AI-based medical technologies, including wearables, telemedicine, LLMs, and digital care twins, significantly impact healthcare. Ensuring AI results are accurate and interpretable is crucial, especially for clinicians. This paper reviews processes and challenges of interpretable ML (IML) and explainable AI (XAI) in healthcare. Objectives include reviewing XAI processes, methods, applications, and challenges, with a focus on quality control. The IML process is classified into data pre-processing interpretability, interpretable modeling, and post-processing interpretability. The paper aims to establish the importance of robust interpretability in healthcare through experimental results, providing insights for creating communicable clinician-AI tools. Research questions, eligibility criteria, and goals were identified following PRISMA and PICO methods. PubMed, Scopus, and Web of Science were systematically searched using specific strings. The survey introduces a step-by-step roadmap for implementing XAI in clinical applications, addressing existing gaps and acknowledging XAI model limitations.

Google's focus on AI stems from the conviction that this transformational technology will benefit society through its capacity to assist, complement, and empower people in almost every field and sector. In no area is the magnitude of this opportunity greater than in the spheres of healthcare and medicine. Commensurate with our mission to demonstrate these societal benefits, Google Research's programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022. Our Health research publications have had broad impact, spanning the fields of biomarkers, consumer sensors, dermatology, endoscopy, epidemiology, medicine, genomics, oncology, ophthalmology, pathology, public & environmental health, and radiology. In each section, we emphasize the importance of a measured and collaborative approach to innovation in health. Unlike the "launch and iterate" approach typical in consumer product development, applying ML to health requires thoughtful assessment, ecosystem awareness, and rigorous testing.

We are seeing lots of developments involving the application of virtual reality (VR), extended reality, and augmented reality in healthcare and medicine. VR healthcare companies are applying these technologies to create virtual environments where patients and caregivers can participate in highly realistic and interactive experiences designed to simulate various healthcare scenarios and needs. Key application areas include education/training, treatment, and physical therapy/rehabilitation. This Advisor series examines the use of VR in healthcare and medicine. Part I covered how companies are using VR technologies to develop applications for healthcare educational and training purposes.

The field of healthcare and medicine and specially the digital healthcare will get a great boost with the advancement and wide scale use of Quantum Computing and Artificial Intelligence. In fact, these technologies have already started transforming different areas of Healthcare and Medicine in a big way. Before even quantum computers were there, scientists at the University of Virginia School of Medicine long back anticipated the potential of quantum computers to better understand genetics and different diseases. The envision been realized and a team at the University's center for quantum computing & biology is now harnessing the power of quantum computing to gain better insights into genetic diseases with the help of machine learning algorithms. Researchers expecting that these efforts will benefit not only health care and medicine but also many other streams of science and technology.

The field of healthcare and medicine and specially the digital healthcare will get a great boost with the advancement and wide scale use of Quantum Computing and Artificial Intelligence. In fact, these technologies have already started transforming different areas of Healthcare and Medicine in a big way. Before even quantum computers were there, scientists at the University of Virginia School of Medicine long back anticipated the potential of quantum computers to better understand genetics and different diseases. The envision been realized and a team at the University's center for quantum computing & biology is now harnessing the power of quantum computing to gain better insights into genetic diseases with the help of machine learning algorithms. Researchers expecting that these efforts will benefit not only health care and medicine but also many other streams of science and technology.

Will artificial intelligence (AI) replace doctors and other clinicians? As both a doctor and a data scientist passionate about using AI, machine learning, and data analytics to improve healthcare and patient outcomes, I have conflicting thoughts and feelings on this topic. In fact, I have three very different views. Viewpoints 1 and 2 are those of an optimist or a pessimist, depending on how one looks at the issue. Viewpoint 3 is more pragmatic and nuanced, warranting further explanation.

AI will have a huge impact on healthcare. It is currently moving out of the laboratory and into real-world applications for healthcare and medicine. Many startups are using modern data and AI technologies to tackle problems related to workflow optimization and automation, demand forecasting, treatment and care, diagnostics, drug discovery, personalized medicine, and many other areas. Some of these companies are beginning to speak publicly about their AI initiatives; our upcoming Artificial Intelligence conferences in San Jose and London have a strong roster of speakers who will describe applications of AI in Health and Medicine. AI's transition to the real world can be challenging. This article highlights new projects and tools that can alleviate and address these challenges.

No industry counts more than healthcare and medicine. And I don't think I should explain'why'. So whatever advancements we may have done in any industry till today with Artificial Intelligence or with any other cutting edge technologies for that matter, our healthcare and medicine should come top in that list and there should not be any disagreement on this. Let's do a reality check and see how far Artificial Intelligence has already been leveraged in our healthcare and medicine. When you go to your doctor today, your doctor can't guarantee that a particular treatment selected for you is going to work for sure.

Ok, this is not about cheating regulatory authorities, but rather making best use of technologies like sensors and artificial intelligence to the prevention, early detection and optimal repair of damage. So, imagine you have this brand new car and you go for a regular (annual) checkup, as indicated by your on board computer. You arrive and they tell you they will check oil, water, air pressure and inspect for visibly hints to corrosion only, since this hightech stuff is too cost intensive. I would want to see your face! Well, now you go for your personal annual health checkup; some blood, urine will be analyzed for app.