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Digital Health Technologies and Innovations
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Kelly H. Zou, Mina B. Riad, Shaantanu Donde, Joan van der Horn, Tarek A. Hassan
Telemedicine is “the ability of physicians and patients to connect via technology other than through virtual interactive physician/patient capabilities, especially enabling rural and out of-area patients to be seen by specialists remotely” (California Code of Regulation).
Reliable Biomedical Applications Using AI Models
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Shambhavi Mishra, Tanveer Ahmed, Vipul Mishra
Given the significant advances in AI for imaging analysis, most radiology and pathology images are expected to be reviewed by a computer at some time. Patient communication and clinical note capture, and this trend will continue with the help of speech and text. The most difficult hurdle for AI in many healthcare fields is not whether the technology is capable enough to be useful, but ensuring its acceptance in daily clinical practice. AI systems must be approved by regulators, integrated with electronic heath record (EHR) systems, standardized to the point that similar products perform in a similar way, taught to clinicians, paid for by public or private payer organizations, and updated in the field over time in order for widespread adoption to occur. These obstacles will be overcome in the end, but it will take considerably longer than the technologies themselves to mature.
Introduction to E-Monitoring for Healthcare
Published in Govind Singh Patel, Seema Nayak, Sunil Kumar Chaudhary, Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, 2023
Seema Nayak, Shamla Mantri, Manoj Nayak, Amrita Rai
Throughout the COVID-19 pandemic, there has been a need to study various applications of IoT-enabled healthcare systems. Cutting-edge information technologies have opened a new door to innovation in our everyday lives. IoT is a developing technology that provides enhancement and good solutions in the medical field, like proper medical record keeping, sampling, integration of devices, and tracking causes of diseases. Sensor-based IoT technology provides an excellent way to reduce the risk of surgery during complicated cases and is helpful in COVID-19-type pandemics. In the medical field, IoT’s focus is to perform the treatment of different COVID-19 cases accurately. Using new technology to minimize risks and increase overall performance also make a surgeon’s job easier. This technology opens up many unique healthcare opportunities for medical students, who can be trained for disease detection and for the future course of action. Use of IoT can help to resolve different medical challenges like speed, price, and complexity. It has also improved the overall performance of healthcare system during the COVID-19 pandemic.
Telemedicine beyond the pandemic: challenges in the pediatric immunology clinic
Published in Expert Review of Clinical Immunology, 2023
Aarti Pandya, Sonya Parashar, Morgan Waller, Jay Portnoy
The combination of telemedicine augmented by remote monitoring with EMRs makes it possible to envision the advent of computer-assisted diagnosis (CAD). This technology uses artificial intelligence (AI) and machine learning algorithms to analyze medical information including images, clinical and physiologic data, test results and remotely obtained information to support healthcare providers in making accurate diagnoses. They use algorithms to identify patterns and relationships that are indicative of specific diseases or conditions. The technology can then provide healthcare providers with a list of potential diagnoses and recommendations for further testing or treatment. By analyzing vast amounts of data and recognizing patterns that may be difficult for a human to detect, CAD systems can provide a more comprehensive and accurate assessment of a patient’s condition.
A review of current biomarkers in chronic rhinosinusitis with or without nasal polyps
Published in Expert Review of Clinical Immunology, 2023
Tsuguhisa Nakayama, Shin-Ichi Haruna
Precision medicine is similar to personalized medicine which provides optimal medical care for single individual patients, but it refers to medical care provided to a population with a specific subtype of a disease on the basis of genetic, environmental, and lifestyle factors [1]. Precision medicine is expected to have multiple benefits in patient treatment; it represents an effective and efficient treatment approach that may improve patient satisfaction and reduce healthcare costs and socioeconomic burden. The realization of precision medicine involves various technological innovations, such as the evolution of next-generation sequencing technology, multi-omics analysis, and image analysis technology. The impact of artificial intelligence (AI) as a tool to interpret the data obtained from these brand-new technologies is also important [2,3].
Artificial Intelligence in Ophthalmology – Status Quo and Future Perspectives
Published in Seminars in Ophthalmology, 2023
Philomena A. Wawer Matos, Robert P. Reimer, Alexander C. Rokohl, Liliana Caldeira, Ludwig M. Heindl, Nils Große Hokamp
Artificial intelligence (AI) is a disruptive technology that has gained importance in many fields including medicine. Naturally, the primary domain of AI methods lies in perception and hence signal processing and in line with this domain comes the early adoption in medical imaging. Being fully digital for more than a decade, radiology has been one of the earliest adaptors of AI algorithms; however, recently numerous other fields using non DICOM-data (i.e. data that are not stored using the world-wide recognized standard for radiological imaging data) have emerged.1 The well-recognized fields include dermatology (e.g. differentiation of moles from melanoma) or pathology while data on AI applications in ophthalmology are emerging.2,3 To understand the opportunities and challenges, it is crucial to understand the fundamental concepts of AI algorithms. To survey the status quo a semi-structured review of current research in this field is conducted that also highlights current limitations and future opportunities of AI in ophthalmology.