 950 | |a Biomedical and Life Sciences (R0) (SpringerNature-43708) | ||
| 912 | |a ZDB-2-SBL | ||
| 049 | |a DE-355 | ||
Datensatz im Suchindex
| DE-BY-UBR_katkey | ZDB-2-SBL-978-3-031-11199-0 |
|---|---|
| _version_ | 1835726561381187584 |
| adam_text | |
| any_adam_object | |
| author2 | Sakly, Houneida Yeom, Kristen Halabi, Safwan Said, Mourad Seekins, Jayne Tagina, Moncef |
| author2_role | edt edt edt edt edt edt |
| author2_variant | h s hs k y ky s h sh m s ms j s js m t mt |
| author_corporate | SpringerLink (Online service) |
| author_corporate_role | |
| author_facet | Sakly, Houneida Yeom, Kristen Halabi, Safwan Said, Mourad Seekins, Jayne Tagina, Moncef SpringerLink (Online service) |
| building | Verbundindex |
| bvnumber | localUBR |
| callnumber-first | R - Medicine |
| callnumber-label | R850 |
| callnumber-raw | R850.A1-854 QH315-320 |
| callnumber-search | R850.A1-854 QH315-320 |
| callnumber-sort | R 3850 A1 3854 |
| callnumber-subject | R - General Medicine |
| collection | ZDB-2-SBL ZDB-2-SXB |
| contents | 1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data. |
| dewey-full | 610.72 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610.72 |
| dewey-search | 610.72 |
| dewey-sort | 3610.72 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| edition | 1st ed. 2022. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04945nam a22006735i 4500</leader><controlfield tag="001">ZDB-2-SBL-978-3-031-11199-0</controlfield><controlfield tag="003">DE-He213</controlfield><controlfield tag="005">20240312141249.0</controlfield><controlfield tag="007">cr nn 008mamaa</controlfield><controlfield tag="008">230101s2022 sz | o |||| 0|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783031111990</subfield><subfield code="9">978-3-031-11199-0</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-3-031-11199-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">R850.A1-854</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QH315-320</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MBGR</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MED106000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MBGR</subfield><subfield code="2">thema</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">610.72</subfield><subfield code="2">23</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Trends of Artificial Intelligence and Big Data for E-Health</subfield><subfield code="h">[electronic resource] /</subfield><subfield code="c">edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed. 2022.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham :</subfield><subfield code="b">Springer International Publishing :</subfield><subfield code="b">Imprint: Springer,</subfield><subfield code="c">2022.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (X, 251 p. 67 illus., 64 illus. in color.) </subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield><subfield code="b">PDF</subfield><subfield code="2">rda</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Integrated Science,</subfield><subfield code="x">2662-947X ;</subfield><subfield code="v">9</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Medicine</subfield><subfield code="x">Research.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Biology</subfield><subfield code="x">Research.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Medical care.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Quantitative research.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Health services administration.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Bioinformatics.</subfield></datafield><datafield tag="650" ind1="1" ind2="4"><subfield code="a">Biomedical Research.</subfield></datafield><datafield tag="650" ind1="2" ind2="4"><subfield code="a">Health Care.</subfield></datafield><datafield tag="650" ind1="2" ind2="4"><subfield code="a">Data Analysis and Big Data.</subfield></datafield><datafield tag="650" ind1="2" ind2="4"><subfield code="a">Data Science.</subfield></datafield><datafield tag="650" ind1="2" ind2="4"><subfield code="a">Health Care Management.</subfield></datafield><datafield tag="650" ind1="2" ind2="4"><subfield code="a">Bioinformatics.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sakly, Houneida.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yeom, Kristen.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Halabi, Safwan.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Said, Mourad.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Seekins, Jayne.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tagina, Moncef.</subfield><subfield code="e">editor.</subfield><subfield code="4">edt</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">SpringerLink (Online service)</subfield></datafield><datafield tag="773" ind1="0" ind2=" "><subfield code="t">Springer Nature eBook</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Printed edition:</subfield><subfield code="z">9783031111983</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Printed edition:</subfield><subfield code="z">9783031112003</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Printed edition:</subfield><subfield code="z">9783031112010</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Integrated Science,</subfield><subfield code="x">2662-947X ;</subfield><subfield code="v">9</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-355</subfield><subfield code="p">ZDB-2-SBL</subfield><subfield code="q">UBG_PDA_SBL</subfield><subfield code="u">https://doi.org/10.1007/978-3-031-11199-0</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SBL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SXB</subfield></datafield><datafield tag="950" ind1=" " ind2=" "><subfield code="a">Biomedical and Life Sciences (SpringerNature-11642)</subfield></datafield><datafield tag="950" ind1=" " ind2=" "><subfield code="a">Biomedical and Life Sciences (R0) (SpringerNature-43708)</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SBL</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-355</subfield></datafield></record></collection> |
| id | ZDB-2-SBL-978-3-031-11199-0 |
| illustrated | Not Illustrated |
| indexdate | 2025-06-23T13:27:53Z |
| institution | BVB |
| isbn | 9783031111990 |
| issn | 2662-947X ; |
| language | English |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | 1 Online-Ressource (X, 251 p. 67 illus., 64 illus. in color.) |
| psigel | ZDB-2-SBL UBG_PDA_SBL ZDB-2-SBL ZDB-2-SXB |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing : Imprint: Springer, |
| record_format | marc |
| series | Integrated Science, |
| series2 | Integrated Science, |
| spelling | Trends of Artificial Intelligence and Big Data for E-Health [electronic resource] / edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina. 1st ed. 2022. Cham : Springer International Publishing : Imprint: Springer, 2022. 1 Online-Ressource (X, 251 p. 67 illus., 64 illus. in color.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Integrated Science, 2662-947X ; 9 1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data. This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare. Medicine Research. Biology Research. Medical care. Quantitative research. Artificial intelligence Data processing. Health services administration. Bioinformatics. Biomedical Research. Health Care. Data Analysis and Big Data. Data Science. Health Care Management. Sakly, Houneida. editor. edt http://id.loc.gov/vocabulary/relators/edt Yeom, Kristen. editor. edt http://id.loc.gov/vocabulary/relators/edt Halabi, Safwan. editor. edt http://id.loc.gov/vocabulary/relators/edt Said, Mourad. editor. edt http://id.loc.gov/vocabulary/relators/edt Seekins, Jayne. editor. edt http://id.loc.gov/vocabulary/relators/edt Tagina, Moncef. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031111983 Printed edition: 9783031112003 Printed edition: 9783031112010 |
| spellingShingle | Trends of Artificial Intelligence and Big Data for E-Health Integrated Science, 1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data. Medicine Research. Biology Research. Medical care. Quantitative research. Artificial intelligence Data processing. Health services administration. Bioinformatics. Biomedical Research. Health Care. Data Analysis and Big Data. Data Science. Health Care Management. |
| title | Trends of Artificial Intelligence and Big Data for E-Health |
| title_auth | Trends of Artificial Intelligence and Big Data for E-Health |
| title_exact_search | Trends of Artificial Intelligence and Big Data for E-Health |
| title_full | Trends of Artificial Intelligence and Big Data for E-Health [electronic resource] / edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina. |
| title_fullStr | Trends of Artificial Intelligence and Big Data for E-Health [electronic resource] / edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina. |
| title_full_unstemmed | Trends of Artificial Intelligence and Big Data for E-Health [electronic resource] / edited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina. |
| title_short | Trends of Artificial Intelligence and Big Data for E-Health |
| title_sort | trends of artificial intelligence and big data for e health |
| topic | Medicine Research. Biology Research. Medical care. Quantitative research. Artificial intelligence Data processing. Health services administration. Bioinformatics. Biomedical Research. Health Care. Data Analysis and Big Data. Data Science. Health Care Management. |
| topic_facet | Medicine Research. Biology Research. Medical care. Quantitative research. Artificial intelligence Data processing. Health services administration. Bioinformatics. Biomedical Research. Health Care. Data Analysis and Big Data. Data Science. Health Care Management. |
| work_keys_str_mv | AT saklyhouneida trendsofartificialintelligenceandbigdataforehealth AT yeomkristen trendsofartificialintelligenceandbigdataforehealth AT halabisafwan trendsofartificialintelligenceandbigdataforehealth AT saidmourad trendsofartificialintelligenceandbigdataforehealth AT seekinsjayne trendsofartificialintelligenceandbigdataforehealth AT taginamoncef trendsofartificialintelligenceandbigdataforehealth AT springerlinkonlineservice trendsofartificialintelligenceandbigdataforehealth |