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The impact of artificial intelligence in medicine ----------------------------------------------------------------------------------------------------- "AI is poised to play an increasingly prominent role in medicine and healthcare because of advances in computing power learning algorithms and the availability of large datasets (big data) sourced from medical records and wearable health monitors. Learning algorithms are becoming more precise and accurate as they interact with training data allowing newer insights into diagnostics treatment options and patient outcomes (Bresnick 2018b). AI is well-suited to handle repetitive work processes managing large amounts of data and can provide another layer of decision support to mitigate errors. The impact of artificial intelligence in medicine on the fut The impact of artificial intelligence in medicine on the future role of the physician AI is poised to play an increasingly prominent role in medicine and healthcare because of advances in computing power, learning algorithms, and the availability of large datasets (big data) sourced from medical records and wearable health monitors. Learning algorithms are becoming more precise and accurate as they interact with training data, allowing newer insights into diagnostics, treatment options, and patient outcomes (Bresnick, 2018b). AI is well-suited to handle repetitive work processes, managing large amounts of data, and can provide another layer of decision support to mitigate errors. articles easier to read in PMC Your unique SEO Text answers the following questions for you: What is the new AI method of machine learning? Artificial Intelligence What is the purpose of the research? to better understand this technology and how it is transforming medicine What is the term for general AI? Artificial Intelligence What type of AI is used for speech recognition? What is the name of AI techniques known as? medicine learning ----------------------------------------------------------------------------------------------------- What is AI poised to play in medicine and healthcare? =========================================== advancements in computing power, learning algorithms, and the availability of large datasets (big data =========================================== What is the health care market for AI >$6.6 billion ----------------------------------------------------------------------------------------------------- The impact of artificial intelligence in medicine on the future role of the physician Abhimanyu S. Ahuja Additional article information Associated Data Data Availability Statement The following information was supplied regarding data availability: No raw data or code was generated from this study; this article is a literature review. Abstract The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician–patient relationship. Keywords: Artificial intelligence, Machine learning, Impact, Medicine, Physician, Deep learning, Radiology, Pathology, Opthalmology, Oncology, Cardiology Introduction ----------------------------------------------------------------------------------------------------- The term “Artificial Intelligence” (AI) was first coined by John McCarthy for a conference on the subject held at Dartmouth in 1956 as “the science and engineering of making intelligent machines” ----------------------------------------------------------------------------------------------------- (Society for the Study of Artificial Intelligence and Simulation of Behavior, 2018). ----------------------------------------------------------------------------------------------------- After a period of reduced funding and interest in AI research, also referred to as the AI winter (Crevier, 1993), optimism in AI has generally increased since the low point in the early 1990s. ----------------------------------------------------------------------------------------------------- Artificial intelligence (AI) is an important field of computer science that seeks to create complex machines with characteristics of human intelligence. We can think of this concept as “General AI,” which has machines that can think and reason and even see and hear like humans (Copeland, 2016). This concept which can be seen in movies like Star Wars (think C-3PO, a droid programmed for etiquette and protocol) is not something we can achieve at this time. However, what is achievable at this time falls under the concept of “Narrow AI” where technologies exist to perform specific tasks as well as, or better than, humans can (Copeland, 2016).

Examples of such narrow AI include speech recognition, facial recognition, etc. These technologies exhibit certain facets of human intelligence. Such intelligence is derived from AI techniques known as machine learning and deep learning which have improved performance in areas such as image classification, text analysis, speech and facial recognition with a range of promising applications such as autonomous vehicles, natural language processing, and in medicine. AI is poised to play an increasingly prominent role in medicine and healthcare because of advances in computing power, learning algorithms, and the availability of large datasets (big data) sourced from medical records and wearable health monitors. ----------------------------------------------------------------------------------------------------- The health care market for AI is increasing at a rate of 40% and is expected to reach $6.6 billion by 2021 (Frost & Sullivan, 2016). Computing power is increasing rapidly due, in part, to the wide availability of Graphics Processor Units that make parallel processing even faster and the availability of seemingly infinite compute resources on demand in the cloud. Big data is also well supported by practically endless storage in the cloud. Learning algorithms are becoming more precise and accurate as they interact with training data, allowing newer insights into diagnostics, treatment options, and patient outcomes (Bresnick, 2018b).

The flood of health care data is helping push the development of new AI applications that promise to improve the efficiency and effectiveness of patient care. Healthcare related big data is available from sources such as Electronic Medical Records (EMR) and wearable health trackers, which can be analyzed in new ways.

The rise of AI in the era of big data can assist physicians in improving the quality of patient care and provide radiologists with tools for improving the accuracy and efficiency of diagnosis and treatment. AI is well-suited to handle repetitive work processes, managing large amounts of data, and can provide another layer of decision support to mitigate errors. The research firm Frost & Sullivan estimates that AI has the potential to improve patient outcomes by 30% to 40% while reducing treatment costs by up to 50% (Hsieh, 2017a).

Experts predict AI to have a significant impact in diverse areas of health care such as chronic disease management and clinical decision making (Bresnick, 2016). While still in the early stages of adoption, AI algorithms are showing promise in specializations such radiology, pathology, ophthalmology, and cardiology (Hsieh, 2017a). This progress raises a thought-provoking question. Will AI at some point displace certain physicians such as radiologists or will it help make them more effective or will it be a bit of both? ----------------------------------------------------------------------------------------------------- This research looks at the potential uses of AI in medicine and considers the possibility of AI replacing certain physicians or at least supplementing the role of physicians. ----------------------------------------------------------------------------------------------------- The rest of this paper is organized as follows. A survey of the literature is provided in ‘Literature Survey’. -----------------------------------------------------------------------------------------------------

AI in Modern Medicine’ ‘Artificial Intelligence vs. Machine Learning vs. Deep Learning’ provides a discussion on AI, Machine Leaning, and Deep Learning and how they relate to each other. ‘Promise of AI in Modern Medicine’ discusses the promise of AI in medicine across various specialties such as radiology, pathology, cardiology, and ophthalmology. ‘Assessing the Impact of AI on Physicians’ assesses the impact of AI on physicians. Conclusions are provided in ‘Conclusions’. ----------------------------------------------------------------------------------------------------- Google Scholar Survey methodology Scholarly articles that were reviewed in this paper were searched in journal databases such as PubMed and subject-specific professional websites including Google Scholar. ----------------------------------------------------------------------------------------------------- The search terms that were used when searching for articles included artificial intelligence, medicine, machine learning, deep learning, radiology, pathology, cardiology, oncology, and ophthalmology. Inclusion criteria for selected articles required that articles be directly related to the topic on artificial intelligence and medicine. ----------------------------------------------------------------------------------------------------- Both qualitative and quantitative articles were reviewed. ----------------------------------------------------------------------------------------------------- Qualitative articles provide insights into the problem by helping understand reasons, opinions, and motivations. ----------------------------------------------------------------------------------------------------- Quantitative articles on the other hand use measurable data to formulate facts and discover patterns in research. Artificial Intelligence vs. Machine Learning vs. Deep Learning AI is a subfield of Computer Science that deals with the design and development of intelligent machines (Society for the Study of Artificial Intelligence and Simulation of Behavior, 2018). ----------------------------------------------------------------------------------------------------- It deals with the use of computers to mimic the cognitive functions of humans and carry out tasks based on algorithms in an intelligent manner such as learning from experience, adjusting to new inputs, and performing human-like tasks (Venkatesan, 2018). ----------------------------------------------------------------------------------------------------- AI has been used in medicine before. Expert systems, an earlier form of AI, tried to encode the decisions of physicians into a set of rules that computers could execute (Schmidt-Erfurth et al., 2018). It was, however, not possible to encode a set of rules for all clinical situations given the complexity of medicine and variations in diseases. For these reasons expert systems were replaced in the 1990s by Machine Learning (ML) where the “rules would be learned by algorithms directly from a set of examples instead of being encoded by hand” (Schmidt-Erfurth et al., 2018). ----------------------------------------------------------------------------------------------------- ML is a subset of AI and emphasizes the learning aspect of intelligence. It has to do with developing computer programs that can learn and improve from experience without being explicitly programmed. This stands in contrast to traditional computer programs which require specific instructions that detail every step the program must take. It would not be incorrect to say that today when we refer to AI we have almost exclusively ML and its derivatives in mind. ----------------------------------------------------------------------------------------------------- ML research continues to reference the brain as an inspirational source and attempts to “imitate the neural structure of the nervous system by creating artificial neural networks (ANNs) which are networks of units called artificial neurons organized into layers” (Pearson, 2017). -----------------------------------------------------------------------------------------------------

The system learns to detect patterns from data Provided to the system from the data (This started a new subfield of ML called deep learning which employs algorithms) ----------------------------------------------------------------------------------------------------- Provided to the system in a training session. The ML approach requires that a set of features be directly measured from the data (e.g., size of breast lesions in CT scans). A deep neural network (DNN) is an ANN consisting of more layers (usually more than five) that allows for improved predictions from data. A significant advantage of DNNs is that their performance continuously improves as the size of the training dataset increases (Schmidt-Erfurth et al., 2018). ----------------------------------------------------------------------------------------------------- This started a new subfield of ML called deep learning which employs algorithms such as DNN and convolutional neural networks (CNNs). The idea is “that a neural network, instead of just acting as a classifier as in the case of classic ML, can also function as the feature extractor as well” (Ahuja & Halperin, 2019). This allows for end-to-end training because a DNN learns to recognize an output category from the input signal directly (Schmidt-Erfurth et al., 2018). CNNs are mostly applied to image segmentation and classification.

----------------------------------------------------------------------------------------------------- Graphical Processing Units enable significant acceleration in the case of CNNs (about 40 times) compared to CPU processing alone (Schmidt-Erfurth et al., 2018). Assessing the Impact of AI on Physicians In terms of predictive analytics and image recognition,

----------------------------------------------------------------------------------------------------- AI may soon become more effective than physicians, who cannot handle millions of images in any reasonable timeframe.

----------------------------------------------------------------------------------------------------- This has led to some concern that AI-based systems will replace physicians, especially radiologists. ------------------------------------------------------------------------------------------------------------ One narrative suggests that AI will interpret even the most complex clinical images as accurately as today’s most experienced radiologists and eventually replace radiologists (Pearson, 2017). --------------------------------------------------------------------------------------------------------------------- The contrarian view is that this will not happen; rather AI will augment radiologists but not replace them (Pearson, 2017).

----------------------------------------------------------------------------------------------------- There is yet another middle of the road view that at some point in the future AI will indeed replace radiologists, but it is not worth worrying about as that will happen in the distant future (Pearson, 2017). -------------------------------------------------------------------------------------------------------- While it is difficult to project the impact of AI on radiologists in the future, Recht et al. provide a nuanced opinion in (Recht & Bryan, 2017a)

----------------------------------------------------------------------------------------------------- that “AI will become a routine part of radiologists’ daily lives, making their work more efficient, accurate, and valuable”. AI-based machines will perform routine reading tasks such as quantification and segmentation and help free up radiologists to “perform more value-added tasks, such as integrating patients’ clinical and imaging information, having more professional interactions, becoming more visible to patients and playing a vital role in integrated clinical teams to improve patient care.” (Recht & Bryan, 2017a).

----------------------------------------------------------------------------------------------------- Looking beyond radiologists and considering physicians generally, it seems a reasonable prediction that AI are likely to augment physicians rather than replace them. There are several limitations of AI that lead one to this conclusion.

----------------------------------------------------------------------------------------------------- AI cannot yet replace doctors at the bedside, given its limitations. Krittanawong (2018) points out that AI “cannot engage in high-level conversation or interaction with patients to gain their trust, reassure them, or express empathy, all important parts of the doctor–patient relationship.” ---------------------------------------------------------------------------------------------------------------------------------- Though one might speculate that this may change at some point in the future with AI being able to make a medical conversation. For example, Google demonstrated a phone call of its AI assistant in their Google I/O 2018 conference.

----------------------------------------------------------------------------------------------------- Physicians are still needed for traditional physical exams, especially in areas such as neurology, which require high-level patient-physician interaction and critical thinking (Krittanawong, 2018). -

---------------------------------------------------------------------------------------------------- Finally, even though AI may reach the point where it can conduct real-time CT scans or other physical scans, physicians will still be needed for interpretation in ambiguous and challenging cases.

----------------------------------------------------------------------------------------------------- AI-based systems are based on precedence in the case of ML and DL, but such algorithms can underperform in novel or unusual cases of drug side effects or treatment resistance where there is no prior example to build on.

----------------------------------------------------------------------------------------------------- For these reasons it can be concluded that AI-based systems will support the skills of physicians and are unlikely to replace the traditional physician–patient relationship. -

----------------------------------------------------------------------------------------------------- Conclusions The avalanche of medical data in the form of clinical, genomic, and imaging data is only likely to accelerate as precision and personalize medicine matures.

----------------------------------------------------------------------------------------------------- Consequently, for the foreseeable future medicine in the future medicine is likely to be even more data-dependent with the synergy between medicine and AI technology becoming more pronounced. In recognition of this important trend in modern medicine, medical schools are strengthening their emerging technology curricula.

----------------------------------------------------------------------------------------------------- New courses are being offered by medical schools in technology infrastructure, ML, DL, and data management alongside their biology classes (Dyche, 2018). -

--------------------------------------------------------------------------------------------------- AI will support the future needs of medicine by analyzing the vast amounts and various forms of data that patients and healthcare institutions record in every moment. AI is likely to support and augment physicians by taking away the routine parts of a physician’s work hopefully enabling the physician to spend more precious time with their patients, improving the human touch.

----------------------------------------------------------------------------------------------------- While AI is unlikely to replace physicians in the foreseeable future, it is incumbent on medical professionals to learn both the fundamentals of AI technology as well as how AI-based solutions can help them at work in providing better outcomes to their patients.

----------------------------------------------------------------------------------------------------- Or, it might come to pass that physicians who use AI might replace physicians who are unable to do so. -

----------------------------------------------------------------------------------------------------- Author Contributions Abhimanyu S Ahuja conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft. Data Availability The following information was supplied regarding data availability: No raw data or code was generated from this study; this article is a literature review The impact of artificial intelligence in medicine on the future role of the physician Abhimanyu Ahuja Artificial intelligence (AI) and robotics have the potential to improve the quality and efficiency of medical care.

----------------------------------------------------------------------------------------------------- Artificial intelligence (AI) is a branch of computer science that seeks to create machines with characteristics of human intelligence.

----------------------------------------------------------------------------------------------------- Artificial intelligence (AI) is a branch of computer science that deals with a narrower range of tasks than human intelligence. Big data has become an important

----------------------------------------------------------------------------------------------------- source of information for researchers and physicians alike as they seek to improve patient care.

----------------------------------------------------------------------------------------------------- Artificial intelligence (AI) has the potential to transform the way we care for our patients.

----------------------------------------------------------------------------------------------------- The first section of this paper is devoted to the topic of artificial intelligence (AI). ----------------------------------------------------------------------------------------------------- Social Media HashTags: Funding Statement The author received no funding for this work. Additional Information and Declarations Competing Interests The author declares there are no competing interests. "What is the new AI method of machine learning?", "Artificial Intelligence" "What is the purpose of the research?", "to better understand this technology and how it is transforming medicine" "What is the term for general AI?", "Artificial Intelligence" "What type of AI is used for speech recognition?", "What is the name of AI techniques known as?", " "machine learning" "What is AI poised to play in medicine and healthcare?", ======================================= "advancements in computing power learning algorithms and the availability of large datasets (big data" ======================================= "What is the health care market for AI?", "$6.6 billion"