1. Introduction

Idoc provides a comprehensive database of academic knowledge integrating medicinal resources, plans of academic conferences, academic news and events, and serves as an information platform for academic exchanges and collaborations for medical professionals worldwide. Nowadays, AI (Artificial Intelligence) is popular for attending to patients or examining images. It is expected to have a significant potential for the future of AI in the healthcare domain. Note that they can support, but not replace, healthcare professionals. This essay introduces AI technologies in the medical field.

An AI is a computer system that can be trained to do a task that is too complex, time-consuming, or difficult for a human to perform quickly and relatively accurately. They can learn, reason, and act for goals on the basis of data measured or sensed, relying on insights (inferences, predictions) to deliver or trigger actions. An AI can be thought of as having five main capabilities: learning, reasoning, perceiving (including via sensing and vision), transforming in accordance with goal states (including via the use of robotics or software), and assisting (having the ability to provide augmentation or translation of human capability). An AI that combines these capabilities is referred to as a “comprehensive AI”, which can process large and heterogeneous data efficiently. The comprehensive AI system is not only sufficient for the construction of highly complex decision trees, but also has causality and background knowledge, making not only prediction, but also when and the reason for generating the response can be output as well.

1.1. Overview of AI in Healthcare

Healthcare is advancing faster than ever, introducing new technologies and treatment methods almost monthly. One of the most significant new developments coming out of the field is that of Artificial Intelligence (AI). Already, AI has found widespread use across the industry, in machines as well as in data, with applications used in hospitals, combat zones, and home healthcare settings. AI is proving particularly poignant in the fight against the COVID-19 pandemic in areas such as vaccine development, Covid tracking maps, and other machine-learning predictive models. This paper is derived from a seminar by the Center for Global Health Science and Security that focused on how AI is and will continue to revolutionize the practice and delivery of healthcare. This paper reviews the rapidly evolving field of AI, and the hurdles and roadblocks such disruption creates.

AI has the potential to assess and diagnose patient care with greater accuracy, truly customize care for each individual patient, and help healthcare workers make the best decisions in circumstances where gaps in knowledge exist. AI may, in the future, be capable of performing one or more, or even all, components of critical tasks in the healthcare setting, including (but not limited to) failure mode and effects analysis, medical error reduction, determining the most cost-effective clinical pathways, intake patient itinerary, early disease diagnosis and intervention, therapeutic regimens, family-patient design, discharge and post-acute follow-up care, breach penetration testing, improving the patient experience, patient conversation recording, and might even be capable of performing surgery. The potential for AI in healthcare is vast, and can lead to dramatic healthcare coverage and patient outcome improvements, as well as considerably lower operational costs. Or is that the mother of all assumptions? Despite the promise of AI in healthcare and public health roles, the industry is currently a patchwork of stand-alone solutions that lack the levels of interoperability required for true seamless, integrated systems that make any meaningful impact on healthcare.

2. The Role of AI in Diagnosis and Treatment

AI has the ability to gather and interpret data, which allows it to identify patterns in diagnostic procedures that current medical research techniques are unable to see. It can even better diagnose conditions than professionals. The predictions of the AI model have been confirmed with the same predictions made by medical professionals. Improved analysis from the AI’s intervention shows the capacity of this technique to enhance clinical decisions with accuracy and effectiveness, which is invaluable to the healthcare industry. Using AI, professionals will have access to all the details straight away. This information will allow them to diagnose quicker and focus on faster treatment.

AI healthcare algorithms can scan at speeds that are impossible for the human eye to capture. This makes the analysis more accurate and efficient. AI systems can be a life-changing technology that improves healthcare quality. It can simulate the thought process of healthcare experts for decision-making and use automation to create more accurate, efficient, and consistent patterns for patient utilization. As a result, the healthcare sector can contribute to improvements and innovations aimed at increasing the well-being of individuals. Technology has the potential to increase the quantity and quality of information on which treatment decisions are based. This can lead to better outcomes and improve the care of public health. Technology also allows body fat to be accurately and reliably assessed. In clinical trials, treatment effect measurement can be an excellent prognostic indicator. Public health benefits for these citizens and the general public are predicted.

2.1. AI Applications in Medical Imaging

Artificial intelligence (AI) has taken healthcare by storm, presenting a wide variety of uses that span health monitoring, predicting patient deterioration, triage, diagnostics, and therapeutics. But why are AI applications in medical imaging referred to so much in the context of the future of medicine? Images are one of the most important sources of information in the medical diagnosis as they allow direct visualization of the anatomy and physiological function of the human body. Advanced medical imaging such as MRI, CT, or PET offers excellent soft-tissue contrast, high spatial resolution, and is capable of molecular and functional imaging for improved diagnostics, prognostics, and predictive modeling. There are often signs of abnormalities on the plethora of images obtained per scan. CT images are widely used in the context of COVID-19 to identify pneumonia as shown in the bottom figure. AI algorithms, which can assist the radiologist in identifying anomalies, either general widespread patterns or particularities, would speed up the diagnosis process.

With the ever-increasing volumes of imaging data output per scan already overwhelming or at the limit of what radiologists can interpret in the constrained reporting time frames, AI can transform the interpretation of the complex medical images and enable novel imaging biomarkers for early and differential diagnosis, personalized treatment design, support the delivery of therapy in real-time, and predict the outcome of intervention non-invasively. The use of AI for medical imaging is thus revolutionizing the early diagnosis and screening of diseases, therapeutics, training, and the development of digital twins. In many cases, AI exceeds human performance in tasks such as the interpretation of ECGs, which has led to close collaboration with humans in the automated diagnosis of diseases, personalized medicine, and the development of advanced clinical decision support systems. Given the plethora of interest in this area, a specific issue of the journal ‘Artificial Intelligence in Medicine’ is entirely dedicated to AI-enhanced medical imaging.

3. Enhancing Patient Care with AI

Another vital area where AI is revolutionizing healthcare is in the personalization of patient treatment and care. AI systems can analyze medical scans and detect anomalies more accurately than humans. They can help design drugs and treatment plans, and consider a patient’s own unique factors from their genetic and molecular makeup, through their medical history and lifestyle. If done correctly, this approach is known as personalized medicine – tailoring treatment to suit the individual. One major use case of this is in designing immunotherapies for treating cancer, where they have been shown to be much more effective when treatments are customized to the individual.

However, developing drugs in this way is resource-intensive and expensive. AI-driven approaches can potentially provide efficiencies to increase the pace of development and reduce the cost of bringing innovative precision therapies to patients. On a population level, health data matching the breadth and quality of the data held in EHR is simply not available to government departments, local authorities, and clinical commissioners. As a result, the potential benefits of AI are limited to small subpopulations for whom the relevant data has been recorded – which biases healthcare provision and may exacerbate existing health inequalities. Without this information, providing truly personalized healthcare is difficult. We believe AI has a critical role in predicting, preventing, and precisely treating illness, as well as being part of the healthcare workforce and improving patient safety.

3.1. Personalized Medicine

Personalized medicine is also known as precision medicine or individualized medicine. It’s an approach to patient care that moves away from one-size-fits-all treatment plans and embraces tailored medical interventions that consider factors such as genes, environment, and lifestyle. It’s this concept of personalization that reduces the risk of treatment failure by taking into account clinical or genetic determinants. While still being a relatively emerging market, the implications are vast. If AI can help integrate molecular and genetic data with patient therapies, the implications could contribute to a reduction in health disparity, improvements in evaluation of new products in development, more precise radiation therapy, and help healthcare providers learn more about a patient and provide tailored treatments. It also makes sense that integrating a patient’s molecular and genetic data into their treatment plan leads to better patient outcomes. Especially for complex diseases such as cancer, the interplay of an individual’s genetic background and environmental contributors could expand the success of applying personalized treatment.

Some precision medicine initiatives are already embracing AI and machine learning. For example, Strata Oncology has assembled what it calls the world’s largest panomic data set (based on genomic, transcriptomic, and proteomic data sets), which has been used to help physicians match 6,500 patients with new cancer therapies. Intrepid, a precision medicine company that was formed by regenerative-medicine startup Biobud, uses an AI platform called Automated Regenerative Intelligence to identify the best treatments for a patient in need of regenerative-medicine areas such as orthopedics, rheumatology, and neurology. In 2020, the company announced that it would extend the AR Jeweler platform to oncology to fast-track the identification of molecules for cancer treatment. One of the company’s results showed that, while Pangenics was indicated for breast cancer, the platform predicted its action on lung cancer. In addition, AI is also essential for paving the way in expanding the understanding of different types of cancer, developing new treatment modalities and selecting potential patients, designing personalized vaccines, predicting and optimizing drug responses in combination with genetic profiling, and predicting efficacy via MRI data.

4. Challenges and Ethical Considerations

One of the biggest obstacles to AI’s widespread use in healthcare is the enormous amount of data needed – a problem that maintenance of data privacy exacerbates. In a world where big names like Facebook and Google are capitalizing on user-generated information, the demand for privacy certainly seems advisable. Yet, if having personal data extent is a basic condition for utilizing AI in the healthcare industry, where does that leave AI? Exclusively for the favor of those who have access to such data? The reports are a bit ambiguous. A publication of the Royal Society of British researchers reveals studies that show that it is indeed possible to use healthcare data without compromising health data. However, where medical information is valued, and where it is not, the stream, formed upon reflection, came from patients themselves, who gave the data legal access. The scope of the data, rather than the precise details of the individuals’ identities, is potentially about revealing more. Complicating all of this even further is that when one patient fails, it potentially causes problems for others – for instance, if a radiology model gets flummuxed by a patient’s unusual skeleton, it could make mistakes with more ordinary sets of X-rays. One system may grind to a halt if it is too expensive to re-educate the AI. Another COVID-19-era starring ethical issue is patient consent. Is getting consent all we need to get for using, according to a Medical Journal today, to use data to create artificial intelligence? What about the surrogate consent for utilizing records for those who are technically unable? What about the retroactiveing of the consent? According to these, instead of simply improving their cost-efficiency, we must reassess the informant-identified basis of our current healthcare system as well as our medical history. Each technological innovation, such as AI, is able to invade essential forces of social inequality on such levels. The point raised raises legitimate concerns about AI reproducing the biases of medicine – an issue that was further highlighted in the training of facial recognition systems by IBM on images of underrepresented people in the US (which was also conducted wrong, of course). “Medical data advantageously simplified economic privilege, compared to the Biociti data set, they built their training model for AI with. Ian Watson carried on the label at the Los Angeles Times to explain that this may not particularly benefit themselves if AI were to replicate these biases. Ian Watson carried on the label at the Los Angeles Times to indicate that this may not profit himself considerably if AI were to reproduce these biases.

4.1. Data Privacy and Security

The advancements of AI-driven healthcare technologies are plotted based on the amount of digital records (including patient-sensitive data) that are processed or upon which they were trained. This mass of data not only forms systemic vulnerabilities, but the adoption of more advanced analytics in healthcare, AI or other, would exacerbate these vulnerabilities. Some of the potential entry points for attacks or unauthorized data access include mechanisms for storing, sending and uploading data, the vulnerability of internet-based medical devices, and the ‘human factor’ – accidental or deliberate users’ errors. This vast collection of data points processed, stored or moved between different parts of a large AI system introduces a commensurately large potential for data theft or misuse.

Each of the various poignant scenarios would rank the data-related vulnerabilities as ones inherent to any AI system engaged in processing patient data, and may have dramatic impacts on individual privacy for a great number of actors. Because such scenarios would have an accumulative impact due to the size of the affected population or the extent of the relevant patient data, redundancy, exclusion, dependency, or limited communication scenarios are conducive to breaches and distributed threats. As a result of these considerations, it emerges that integrating AI in healthcare can greatly impact patients’ privacy, the confidentiality of their medical information, as well as the security of a tremendous volume of data. Where the processing of personal data is concerned, the processing entity under GDPR bears responsibility for formally documenting how it manages such data. In AI systems, depending on how the latter are deployed, the data ‘controller’ could be any of the aforementioned institutions (e.g. healthcare provider, financing regulator) and the technology developer. AI systems integrated in healthcare management or decision-making should provide full transparency on the storage and processing of the data. On a technical level, one can put in place methods for integrating privacy safeguards into machine learning pipelines, or develop AI methods that provably protect patient data as they are trained and operationalized. Any advancements in those areas would be of interest to healthcare. In other terms, AI for healthcare technologies that will be made for operational use in the foreseeable future should not only aim for maximal functionality and fit-to-purpose in aiding the infringement of individual rights. A noteworthy breakthrough would be the development of tools or methods that can ensure, at the level of an AI model, the identification and enforcement of obligations of data minimization or informed consent.

5. Conclusion and Future Prospects

AI technologies hold great promise in advancing the field of medicine due to their transformative power and versatility. From assisting analysts in laboratory testing and diagnostic procedures to intraoperative guidance in surgical procedures, the intelligent solutions that use AI methodologies offer a range of potential applications. That said, the application of AI in healthcare is still in its early stages of development. Results from the academic research projects discussed in this essay have not been used as direct support for routine clinical decision making. However, the work carried out in hospitals and laboratories shows potential impacts in the mid-to long-term future. Overall, the research underpinning these AI applications in medicine faces numerous practical, methodological, technological, economic, and ethical issues in order to be fully developed in the futuristic scenarios envisioned.

In the next 5-10 years, the field of healthcare is expected to experience rapid changes due to the convergence of technologies such as AI, digital health, and life science innovation. These changes offer multiple opportunities to democratize medicine and deliver improved patient care. AI has the potential to address acute clinical need, to optimize drug and patient stratification, and to create wealth for nations. These changes will occur through a data-enabled approach to care delivery, with patients as partners and collaborators, connecting across research and care delivery communities. State-of-the-art lab practices can enable early translation to attract investment, and the sharing of intelligence across networks, building a collective body of knowledge expressed through shared digital technology tools and knowledge infrastructure. Investing in the translational space will transform patient care, matching patients to healthcare innovation in a connected ecosystem of healthcare data and delivery. This clinical innovation will drive economic growth and improve efficiencies in care delivery in both the short and long term.

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