Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes
AI has the potential to revolutionize healthcare by enabling more accurate diagnoses, more effective treatment regimens, and improved patient outcomes. While AI is promising, many challenges remain including limited case studies from the real world, regulatory pressure, bias in data and integration...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04004.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850272153525551104 |
|---|---|
| author | Rashid Zahraa M. Balaram Allam Lavanya Chinthamalla Mary P Shyamala Anto Tiwari Mohit P K Chidambaram |
| author_facet | Rashid Zahraa M. Balaram Allam Lavanya Chinthamalla Mary P Shyamala Anto Tiwari Mohit P K Chidambaram |
| author_sort | Rashid Zahraa M. |
| collection | DOAJ |
| description | AI has the potential to revolutionize healthcare by enabling more accurate diagnoses, more effective treatment regimens, and improved patient outcomes. While AI is promising, many challenges remain including limited case studies from the real world, regulatory pressure, bias in data and integration into existing health care delivery systems. In this research we intend to overcome these challenges by designing a comprehensive framework to enhance transactive adoption of AI in healthcare. Cohorts combined with longitudinal case studies advance the study; ethical perspectives, data quality improvement, and bias mitigation emphasises justification for the validity and generalizability of the AI technologies used, which improves the quality of the study. Focus of the Research The research attempts to build interoperable AI systems (which can connect with current healthcare infrastrukture) by Ideating solutions for scalable AI Integration Additionally, it also discusses the challenges posed by hackers and criminal organisations, along with measures to promote patient data privacy, regulatory compliance, and the long-term effects of artificial intelligence on patient healthcare. Such understanding may facilitate an adequate implementation of AI by healthcare professionals and organizations as to impact patient safety, decrease costs and increase the outcome of patient population sorting for different clinical environments. |
| format | Article |
| id | doaj-art-97c4ae23013e4b2aa5751caab938e8e2 |
| institution | OA Journals |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-97c4ae23013e4b2aa5751caab938e8e22025-08-20T01:51:57ZengEDP SciencesITM Web of Conferences2271-20972025-01-01760400410.1051/itmconf/20257604004itmconf_icsice2025_04004Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient OutcomesRashid Zahraa M.0Balaram Allam1Lavanya Chinthamalla2Mary P Shyamala Anto3Tiwari Mohit4P K Chidambaram5Medical Instrumentation Department, Technical Institute of Babylon, Al-furat Al-AWsat Technical UniversityProfessor and Head, Department of Computer Science and Engineering, MLR Institute of TechnologyAssistant Professor, Department of Computer Science and Engineering, CVR College of EngineeringAssistant Professor, Department of Mathematics, SRM TRP Engineering CollegeAssistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of EngineeringProfessor, Department of Mechanical, New Prince Shri Bhavani College of Engineering and TechnologyAI has the potential to revolutionize healthcare by enabling more accurate diagnoses, more effective treatment regimens, and improved patient outcomes. While AI is promising, many challenges remain including limited case studies from the real world, regulatory pressure, bias in data and integration into existing health care delivery systems. In this research we intend to overcome these challenges by designing a comprehensive framework to enhance transactive adoption of AI in healthcare. Cohorts combined with longitudinal case studies advance the study; ethical perspectives, data quality improvement, and bias mitigation emphasises justification for the validity and generalizability of the AI technologies used, which improves the quality of the study. Focus of the Research The research attempts to build interoperable AI systems (which can connect with current healthcare infrastrukture) by Ideating solutions for scalable AI Integration Additionally, it also discusses the challenges posed by hackers and criminal organisations, along with measures to promote patient data privacy, regulatory compliance, and the long-term effects of artificial intelligence on patient healthcare. Such understanding may facilitate an adequate implementation of AI by healthcare professionals and organizations as to impact patient safety, decrease costs and increase the outcome of patient population sorting for different clinical environments.https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04004.pdfartificial intelligencehealthcarepatient outcomesdata qualitybias mitigationregulatory complianceai integrationdiagnostic accuracyinteroperabilityethical considerationspatient safetylongitudinal studiesscalabilityhealthcare technology |
| spellingShingle | Rashid Zahraa M. Balaram Allam Lavanya Chinthamalla Mary P Shyamala Anto Tiwari Mohit P K Chidambaram Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes ITM Web of Conferences artificial intelligence healthcare patient outcomes data quality bias mitigation regulatory compliance ai integration diagnostic accuracy interoperability ethical considerations patient safety longitudinal studies scalability healthcare technology |
| title | Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes |
| title_full | Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes |
| title_fullStr | Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes |
| title_full_unstemmed | Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes |
| title_short | Artificial Intelligence in Healthcare Applications Challenges and Opportunities for Improved Patient Outcomes |
| title_sort | artificial intelligence in healthcare applications challenges and opportunities for improved patient outcomes |
| topic | artificial intelligence healthcare patient outcomes data quality bias mitigation regulatory compliance ai integration diagnostic accuracy interoperability ethical considerations patient safety longitudinal studies scalability healthcare technology |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/07/itmconf_icsice2025_04004.pdf |
| work_keys_str_mv | AT rashidzahraam artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes AT balaramallam artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes AT lavanyachinthamalla artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes AT marypshyamalaanto artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes AT tiwarimohit artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes AT pkchidambaram artificialintelligenceinhealthcareapplicationschallengesandopportunitiesforimprovedpatientoutcomes |