Human-AI Collaboration in Healthcare: A Review and Research Agenda

Advances in Artificial Intelligence (AI) have led to the rise of human-AI collaboration. In healthcare, such collaboration could mitigate the shortage of qualified healthcare workers, assist overworked medical professionals, and improve the quality of healthcare. However, many challenges remain, such as investigating biases in clinical decision-making, the lack of trust in AI and adoption issues. While there is a growing number of studies on the topic, they are in disparate fields, and we lack a summary understanding of this research. To address this issue, this study conducts a literature review to examine prior research, identify gaps, and propose future research directions. Our findings indicate that there are limited studies about the evolving and interactive collaboration process in healthcare, the complementarity of humans and AI, the adoption and perception of AI, and the long-term impact on individuals and healthcare organizations. Additionally, more theory-driven research is needed to inform the design, implementation, and use of collaborative AI for healthcare and to realize its benefits

Embodied interaction with visualization and spatial navigation in time-sensitive scenarios

Paraphrasing the theory of embodied cognition, all aspects of our cognition are determined primarily by the contextual information and the means of physical interaction with data and information. In hybrid human-machine systems involving complex decision making, continuously maintaining a high level of attention while employing a deep understanding concerning the task performed as well as its context are essential. Utilizing embodied interaction to interact with machines has the potential to promote thinking and learning according to the theory of embodied cognition proposed by Lakoff. Additionally, the hybrid human-machine system utilizing natural and intuitive communication channels (e.g., gestures, speech, and body stances) should afford an array of cognitive benefits outstripping the more static forms of interaction (e.g., computer keyboard). This research proposes such a computational framework based on a Bayesian approach; this framework infers operator\u27s focus of attention based on the physical expressions of the operators. Specifically, this work aims to assess the effect of embodied interaction on attention during the solution of complex, time-sensitive, spatial navigational problems. Toward the goal of assessing the level of operator\u27s attention, we present a method linking the operator\u27s interaction utility, inference, and reasoning. The level of attention was inferred through networks coined Bayesian Attentional Networks (BANs). BANs are structures describing cause-effect relationships between operator\u27s attention, physical actions and decision-making. The proposed framework also generated a representative BAN, called the Consensus (Majority) Model (CMM); the CMM consists of an iteratively derived and agreed graph among candidate BANs obtained by experts and by the automatic learning process. Finally, the best combinations of interaction modalities and feedback were determined by the use of particular utility functions. This methodology was applied to a spatial navigational scenario; wherein, the operators interacted with dynamic images through a series of decision making processes. Real-world experiments were conducted to assess the framework\u27s ability to infer the operator\u27s levels of attention. Users were instructed to complete a series of spatial-navigational tasks using an assigned pairing of an interaction modality out of five categories (vision-based gesture, glove-based gesture, speech, feet, or body balance) and a feedback modality out of two (visual-based or auditory-based). Experimental results have confirmed that physical expressions are a determining factor in the quality of the solutions in a spatial navigational problem. Moreover, it was found that the combination of foot gestures with visual feedback resulted in the best task performance (p\u3c .001). Results have also shown that embodied interaction-based multimodal interface decreased execution errors that occurred in the cyber-physical scenarios (p \u3c .001). Therefore we conclude that appropriate use of interaction and feedback modalities allows the operators maintain their focus of attention, reduce errors, and enhance task performance in solving the decision making problems

Työn uusjako - Sosiaali- ja terveydenhuollon ammattiryhmien työnkuvien

Hankkeen tavoitteena oli kuvata, selittää ja arvioida sosiaali- ja terveydenhuollon ammattiryhmien työnkuvien ja keskinäisen työnjaon kohdistamista ja tehostamisesta. Tutkimushankkeessa toteutettiin kaksi systemoitua kirjallisuuskatsausta, dokumenttianalyysi, empiirinen kysely, asiantuntijahaastattelu sekä rekisteritutkimus. Tutkimushankkeen tulokset vahvistavat aikaisempaa tutkimustietoa siitä, että sosiaali- ja terveydenhuollon työnjako on eriytynyttä ja, että asiakaslähtöisyyden toteuttaminen edellyttää työn uudelleenjakoa. Sote-työnjaon asiakaslähtöisyyttä tukevia mahdollisuuksia ovat uudet palvelumuodot, uuden teknologian tuomat ratkaisut sekä työnjaon osaamisperustainen paikallinen sopiminen. Palveluiden laadun ja määrällisen riittävyyden turvaamiseksi sekä asiakaslähtöisyyden mahdollistamiseksi sote-työnjakoa on arvioitava ja kehitettävä yksilöiden, ammattien ja maantieteellisten alueiden välillä. Työnjaon ja sote-henkilöstön rakenteen on sopeuduttava muuttuvaan toimintaympäristöön. Koska muutos itsessään on pysyvä tila, pysyviksi suunnitellut työnjaon ratkaisut eivät ole toimivia. Työnjaon malleihin on sisään rakennettava joustavuus, mikä tarkoittaa erilaista työnjakoa erilaisissa ympäristöissä sekä työn jakamista oikealla tasolla. Vaikka työn uusjaolla esimerkiksi henkilöstörakennetta muuttamalla uusia teknologioita käyttöönottamalla ei saavutettaisi välittömiä kustannussäästöjä tai palvelun laadun kohenemista, keskeistä on tunnistaa mielekkään ja kohdistetun työnjaon välilliset vaikutukset hoidon ja palvelun tuloksellisuudessa sekä henkilöstön työhyvinvoinnin ja asiakastyytyväisyys paranemisessa. Vaikutusten tunnistamisessa avainasemassa on työnjaon käsitteiden yksiselitteisyys ja mittareiden luotettavuus. Kaikkien työnjaon osallisten on ymmärrettävä työnjako samalla tavoin sekä miten työnjaon kehittämiseksi suunniteltujen toimien vaikutusta mitataan ja arvioidaa