{"id":92050,"date":"2023-08-11T09:58:32","date_gmt":"2023-08-11T09:58:32","guid":{"rendered":"https:\/\/www.techopedia.com"},"modified":"2023-09-08T08:34:09","modified_gmt":"2023-09-08T08:34:09","slug":"why-healthcare-needs-multimodal-ai-to-aid-informed-decisions","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making","title":{"rendered":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions"},"content":{"rendered":"

In the rapidly evolving landscape of artificial intelligence<\/a> (AI), single-input systems dominate the field.<\/p>\n

However, healthcare professionals rely on a diverse spectrum of input \u2013 from patient records to direct observations \u2013 to make critical decisions.<\/p>\n

Multimodal AI<\/a> may be the paradigm shift that closes the gap in healthcare, with immense potential to elevate decision-making processes by having access to data from multiple silos.<\/p>\n

AI in Healthcare<\/span><\/h2>\n

Artificial intelligence (AI) has become an integral component of healthcare, with the ability to transform various aspects of medical practice and research.<\/p>\n

AI-powered algorithms can analyze complex medical data, such as imaging scans and genetic information, with remarkable speed and accuracy, aiding in disease diagnosis and treatment planning.<\/p>\n

Additionally, AI-driven predictive models<\/a> enhance patient care by forecasting disease trends and patient outcomes.<\/p>\n

Moreover, administrative tasks are streamlined through automation, allowing healthcare professionals to allocate more time to patient interaction, with AI potentially tackling the global shortage of radiologists<\/a>.<\/p>\n

AI is effectively utilized across various medical modalities, rapidly detecting irregularities in radiological scans, deciphering complex biomedical signals for early disease detection, and enabling tailored treatment approaches by analyzing genetic data. Furthermore, AI enhances clinical decision-making<\/a> and predictive outcomes, for instance integrating generative AI <\/a>into\u00a0electronic health records<\/a>.<\/p>\n

Nonetheless, while AI has predominantly been deployed to analyze individual data modalities, this unimodal AI approach has several limitations in healthcare:<\/p>\n

\u2022 Incomplete Overview:<\/strong> Unimodal AI systems lack the capacity to consider a holistic view of a patient’s condition. For example, an AI system focused solely on medical images might overlook vital information in clinical notes or genetic data.<\/p>\n

\u2022 Performance Limitations:<\/strong> Depending solely on a single data source can result in restricted diagnostic accuracy, particularly when addressing intricate cases demanding a multidimensional approach.<\/p>\n

\u2022 Data Silos and Lack of Integration:<\/strong> Unimodal AI systems may be developed independently for each data source, leading to data silos<\/a> and difficulties in integrating insights from different sources.<\/p>\n

\u2022 Restricted Adaptability:<\/strong> Unimodal AI systems are often designed to perform specific tasks on specific data types. Adapting them to new tasks or data types can be challenging.<\/p>\n

What is Multimodal AI?<\/span><\/h2>\n

Multimodal AI refers to AI systems that are designed to process and understand information from multiple sources or types of data simultaneously.<\/p>\n

These data sources, known as modalities, can include various forms of inputs such as text, images, audio, video, sensor data, and more. Multimodal AI aims to enable machines to leverage the combined insights and context provided by these diverse data modalities to make more accurate and holistic predictions or decisions.<\/p>\n

In contrast to traditional AI systems that often focus on a single type of data input, multimodal AI harness the power of different modalities <\/a>to gain a comprehensive understanding of a situation or problem. This approach mirrors how humans naturally process information by considering various sensory inputs and contextual cues when making decisions.<\/p>\n

Multimodal AI in Healthcare<\/span><\/h2>\n

Healthcare is fundamentally multimodal due to the diverse and interconnected nature of information and data involved in the medical field.<\/p>\n

When delivering healthcare, medical professionals routinely decipher information from a wide array of sources, including medical images, clinical notes, laboratory tests, electronic health records, genomics, and more.<\/p>\n

They synthesize information from multiple modalities to form a comprehensive understanding of a patient’s condition, which enables them to make accurate diagnoses and effective treatments.<\/p>\n

The multiple modalities that healthcare professionals typically consider include:<\/p>\n

\u2022 Medical Images:<\/strong> These range from X-rays, MRI<\/a> scans, CT scans, ultrasounds, and more. Each type of image provides unique insights into different aspects of a patient’s anatomy and condition.<\/p>\n

\u2022 Clinical Notes:<\/strong> These are the written records of a patient’s medical history, symptoms, and progress. These notes are often taken by different healthcare professionals over time and need to be integrated to provide a holistic view.<\/p>\n

\u2022 Lab Tests:<\/strong> These encompass various tests, such as blood tests, urine tests, and genetic tests. Each test provides specific data points that help diagnose and monitor health conditions.<\/p>\n

\u2022 Electronic Health Records (EHRs):<\/strong> These digital records contain a patient’s medical history, diagnoses, medications, treatment plans, and more. EHRs centralize patient information for easy access but require careful interpretation to extract relevant insights.<\/p>\n

\u2022 Genomic Data:<\/strong> With advancements in genetics, healthcare now involves analyzing a patient’s genetic structure to understand his susceptibility to certain diseases and tailor treatment plans accordingly.<\/p>\n

\u2022 Patient Monitoring Devices:<\/strong> Devices like heart rate monitors, blood pressure monitors, and wearable fitness trackers provide real-time data on a patient’s health, contributing to the overall diagnostic process.<\/p>\n

\u2022 Medical Literature:<\/strong> The constantly evolving landscape of medical research and literature provides additional information that healthcare professionals need to consider when making decisions.<\/p>\n

How Multimodal AI Overcomes Challenges Faced by Traditional AI<\/span><\/h2>\n

Multimodal AI in healthcare can overcome the challenges of unimodal AI in the following ways:<\/p>\n

\u2022 Holistic Perspective:<\/strong> Multimodal AI combines information from diverse sources, providing a holistic view of a patient’s health. Integrating data from medical images, clinical notes, lab results, genomics, and more can offer a more accurate and complete understanding of the patient’s condition.<\/p>\n

\u2022 Enhanced Predictions:<\/strong> By leveraging data from multiple sources, multimodal AI can enhance diagnostic accuracy. It can identify patterns and correlations that might be missed by analyzing each modality independently, leading to more accurate and timely diagnoses.<\/p>\n

\u2022 Integrated Insights:<\/strong> Multimodal AI promotes data integration by combining insights from various modalities. This facilitates healthcare professionals’ access to a unified patient information view, fostering collaboration and well-informed decision-making.<\/p>\n

\u2022 Adaptability and Flexibility:<\/strong> Multimodal AI’s ability to learn from various data types equips it to adapt to new challenges, data sources, and medical advancements. It can be trained in different contexts and evolve with changing healthcare paradigms.<\/p>\n

Opportunities of Multimodal AI in Healthcare<\/span><\/h2>\n

Apart from overcoming the challenges of traditional unimodal AI, multimodal AI presents numerous additional opportunities for healthcare. Some of these are mentioned below.<\/p>\n

\u2022 Personalized Precision Health:<\/strong> By integrating diverse data, including ‘omics’ data like genomics<\/a>, proteomics<\/a>, and metabolomics<\/a>, along with electronic health records (EHR) and imaging, we can enable customized approaches to prevent, diagnose, and treat health issues effectively.<\/p>\n

\u2022 Digital Trials:<\/strong> The fusion of wearable sensor<\/a> data with clinical information can transform medical research by enhancing engagement and predictive insights, as exemplified during the COVID-19 pandemic.<\/p>\n

\u2022 Remote Patient Monitoring:<\/strong> Progress in biosensors, continuous tracking, and analysis enables home-based hospital setups, reducing costs, lowering healthcare workforce needs, and providing better emotional assistance.<\/p>\n

\u2022 Pandemic Surveillance and Outbreak Detection:<\/strong> COVID-19 has highlighted the need for robust infectious disease monitoring. Countries have utilized varied data like migration patterns, mobile usage, and health delivery data to forecast outbreaks and detect cases.<\/p>\n

\u2022 Digital Twins:<\/strong> The digital twins<\/a> originate from engineering and have the potential to replace traditional clinical trials by predicting therapy impact on patients. These models, rooted in complex systems, enable rapid strategy testing. Digital twins advance drug discovery in healthcare, especially in oncology and heart health. Collaborations like the Swedish Digital Twins Consortium highlight cross-sector partnerships. AI models learning from varied data drive real-time healthcare predictions.<\/p>\n

Challenges of Multimodal AI in Healthcare<\/span><\/h2>\n

Despite its numerous benefits and opportunities, implementing multimodal AI in healthcare is not without challenges. Some of the key challenges are as follows:<\/p>\n

\u2022 Availability of Data:<\/strong> Multimodal AI models necessitate extensive and varied datasets for their training and validation. The limited accessibility of such datasets presents a substantial obstacle to multimodal AI for healthcare.<\/p>\n

\u2022 Data Integration and Quality:<\/strong> Integrating data from various sources while maintaining high data quality<\/a> can be complex. Inaccuracies or inconsistencies in data across modalities can hinder the performance of AI models.<\/p>\n

\u2022 Data Privacy and Security:<\/strong> Combining data from multiple sources raises concerns about patient privacy and data security<\/a>. Ensuring compliance with regulations like HIPAA<\/a> while sharing and analyzing data is crucial.<\/p>\n

\u2022 Model Complexity and Interpretability:<\/strong> Multimodal AI models can be intricate, making it challenging to interpret their decision-making processes. Transparent and explainable models<\/a> are essential to gain the trust of healthcare professionals.<\/p>\n

\u2022 Domain Expertise:<\/strong> Developing effective Multimodal AI systems requires a deep understanding of AI techniques and medical domain knowledge. Collaboration between AI experts and healthcare professionals is vital.<\/p>\n

\u2022 Ethical Considerations:<\/a><\/strong> The ethical implications of AI in healthcare, including fairness<\/a>, accountability, and bias<\/a>, become more complex when dealing with multiple data sources.<\/p>\n

The Bottom Line<\/span><\/h2>\n

Incorporating diverse sources of information is critical in healthcare decision-making, yet current AI systems often focus on singular data types.<\/p>\n

Multimodal AI, which integrates various data modalities like images, text, and numbers, has the potential to revolutionize healthcare. It enhances diagnostic accuracy, promotes collaboration, and adapts to new challenges.<\/p>\n

While presenting opportunities like personalized precision health, digital trials, and pandemic surveillance, it also faces challenges like data availability, integration, privacy concerns, model complexity, and the need for domain expertise.<\/p>\n

Multimodal AI integration could improve patient care, research, and predictive capabilities, reshaping the healthcare landscape.<\/p>\n","protected":false},"excerpt":{"rendered":"

In the rapidly evolving landscape of artificial intelligence (AI), single-input systems dominate the field. However, healthcare professionals rely on a diverse spectrum of input \u2013 from patient records to direct observations \u2013 to make critical decisions. Multimodal AI may be the paradigm shift that closes the gap in healthcare, with immense potential to elevate decision-making […]<\/p>\n","protected":false},"author":7914,"featured_media":92397,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"","_lmt_disable":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[573,583,599,1],"tags":[1172],"category_partsoff":[],"class_list":["post-92050","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-healthcare-it","category-machine-learning","category-uncategorized","tag-multimodal-ai"],"acf":[],"yoast_head":"\nWhy Healthcare Needs Multimodal AI to Aid Informed Decisions<\/title>\n<meta name=\"description\" content=\"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.\" \/>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why Healthcare Needs Multimodal AI to Aid Informed Decisions\" \/>\n<meta property=\"og:description\" content=\"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\" \/>\n<meta property=\"og:site_name\" content=\"Techopedia\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/techopedia\/\" \/>\n<meta property=\"article:author\" content=\"https:\/\/scholar.google.com.pk\/citations?user=CuQ9S_kAAAAJ&hl=en\" \/>\n<meta property=\"article:published_time\" content=\"2023-08-11T09:58:32+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-08T08:34:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Dr. Tehseen Zia\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@techopedia\" \/>\n<meta name=\"twitter:site\" content=\"@techopedia\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. Tehseen Zia\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\"},\"author\":{\"name\":\"Dr. Tehseen Zia\",\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/person\/fb77f994a3e084b6598f08565f584002\"},\"headline\":\"Why Healthcare Needs Multimodal AI to Aid Informed Decisions\",\"datePublished\":\"2023-08-11T09:58:32+00:00\",\"dateModified\":\"2023-09-08T08:34:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\"},\"wordCount\":1459,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.techopedia.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png\",\"keywords\":[\"Multimodal AI\"],\"articleSection\":\"\",\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#respond\"]}],\"copyrightYear\":\"2023\",\"copyrightHolder\":{\"@id\":\"https:\/\/www.techopedia.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\",\"url\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\",\"name\":\"Why Healthcare Needs Multimodal AI to Aid Informed Decisions\",\"isPartOf\":{\"@id\":\"https:\/\/www.techopedia.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png\",\"datePublished\":\"2023-08-11T09:58:32+00:00\",\"dateModified\":\"2023-09-08T08:34:09+00:00\",\"description\":\"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage\",\"url\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png\",\"contentUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png\",\"width\":1200,\"height\":600,\"caption\":\"UnitedHealth Data Breach: Many Americans Exposed on Dark Web\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.techopedia.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Artificial Intelligence\",\"item\":\"https:\/\/www.techopedia.com\/ai\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Why Healthcare Needs Multimodal AI to Aid Informed Decisions\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.techopedia.com\/#website\",\"url\":\"https:\/\/www.techopedia.com\/\",\"name\":\"Techopedia\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/www.techopedia.com\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.techopedia.com\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.techopedia.com\/#organization\",\"name\":\"Techopedia\",\"url\":\"https:\/\/www.techopedia.com\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2025\/02\/techopedia-light-logo.svg\",\"contentUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2025\/02\/techopedia-light-logo.svg\",\"caption\":\"Techopedia\"},\"image\":{\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/techopedia\/\",\"https:\/\/x.com\/techopedia\",\"https:\/\/www.linkedin.com\/company\/techopedia\/\",\"https:\/\/www.youtube.com\/c\/Techopedia\"],\"publishingPrinciples\":\"https:\/\/www.techopedia.com\/about\/editorial-policy\",\"ownershipFundingInfo\":\"https:\/\/www.techopedia.com\/about\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/person\/fb77f994a3e084b6598f08565f584002\",\"name\":\"Dr. Tehseen Zia\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.techopedia.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/02\/tehseen_zia.jpg\",\"contentUrl\":\"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/02\/tehseen_zia.jpg\",\"caption\":\"Dr. Tehseen Zia\"},\"description\":\"Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is Tenured Associate Professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.\",\"sameAs\":[\"https:\/\/scholar.google.com.pk\/citations?user=CuQ9S_kAAAAJ&hl=en\",\"https:\/\/www.linkedin.com\/in\/tehseen-zia-48764638\/?originalSubdomain=pk\"],\"knowsAbout\":[\"Tenured Associate Professor\",\"COMSATS University Islamabad (CUI)\"],\"url\":\"https:\/\/www.techopedia.com\/contributors\/dr-tehseen-zia\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions","description":"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.","robots":{"index":"noindex","follow":"follow"},"og_locale":"en_US","og_type":"article","og_title":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions","og_description":"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.","og_url":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making","og_site_name":"Techopedia","article_publisher":"https:\/\/www.facebook.com\/techopedia\/","article_author":"https:\/\/scholar.google.com.pk\/citations?user=CuQ9S_kAAAAJ&hl=en","article_published_time":"2023-08-11T09:58:32+00:00","article_modified_time":"2023-09-08T08:34:09+00:00","og_image":[{"width":1200,"height":600,"url":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png","type":"image\/png"}],"author":"Dr. Tehseen Zia","twitter_card":"summary_large_image","twitter_creator":"@techopedia","twitter_site":"@techopedia","twitter_misc":{"Written by":"Dr. Tehseen Zia","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#article","isPartOf":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making"},"author":{"name":"Dr. Tehseen Zia","@id":"https:\/\/www.techopedia.com\/#\/schema\/person\/fb77f994a3e084b6598f08565f584002"},"headline":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions","datePublished":"2023-08-11T09:58:32+00:00","dateModified":"2023-09-08T08:34:09+00:00","mainEntityOfPage":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making"},"wordCount":1459,"commentCount":0,"publisher":{"@id":"https:\/\/www.techopedia.com\/#organization"},"image":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage"},"thumbnailUrl":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png","keywords":["Multimodal AI"],"articleSection":"","inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#respond"]}],"copyrightYear":"2023","copyrightHolder":{"@id":"https:\/\/www.techopedia.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making","url":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making","name":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions","isPartOf":{"@id":"https:\/\/www.techopedia.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage"},"image":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage"},"thumbnailUrl":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png","datePublished":"2023-08-11T09:58:32+00:00","dateModified":"2023-09-08T08:34:09+00:00","description":"Unlike conventional AI, which relies on singular inputs, Multimodal AI processes diverse data \u2013 from medical images to clinical notes.","breadcrumb":{"@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#primaryimage","url":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png","contentUrl":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/08\/ipad_screen_with_pie_charts_01.png","width":1200,"height":600,"caption":"UnitedHealth Data Breach: Many Americans Exposed on Dark Web"},{"@type":"BreadcrumbList","@id":"https:\/\/www.techopedia.com\/leveraging-multimodal-ai-for-enhanced-healthcare-decision-making#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.techopedia.com\/"},{"@type":"ListItem","position":2,"name":"Artificial Intelligence","item":"https:\/\/www.techopedia.com\/ai"},{"@type":"ListItem","position":3,"name":"Why Healthcare Needs Multimodal AI to Aid Informed Decisions"}]},{"@type":"WebSite","@id":"https:\/\/www.techopedia.com\/#website","url":"https:\/\/www.techopedia.com\/","name":"Techopedia","description":"","publisher":{"@id":"https:\/\/www.techopedia.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.techopedia.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.techopedia.com\/#organization","name":"Techopedia","url":"https:\/\/www.techopedia.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.techopedia.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2025\/02\/techopedia-light-logo.svg","contentUrl":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2025\/02\/techopedia-light-logo.svg","caption":"Techopedia"},"image":{"@id":"https:\/\/www.techopedia.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/techopedia\/","https:\/\/x.com\/techopedia","https:\/\/www.linkedin.com\/company\/techopedia\/","https:\/\/www.youtube.com\/c\/Techopedia"],"publishingPrinciples":"https:\/\/www.techopedia.com\/about\/editorial-policy","ownershipFundingInfo":"https:\/\/www.techopedia.com\/about"},{"@type":"Person","@id":"https:\/\/www.techopedia.com\/#\/schema\/person\/fb77f994a3e084b6598f08565f584002","name":"Dr. Tehseen Zia","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.techopedia.com\/#\/schema\/person\/image\/","url":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/02\/tehseen_zia.jpg","contentUrl":"https:\/\/www.techopedia.com\/wp-content\/uploads\/2023\/02\/tehseen_zia.jpg","caption":"Dr. Tehseen Zia"},"description":"Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is Tenured Associate Professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.","sameAs":["https:\/\/scholar.google.com.pk\/citations?user=CuQ9S_kAAAAJ&hl=en","https:\/\/www.linkedin.com\/in\/tehseen-zia-48764638\/?originalSubdomain=pk"],"knowsAbout":["Tenured Associate Professor","COMSATS University Islamabad (CUI)"],"url":"https:\/\/www.techopedia.com\/contributors\/dr-tehseen-zia"}]}},"modified_by":"vukstojkovic","_links":{"self":[{"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/posts\/92050","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/users\/7914"}],"replies":[{"embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/comments?post=92050"}],"version-history":[{"count":0,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/posts\/92050\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/media\/92397"}],"wp:attachment":[{"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/media?parent=92050"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/categories?post=92050"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/tags?post=92050"},{"taxonomy":"category_partsoff","embeddable":true,"href":"https:\/\/www.techopedia.com\/wp-json\/wp\/v2\/category_partsoff?post=92050"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}