People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea—paused or reduced breathing, respectively—each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical co-morbidities, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channel SpO2 signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for the automated classification of SA versus no SA using SpO2 signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL for SpO2 signal-based diagnosis of SA. . A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility of SpO2 signals in wearable devices for home-based SA detection.

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In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous sh, endangered sea cows, Dugong dugons, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on Lightweight Deep Seagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classi es the seagrasses quickly and with lesser parameters. The deepseagrass dataset is used to test LWDS's applicability.

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