(p.2) In health care, AI is defined as the mimicking of human cognitive functions by computers. AI has been inspired by the functioning of biological neurons and includes the basics of sensing, recognition, and object recognition to enable machines to perform as good as or even better than humans. However, with the inherent lack of articulation and generation of insights, AI cannot replace physicians in health care.
With no universally applicable rules in health care, AI must be supplemented with physician judgment in many instances. An extensive correlation of history and clinical findings is needed for the diagnosis or monitoring...

(p.87) The problem is that we have a hard time knowing what lies ahead in this new frontier. Who would have thought a few decades ago that most people today would do their work by staring into PC monitors, laptops, and mobile devices? There is no fixed amount of human endeavor, and work of different kinds will always appear to fill new economic demands.

(p.89) Some new jobs may appear, but they will not last for long. Machines have begun to learn by observation, by trial and error, and even from other machines—as we do, but much faster. They are likely to master most new occupations before we humans ever have the chance. We face a time when humans will hop from one career to the next, struggling to stay ahead of automation. Saddled by debt and discouraged by a broken social contract, many may succumb to despair unless we find an alternative to endless retraining.

(p.922) Uncertainty about how AI will shape the future of work parallel concerns about how AI may alter what it is like to be human. AI‐enabled applications that are beginning to enter the workplace need the attention of occupational safety and health practitioners, researchers, employers, and workers. When AI‐ enabled devices or systems are considered for introduction into the workplace, thorough preplacement safety and health review of their benefits and risks should be performed. A proactive approach to AI and its implications for the future of work re-quire occupational safety and health professionals to develop strategic foresight to better anticipate and...

(p.414) It is possible that artificial general intelligence—AI that out-performs humans in all ways—will arrive in several decades and the resulting employment disruptions will dwarf the disruptions described in this paper. But between today and 2040, the AI that already exists will disrupt the nation’s occupational structure. The question is whether these disruptions will seriously destabilize the country’s political and social structure. Part of the answer involves the speed of technical disruptions. The evidence in this article is mixed, but on balance there appears to be some time to develop anticipatory policies to assist people who will lose jobs and other...

(p.201) I provide the example of innovation in machine intelligence, which substitutes human skills characteristic of middle-class jobs, making these jobs redundant. As an effect, more people may be forced to find jobs that are less amenable to automation, but which, paradoxically, may turn out to be less desirable than the jobs most humans could find in the past. This undermines one of the arguments supporting machine use, namely that machines substitute men in hard physical tasks, then release man from the burden of heavy workloads, hazardous work environments, boring and repetitive tasks, and close supervision by other humans. I will...

(p.153) In the end, the trajectory of the digital economy may mean that an unprecedented number of citizens could lose their jobs to robots and software that can work for cheaper and for longer hours than any human. If such a transformation occurs, it will most likely be gradual (Susskind & Susskind, 2016), but even so, anticipating the individual and social outcomes is a matter worth pursuing. Hopefully, by recognizing the potential dangers of unemployment and by assessing both the trajectories and discourses associated with newer technologies, social scientists will be more equipped to discuss the implications of robotics, AI, and...

(p.10) Artificial intelligence has the power to change the nature of work for many people, but the pace of adoption and the extent of the disruption are still the subject of debate. A rapid adoption of autonomous self-driving vehicles, for instance, could dramatically displace millions of workers, but a more gradual and partial adoption, especially in a growing economy, will have far less impact on drivers.

(p.5) Applying ML concept in computational pathology, which is defined as an approach to diagnosis that incorporates multiple sources of data (e. g. pathology, radiology, clinical, molecular and laboratory operations), presents clinically actionable knowledge to doctors, investigators and patients [31]. The work culture will play a significant role to be more inclusive to allow different experts from all relent areas to work cohesively together including, IT application specialists, medical and scientific experts, data scientists, hardware and software engineers, biostatisticians, and algorithm designers.

Some 69 per cent of full-time British male employees and 65 per cent of British full-time female employees reported that they would continue in employment if this were no longer financially necessary. Conversely, only 15 and 18 per cent of these two groups indicated that they would want to stop work and never start again in the absence of financial need (p.309)