Artificial intelligent (AI) conversational agents hold a promising future in the field of mental health, especially in helping marginalized communities that lack access to mental health support services. It is tempting to have a 24/7 mental health companion that can be accessed anywhere using mobile phones to provide therapist-like advice. Yet, caution should be taken, and studies around their feasibility need to be surveyed. Before adopting such a rapidly changing technology, studies on its feasibility should be explored, summarized, and synthesized to gain a solid understanding of the status quo and to enable us to build a framework that can guide us throughout the development and deployment processes. Different perspectives must be considered when investigating the feasibility of AI conversational agents, including the mental healthcare professional perspective. The literature can provide insights into their perspectives in terms of opportunities, concerns, and implications. Mental health professionals, the subject-matter experts in this field, have their points of view that should be understood and considered. This systematic literature review will explore mental health practitioners' attitudes toward AI conversational agents and the factors that affect their adoption and recommendation of the technology to augment their services and treatments. The TAM3 Framework will be the lens through which this systematic literature review will be conducted.

"Al will not replace doctors but instead will augment them, enabling physicians to practice better medicine with greater accuracy and increased efficiency." Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology. Medical imaging professionals in the coming years will be able to use a rapidly expanding AI-enabled diagnostic toolkit for detecting, classifying, segmenting, and extracting quantitative imaging features. It will eventually lead to accurate medical data interpretation, enhanced diagnostic processes, and improved clinical outcomes.

The healthcare sector experienced transition as a result of COVID-19, and this shift will last for years to come. Despite industry obstacles, the pandemic has led to a growing acceptance of new technology among patients, providers, and healthcare practitioners. These technologies lessen workplace stress and improved patient care. But there is still hope for change. Many medical schools now include the use of technology in their curriculum; the new generation of medical practitioners has a distinct relationship with technology.

With advancement in science and technology, healthcare has improved dramatically over the last century. Average life expectancy has improved significantly. While there are many factors contributing to this, the use of technology in healthcare has played a major role in improving quality of life and increasing life expectancy. Some breakthrough technology interventions happened as early as 1958 when the first ever pacemaker was implanted in a person so that the heart pulsates artificially! Invention of MRI (Magnetic Resonance Imaging) machines to non-invasively diagnose health anomalies and Laser for vision correction are yet other examples of early disruption on technology in healthcare.

Machine learning-based technologies are playing a substantial role in the response to the COVID-19 pandemic. Experts are using machine learning to study the virus, test potential treatments, diagnose individuals, analyze the public health impacts, and more. Below, we describe some of the leading efforts and identify data protection and ethical issues related to machine learning and COVID-19, with a particular focus on apps directed to health care professionals that leverage audio-visual data, text analysis, chatbots, and sensors. "Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care." Now – with the development of the pandemic resulting from the spread of the coronavirus (COVID-19), medical providers, institutions, and commercial developers are all considering whether and how to apply machine learning to confront the threat of this current crisis.

Chronolife, an artificial intelligence company specialising in digital health, has secured Class IIa medical certification from the European Union for its medical grade smart T-shirt, KeeSense. The multi-sensor wearable device continuously monitors electrocardiography (ECG), thoracic respiration, abdominal respiration, skin temperature, thoracic impedance, and physical activity, enabling healthcare practitioners to remotely track vital clinical data about their patients. The platform's receipt of a CE mark allows KeeSense to be marketed in the European Economic Area as a wearable medical device for healthcare purposes including remote monitoring of patients with chronic diseases and support for diagnostics. The KeeSense T-shirt is designed for round-the-clock use, and is fully reusable and washable, mimicking similar daily use garments. The T-shirt transmits data to its paired smartphone app via Bluetooth, which then sends the data to a server for live or time-delayed analysis by the wearer's healthcare team.

Healthcare is one of the major success stories of our times, but it is also in crisis. Healthcare providers face rising demand, increasing delivery costs and workforce shortages, as well as the need to adapt to new ways of working that maximize on innovative technology to deliver better quality care. While most healthcare practitioners are unlikely to be displaced by AI, their professional lives will be augmented, supported and in some cases, challenged, by the introduction of AI. AI is not a panacea for healthcare systems – it comes with strings attached. Healthcare decision-makers, healthcare providers and healthcare practitioners must work together to resolve ethical and practical concerns around AI in healthcare and ensure populations reap the full benefits of AI.

Patients' wellbeing should be the central focus of healthcare. Studies show that doctors spend almost half their time on data entry and more than half of their workdays managing records instead of caring for patients. Add this lost time to the fact that healthcare in the United States is already understaffed – averaging 2.5 healthcare practitioners to every 1,000 patients – and it's easy to see that something's got to give to bring focus back to what really matters: actual patient care. Automation technologies, such as Robotic Process Automation (RPA), offer a chance to relieve overburdened healthcare practitioners of the time-intensive administrative work detracting from time spent with patients. RPA software allows users to configure "bots" to take on repetitive tasks by emulating and integrating the actions of a human within digital systems to perform a business process, eliminating often repetitive tasks from a person's workload.

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what should be automated. We quantitatively investigate these research questions by utilizing probabilistic machine learning models trained on thousands of ratings, provided by both healthcare practitioners and automation experts. Based on our findings, we present an analytical tool (Automatability-Desirability Matrix) to support policymakers and organizational leaders in developing practical strategies on how to harness the positive power of automation technologies, while accompanying change and empowering stakeholders in a participatory fashion.

After gaining the highest amount of venture funding in 2014, the healthcare industry became a viable target for startups. The US healthcare system is now worth $3,504 trillion and the industry is opening up hundreds of new opportunities for entrepreneurs. Right now, every healthcare practitioner is utilizing at least one mHealth application or medical tracking device to perform his or her duties faster and more efficiently. It won't be long until older technologies for patient monitoring and data gathering are replaced by healthcare apps with AI and machine learning functions. As they say, starting a business in the healthcare industry will require more preparation and bravery compared to venturing into other types of industries.