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- Ghayvat, Hemant, et al.
(författare)
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Guest Editorial AIoPT (Artificial Intelligence of Paediatric Things) : Informatics in Meeting Paediatric Needs and Patient Monitoring
- 2023
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 27:6, s. 2600-2602
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Medical (health) informatics broadly encompasses the cognitive, information processing, and communication tasks inherent in medical practice, education, and research, with a particular emphasis on the development of computer-based patient records, decision support systems, information standards, data aggregation systems, communication systems, and educational programs for patients and health providers. In addition, this rapidly growing area is confronted with developing technological solutions sensitive to special populations' specific requirements, i.e., Preventive, Assistive, and Medical Children Health Informatics . First, children have distinct physiology, come from diverse backgrounds, and are disproportionately affected by illnesses. Thus, children are not little adults, as a famous adage among child health experts. These distinctions have been extensively discussed and are frequently called the four D's. Second, children depend on their parents and extended relatives to access necessary health care. Thus, plans must include gathering and distributing information to many patients. Third, childhood is defined by a developmental trajectory marked by fast change and the emergence of capacities for health information utilization. Fourth, children's health is defined by distinct epidemiology characterized by fewer significant chronic diseases, a high prevalence of acute illnesses, and reliance on preventative interventions. Finally, since children are the poorest and most varied in our society, they exhibit distinct demographic trends.
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| 2. |
- Henriquez, Pedro, et al.
(författare)
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Mirror Mirror on the Wall ... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization
- 2017
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Ingår i: IEEE transactions on multimedia. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1520-9210 .- 1941-0077. ; 19:7, s. 1467-1481
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Tidskriftsartikel (refereegranskat)abstract
- A persons well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.
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| 3. |
- Marti-Bonmati, Luis, et al.
(författare)
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Considerations for artificial intelligence clinical impact in oncologic imaging : an AI4HI position paper
- 2022
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Ingår i: Insights into Imaging. - : Springer. - 1869-4101. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
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