{"id":184021,"date":"2026-11-09T16:30:10","date_gmt":"2026-11-09T15:30:10","guid":{"rendered":"http:\/\/uncode.beger-design.com\/?post_type=portfolio&p=184021"},"modified":"2026-11-11T10:18:11","modified_gmt":"2026-11-11T09:18:11","slug":"ask-ki-workstation","status":"publish","type":"portfolio","link":"https:\/\/begerdesign.de\/en\/portfolio\/ask-ki-workstation\/","title":{"rendered":"ASK-KI Workstation"},"content":{"rendered":"

[vc_row row_height_percent=”85″ override_padding=”yes” h_padding=”2″ top_padding=”5″ bottom_padding=”5″ back_image=”184292″ back_position=”center center” overlay_alpha=”0″ gutter_size=”3″ column_width_percent=”100″ shift_y=”0″ z_index=”0″ bottom_divider=”gradient-soft” auto_height_device=”yes” content_parallax=”0″ uncode_shortcode_id=”201607″][vc_column column_width_percent=”100″ position_horizontal=”left” gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_width=”0″ css_animation=”alpha-anim” animation_speed=”1000″ width=”1\/1″ uncode_shortcode_id=”136888″][\/vc_column][\/vc_row][vc_row row_height_percent=”0″ override_padding=”yes” h_padding=”2″ top_padding=”0″ bottom_padding=”0″ overlay_alpha=”50″ gutter_size=”3″ column_width_percent=”100″ shift_y=”0″ z_index=”2″ uncode_shortcode_id=”160945″][vc_column column_width_percent=”100″ gutter_size=”3″ override_padding=”yes” column_padding=”4″ back_color=”color-xsdn” overlay_alpha=”50″ shadow=”std” shift_x=”0″ shift_y=”-4″ shift_y_fixed=”yes” shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_width=”0″ width=”1\/1″ uncode_shortcode_id=”196526″ back_color_type=”uncode-palette”][vc_row_inner row_inner_height_percent=”0″ overlay_alpha=”50″ gutter_size=”4″ shift_y=”0″ z_index=”0″ limit_content=”” uncode_shortcode_id=”147759″][vc_column_inner column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”7″ mobile_width=”0″ width=”6\/12″ uncode_shortcode_id=”467974″][vc_custom_heading text_size=”h1″ uncode_shortcode_id=”657468″]ASK-KI Workstation[\/vc_custom_heading][vc_empty_space empty_h=”3″ desktop_visibility=”yes” mobile_visibility=”yes”][\/vc_column_inner][vc_column_inner column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”4″ mobile_width=”0″ width=”3\/12″ uncode_shortcode_id=”166299″][vc_custom_heading heading_semantic=”h3″ text_size=”h4″ uncode_shortcode_id=”192122″ subheading=”ASK-KI”]Research Project[\/vc_custom_heading][vc_custom_heading heading_semantic=”h3″ text_size=”h4″ uncode_shortcode_id=”482804″ subheading=”UX \/ UI”]Services[\/vc_custom_heading][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row unlock_row_content=”yes” row_height_percent=”0″ override_padding=”yes” h_padding=”5″ top_padding=”5″ bottom_padding=”5″ overlay_alpha=”50″ gutter_size=”3″ column_width_percent=”100″ shift_y=”0″ z_index=”0″ uncode_shortcode_id=”137580″ el_class=”test-zero”][vc_column column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_width=”0″ width=”1\/1″ uncode_shortcode_id=”170612″][vc_tabs vertical=”yes” history=”yes” target=”row” tab_no_fade=”yes” typography=”advanced” tab_no_border=”yes” tab_h_border=”yes” tab_custom_size=”yes” tab_size=”3″ tab_gap=”3″ custom_padding=”yes” gutter_tab=”2″ active_bg_color=”color-xsdn” radius=”std” tab_tablet_bp=”yes” accordion_bp=”yes” titles_size=”h3″ titles_weight=”600″ gutter_simple=”1″ uncode_shortcode_id=”565016″ active_bg_color_type=”uncode-palette”][vc_tab gutter_size=”2″ column_padding=”0″ title=”User Interface” tab_id=”1671531983-1-92167282140965917157660734888639-b55e31792592099469″][vc_custom_heading uncode_shortcode_id=”214625″]The Key Innovation[\/vc_custom_heading][vc_empty_space empty_h=”2″][vc_row_inner][vc_column_inner width=”1\/3″][vc_column_text uncode_shortcode_id=”477849″]<\/p>\n

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The goal of the project is to develop AI-assisted process control to support and alleviate the workload of OR and AEMP management. The AI identifies bottlenecks in real time, prioritizes instrument sets\u2014for example, in emergencies\u2014and provides recommendations for action that can be reviewed by those in charge.<\/p>\n

Interoperability and Data Acquisition<\/strong><\/p>\n

Existing systems in the OR, OR management, and AEMP are networked to collect relevant data (devices, maintenance, inventory) and make it available for process control.<\/p>\n

Development of Workstations<\/strong><\/p>\n

Interactive cockpits for OR management and AEMP display process data, alerts, and recommendations and are tailored to workflows.<\/p>\n

ASK AI Assistant<\/strong><\/p>\n

The AI processes data from the OR and AEMP, generates recommendations, and learns from feedback to adapt to the hospital\u2019s specific needs.<\/p>\n<\/div>\n

[\/vc_column_text][\/vc_column_inner][vc_column_inner column_width_percent=”86″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_width=”0″ width=”2\/3″ uncode_shortcode_id=”206772″][vc_single_image media=”184430″ media_lightbox=”yes” media_width_percent=”50″ uncode_shortcode_id=”535591″][\/vc_column_inner][\/vc_row_inner][vc_empty_space empty_h=”2″][vc_column_text uncode_shortcode_id=”167049″]The AI is continuously refined by utilizing real and simulated process data and responding to feedback from the workstations to increase the efficiency and
\nadaptability of the process control system.[\/vc_column_text][vc_empty_space empty_h=”2″][vc_gallery el_id=”gallery-169299″ medias=”184215,184214,184213,184212″ gutter_size=”3″ screen_lg=”1000″ screen_md=”600″ screen_sm=”480″ single_width=”5″ single_overlay_opacity=”1″ single_padding=”2″ single_border=”yes” uncode_shortcode_id=”666550″][\/vc_tab][vc_tab gutter_size=”2″ column_padding=”0″ title=”Research” tab_id=”1671531983-2-89167282140965917157660734888639-b55e91792592099469″][vc_custom_heading uncode_shortcode_id=”345506″]Assisted Sterile Supply Cycle with Artificial Intelligence (ASK-KI)[\/vc_custom_heading][vc_empty_space empty_h=”2″][vc_single_image media=”184432″ media_width_percent=”100″ uncode_shortcode_id=”152179″][vc_empty_space empty_h=”2″][vc_column_text uncode_shortcode_id=”158559″]Background and Problem Statement<\/strong>
\nThe successful performance of surgical procedures requires close interdisciplinary collaboration, particularly within the sterile supply chain, which encompasses resource planning, the performance of surgeries, and the reprocessing and provision of sterile supplies. Challenges such as unexpected complications, delays, or inadequate sterile supplies can lead to canceled surgeries. The management of this process has a significant impact on both patient care and hospital costs. Given the shortage of skilled workers and the need for resource-efficient planning, automation is becoming increasingly important. However, existing planning systems have shortcomings that result in inefficient supply chains and bottlenecks. Optimized sterile supply management and dynamic process control are therefore essential for achieving cost-effective and sustainable improvements.<\/p>\n

State of Science and Technology<\/strong>
\nModern supply chains rely on human-centered assistance systems and AI to enable rapid decision-making and efficient problem-solving. While concepts such as the \u201csmart warehouse\u201d and AI-supported optimizations are well established in industry, the healthcare sector faces challenges such as fluctuating material requirements and inflexible systems. AI could help here by optimizing resource utilization, improving demand forecasting, and increasing productivity. However, current inventory systems in sterilized supply management do not allow for real-time adjustments. Projects such as ZiMT and PriMed aim to integrate AEMP processes more closely with hospital logistics, thereby improving AI-supported planning. Nevertheless, there is still a lack of dynamic, interactive solutions that flexibly account for uncertainties in hospital operations.<\/p>\n

Description of the Innovation<\/strong>
\nThe project aims to establish AI-supported process control at the interface between the operating room, OR management, and the AEMP. The goal is to optimize the use of personnel, materials, and energy in order to counteract staff shortages and utilize resources sustainably. Through assistant cockpits, OR management and AEMP stations receive dynamically processed information, while AEMP staff can interactively feed process data back to the AI. The AI-based control system creates transparency in the instrument cycle, reduces delays, and minimizes risks for patients and staff. What makes this innovative is the open networking and utilization of existing data sources, as well as their integration into interactive AI technologies.[\/vc_column_text][vc_empty_space empty_h=”2″][vc_row_inner][vc_column_inner width=”1\/3″][vc_button size=”btn-xl” border_width=”0″ link=”url:https%3A%2F%2Fask-ki.de%2F|target:_blank” uncode_shortcode_id=”186711″]More about ASK-KI[\/vc_button][\/vc_column_inner][vc_column_inner width=”1\/3″][\/vc_column_inner][vc_column_inner column_width_percent=”100″ position_horizontal=”right” align_horizontal=”align_right” gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_width=”0″ width=”1\/3″ uncode_shortcode_id=”153032″][vc_single_image media=”184279″ media_width_percent=”85″ alignment=”right” uncode_shortcode_id=”199760″][\/vc_column_inner][\/vc_row_inner][\/vc_tab][\/vc_tabs][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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