Question

Write a paper (3,000-6,000 words) on the topic " Revolutionizing Healthcare and Medicine through Artificial Intelligence"
Give an abstract, introduction and definitions
The Impact of Artificial Intelligence on Healthcare and Medicine
List and Explain the regulations and laws of Artificial Intelligence on Healthcare and Medicine
List and Explain the Different Types of Artificial Intelligence
Give Modern Trends, Advancements, Advantages, Disadvantages, Open Issues and Challenges
Future of Artificial Intelligence, Recommendations and Conclusion

Would be good if you include several pictures and tables or informations in charts, graphs etc.

Answer 1

Introduction

Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. ... The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes.

According to a recent study conducted by Forbes magazine, Americans visit the doctor on an average of four times a year. While a long wait to schedule an appointment is one of the reasons that discourages patients from visiting a hospital, the cost of healthcare also acts as a major deterrent to people to seek the medical help they need.

A good healthcare system not only contributes to the welfare of the citizens, but also boosts the economy by making sure that productive labor is not lost due to down time. With rapid strides in technology, it is not surprising that the healthcare industry collects a lot of data about patients and this has the potential to generate valuable insights for medical practitioners and even when it comes to medical research. Big data and healthcare analytics have changed the face of the industry from a traditional set-up to one that thrives and is driven by technology.

Most hospitals already have several years of data. What they don’t realize is that their data is actually an asset that can be used to generate data models and benefit across clinical, operational, and revenue cycle processes. In addition, the right people, processes and technology, along with both short-term and long-term strategies are critical components of any effort to leverage data. A patient’s data not only helps us understand their medical history, but also helps us predict the complications a patient can face and their possibility of readmission. This in turn translates to better health outcomes for the patients; it could also make a difference in improving reimbursements and regulatory compliance.

Impact of artificial intelligence

Artificial Intelligence has a multitude of impacts on our daily lives, from recommending movies based upon your Netflix viewing habits, to assisting pilots with flying modern jets. One of the most significant applications, however, will be in healthcare.

According to PricewaterhouseCoopers, “chronic diseases and conditions are on the rise worldwide.” When infectious diseases like SARS and Ebola emerged, there was a rapid, global spread. Given the significant increase in global mobility, outbreaks must be dealt with quickly to minimize the number of people who may be infected.

Although there have been significant advances in the control of common communicable diseases, presently, some of the most common infections like tuberculosis, malaria, and HIV still do not have effective vaccines.

On a positive note, the past few years has also seen major progress in the diagnosis, management and prevention of certain cancers like cervix and breast cancer and childhood leukemia.

The World Health Organization reports that by 2020, the prevalence of chronic disease is expected to rise 57%. However, advancements in detecting and diagnosing diseases will help to minimize the cost of treating chronic diseases. Some of these new technologies include genomics, proteomics, cell biology, stem cell and organ therapy, and minimally invasive and robotic surgery.

In the past 10 years, medical advances and breakthroughs have included new technologies including:

1.3D printing to create human body parts, reproduce blood vessels and printing skin cells for rapid wound healing

2.An artificial eye by California-based, Second Sight, that enables patients to attain a level of vision

4.A small, implanted, remote controlled device that sends electrical pulses to help reduce the impact of headaches

5.Graphene, an extremely flexible material 200 times stronger than steel, is now being produced at a lower cost and can be used to develop revolutionary medical devices used in biomedical applications like tissue engineering

6.Eye drops that dissolve cataracts, eliminating the need for surgery

7.An artificial pancreas that measures blood glucose using a sensor and delivers insulin, adjusting the dosage according to readings

8.An implant for opioid dependent patients that automatically administers doses of buprenorphine, a narcotic that can treat pain as well as addiction to narcotic pain relievers

Regulations and laws of Artificial Intelligence on Healthcare and Medicine

Digitisation is advancing steadily and disrupting the sector. One of the most important aspects is the potential of artificial intelligence (AI) for applications in the field of health. Alongside the technical challenges, the digitisation of healthcare raises a series of legal questions. This is often “terra incognita” in terms of the law. Is the software a medical device? Are contracts concluded automatically? And who is accountable if the self-learning app makes a mistake?

AI and robotics in healthcare that use artificial intelligence are developing at a furious pace, in particular early detection and diagnostics applications. At the same time, AI is becoming increasingly sophisticated, enabling it to do what humans do – often more efficiently, faster and at lower cost.

An important area of application is preventative care. AI can be used to help people stay healthy. Apps, like fitness trackers for example, can promote healthier behaviour and help individuals manage a healthy lifestyle on their own. The goal is to give consumers better control of their health and wellbeing.

Today already, AI is indispensable in the field of early detection and diagnostics. It is used in a variety of ways to detect diseases like cancer more accurately, more reliably and sooner. Put simply, it does so by comparing data from a specific patient – also in the form of images – with large quantities of data from other patients. The self-learning systems detect correlations and suggest diagnoses. An example is IBM’s Watson for Health, which assists healthcare organisations in the use of cognitive technologies with a large quantity of health data. Google’s DeepMind Health Technology combines machine learning with system-neuroscience to simulate the human brain using AI and offers diagnostic and decision-making support to those working in healthcare.

The last-mentioned aspect forms an additional important pillar of AI applications in healthcare. Drawing on extensive data sets, so-called decision support software uses predictive analytics to support clinical decisions and measures as well as streamline processes. In addition, pattern recognition helps identify patients with a high risk for certain diseases or who are experiencing a deterioration in their general state of health due to lifestyle, environment, genomics or other factors.

Added to this are further areas of application such as assisting in patient treatments, for example by improving treatment plans or monitoring treatment successes, or using robot technology in surgery.

Liability Issues

At present, a medical professional is responsible in case a deficiency of his/her duty leads to negligence. There have been instances of civil as well as criminal penalties being imposed on medical professionals in the past for negligence. The regulations do not, however, distinguish cases where there is an error in diagnosis malfunction of a technology, or the use of inaccurate or inappropriate data. As a result, presently there is no accountability for the software developer developing the AI solution or the specific program engineer who designed it. It is also unclear on how one determines the level of accountability of the medical professional when he/she provides the wrong treatment or diagnosis due to a glitch in the system or an error in data entry.

For instance, publicly available data suggests that certain AI solutions created for treating cancer patients have had instances of giving unsafe recommendations. Reports have suggested that cancer patients with severe bleeding have been recommended a drug that could cause the bleeding to worsen. Under the current regulations in India, the medical professional can be held accountable for prescribing the relevant drug and may not be able to take a defence that he/she relied on the recommendation of an AI solution.

Data Privacy

The use of AI would entail a constant exchange of information between the patients and the AI service provider. These create massive datasets which are then processed for training, validation and creating algorithms. The lack of adequate data privacy laws in India could result in such data sets being commercially exploited for matters beyond development of AI solutions.

Recognizing the issue, earlier this year, the Ministry of Health and Family Welfare released a draft of the Healthcare Security Act. In addition to the electronic health record standards, this law proposes to provide civil and criminal remedies for breach of data and principles for data collection and use. It also provides for the establishment of the National Digital Health Authority, a regulator which will focus exclusively on enforcing healthcare data protection norms.

IPR Regime

The intellectual property regime in India does not recognize patentability of algorithms, the basis on which an AI solution functions. In fact, the Patents Act expressly exempts algorithms from being “inventions” eligible for patent protection. This regime may result in being averse to incentivizing development of AI solutions.

Also, AI algorithms are created by collating and analyzing human-created works and data-sets. Indian laws grant copyrights to creators of the work with the exclusive right to reproduce their works. It is unclear whether creating copies of these works and datasets (without the consent of the creator) for developing AI solutions could be viewed as copyright infringement by the developer.

Presently, AI in healthcare does face regulatory challenges (some of which have been highlighted above) which can be resolved only by formulating an effective regulatory framework to ensure sufficient oversight on AI. While the technology is fast changing, and AI will play an increasingly important role in healthcare in India, the India laws will also need to be developed and amended constantly to adequately regulate the role of AI in the healthcare sector. India could consider forming a committee entrusted with the task of evaluating the operation of AI-driven solutions and suggesting changes to Indian regulations.

There are four types of artificial intelligence:

1. reactive machines

2.limited memory

3.theory of mind

4. self-awareness.

1. REACTIVE MACHINES

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world.

The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties. The innovation in Deep Blue’s design was not to broaden the range of possible movies the computer considered. Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome. Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.

Similarly, Google’s AlphaGo, which has beaten top human Go experts, can’t evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blue’s, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled.

They can’t interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But it’s bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won’t ever be bored, or interested, or sad.

2. LIMITED MEMORY

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

3. THEORY OF MIND

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

4. SELF-AWARENESS

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligences. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

Why Artificial Intelligence is Revolutionizing Healthcare

Artificial intelligence is making waves in the medical industry, especially in the media. From bionic arms and robots performing detailed surgeries, it is hard to ignore. According to research conducted by HIMSS Media, 63% of research participants believe that “ AI and machine learning are already delivering value in specialty care including radiology, pathology and pharma.” This information is promising as it shows that the majority of people feel as if artificial intelligence and machine learning are making a positive impact in healthcare.

This article focuses on why artificial intelligence is revolutionizing healthcare, real-life use cases of the technology being used and the ethics surrounding implementing this kind of technology into the medical industry.

How AI is Revolutionizing Healthcare

The ways in which artificial intelligence is transforming healthcare goes beyond what you see in the media. We have seen Tilly Lockey, the first teenager in the UK with bionic arms. Open Bionics created these arms, the world’s first medically approved 3D printed bionic arms.

However, there is much more technology being developed than this. These new types of technologies not only can be used to change patients lives, but also change the work lives of doctors and nurses by creating more efficiency and organization.

AI can be used to create personalized treatment plans as well as better organized patient routes. This new technology can also be used to speed up processes such as discovering candidates for clinical drug trials. As well as this, artificial intelligence is being used in predicting diagnoses that require early intervention. This will improve the lives of patients and their families if they can use technology to detect illnesses prior to onset.

What lies ahead

Artificial intelligence has challenges to overcome before it gains full traction in many fields. And healthcare is no exception, especially where privacy is concerned. Last year, DeepMind ran afoul of UK authorities and privacy groups over its data sharing deal with the NHS. Medical information is sensitive, and institutions that handle it need to mind their collection, storage and sharing policies.

Some firms are considering blockchain, the distributed ledger that supports Bitcoin and Ethereum, as a solution. Morpheo, for instance, uses blockchain to ensure transparency and privacy of patient data on its platform.

Another open-ended question is how artificial intelligence will affect jobs in the healthcare sector. At the current stage, it’s a given that caring for humans is the job of humans. For the moment, no algorithm is able to emulate both the social and professional functions of a doctor or nurse. In fact, robots are not replacing but enhancing human efforts to improve the overall quality and availability of health services.

Will the suggestion-making role of AI-based healthcare tools someday turn into decision-making? Only time can tell. But recent developments in artificial intelligence show that machines still have quite a few surprises up their sleeves.

Examples of AI in Healthcare

There are many new companies driving the development of artificial intelligence in healthcare. I am going to discuss three companies at the forefront of this phenomenon, these are Nuance, AIVaid, and Prognos.

Nuance is a multinational computer software technology corporation based in the United States. It is a company that uses artificial intelligence in many areas, such as communication and healthcare. Nuance uses their artificially intelligent technology in many sections, such as; documentation capture solutions, clinical and revenue integrity solutions, quality management solutions, optimization and consulting services, diagnostic solutions, and ambulatory solutions. Through using artificial intelligence to tackle all of these areas, it allows for transformations in both the lives of patients and staff. Their AI-powered solutions allow for 45% less time spent on documentation, this time saved can be used to care for and treat patients.

AIVaid is a free application that uses machine learning and an inference engine( applies logical rules to the knowledge base to gather new information) to allow users, both patients and doctors, to ask medical questions to reveal possible conditions. The information used to develop this application is collected by the company Infermedica. This company collects, analyzes and organizes data and feeds it into the technology. This in turn allows the technology to learn and progress further. The last piece of their technology is the API which enables the user interface.

Now that we have covered the technology, let’s discuss the benefits of using a tool like AIVaid. The first advantage would of course be cost as it is free. It saves time as you don’t have to travel, wait or attend meetings. The app is, as expected, 24/7, meaning you don’t have to be restricted by times of business. As well as this you also get instant, accurate reports. Another element of this application that is beneficial is trust. As it is a private and secure app, people can trust the app with their private data.

Prognos is an artificial intelligence software company that use artificial intelligence to successfully diagnose patients in order to accelerate the treatment process. Prognos’ technology is as advanced as it is because it has access to more than 20 billion medical records. In fact, the Prognos Registry is the largest source of clinical diagnostics information covering 50 disease areas, and 200 million patients.

To conclude, artificial intelligence will be used in the future to make the lives of both physicians and patients easier.