1. Introduction

Gone are the days when only medical professionals handled healthcare delivery. Today, advancements in technology witness a range of activities, diagnostics, tests, therapies, and innate treatments carried out by robots, drones, and cloud computing technologies which are based on Artificial Intelligence (AI) analyses and insights. Today’s tech-enabled therapeutic processes and Digital Therapeutics (DTx) enable precisely structured human-machine partnerships and relationships optimized between medics, caregivers, controllers, medical machines, and patients to create amazing patient-health outflows both from the hospitals and homes. Digitization in healthcare is transforming the healthcare industry into the bio-digital world. More than machinery and equipment, healthcare technologies promise precision, efficient workflows, and an all-new focus on patient health management. So, it has become an earnest need to effectively channelize the research to directly improve patient care, hospital revenues, and to cut down medical errors. This calls for industrial adaptation, agile engineering, or healthcare delivery intelligence.

Researchers in AI have had success in promoting nurture of the key technical advancements in healthcare advantageous triggers like image recognition, natural language processing, predictive analytics, deep learning, machine learning, single-cell transcriptomics, representation learning and intelligent data generation, autonomous automation systems for next-generation drug discovery, predictions, and analysis of diseases, pharmacogenomics, and prediction of human drug metabolism. However, these technological breakthroughs stand far and require agile engineering into patient engagement, monitoring, rounded range diagnostics, hospital operations, therapeutic sessions, rehabilitation treatments, and other medical scenarios. Digital therapeutics, especially, are vitalized therapeutics that are transforming patient care among the chronic, mental, neurological, cardiac, and metabolic patient populations.

2. The Role of Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making a significant impact on many industries; healthcare is no exception. AI technology has advanced significantly in recent years. Available computing power is exponentially greater than what it was just five years ago. The data storage tanks needed to store information generated on patients’ electronic health records (EHRs) are also now readily available. As one of the top drivers in cost and innovation, EHRs started a revolution in medicine that now enables the application of AI to improve patient care in many ways.

AI occupies a unique space in healthcare. It has the ability to process and interpret massive amounts of data faster and more accurately than humans. AI can also be in multiple places at once, standardizing care protocols and offering alternatives for treatment. While the use of AI may reduce costs or free human resources, enhancing patient care is the top outcome when applying artificial intelligence to healthcare. AI impacts healthcare through three central applications: robotics, diagnostics, and predictive analytics. At the Physicians Summit held at the 2021 Dubai Health Forum, more examples of how AI is used for healthcare in the region were discussed, including enterprise applications, workflow, assisted decision making, sign language interpretation, and the recovery of mental health in children. As these and more AI technologies are harnessed for healthcare, there is great potential for the gap between healthcare and technology to further close, and the most possible “care for all” will occur.

2.1. 1.1 Advancements in AI Technologies

Advancements in AI Technologies

The digital rupture does not end with the advent of AI technologies in healthcare; on the contrary, the development of AI technologies has increased at an exciting pace, enhancing, for instance, how medical imaging is visualized or how severe diseases may be addressed. After IBM’s Watson defeated Jeopardy world experts back in 2011, the general consensus was that AI was finally “out to pasture.” The next step, it was thought, was to apply that technology to various domains, including healthcare. With this in mind, 2022 has already seen several breakthroughs in AI advances, including:

Federated learning, a new ML concept, allows data sharing across platforms without storing individual patient-specific data—an especially timely concept in the practice of telemedicine. Releasing massive language models such as OpenAI’s GPT-3, which has increased the overall NLP benchmark performance. The National Institute for Standards and Technology recently introduced Biometric Conformance Test Software to Augment Certification (BioCT), a software package that works together with traditional Automated Biometric Identification System (ABIS) technology.

LLNL’s focus has joined public endeavor to recalibrate and rejuvenate the underlying technologies and strategies associated with Abigail. Most significantly, the laboratory is exploring how to improve upon the well-recognized aspects of AI, such as image processing, NLP, and others, to introduce new model capabilities in those same areas to yield higher-performance and more impactful deliverables than conventional “Go” and “Watson” AI systems have previously. Additionally, LLNL is seeking to include new technologies that enhance certain healthcare characteristics such as precision. To this end, the laboratory is currently looking into Temporal and Situational AI models, Interpretable Deep Learning, transformative variation of Bi-LSTMs, ideas for encouraging better accuracy or biometric modalities, and other new developments in the AI field.

2.2. 1.2 Applications of AI in Healthcare

1.2.2 Applications of AI in Healthcare

Complementing the advancements in AI technologies, it is of paramount importance to discuss the application of AI, not only in the digital world, but also its practical implementation in clinical settings and patient care. AI is currently being utilized in a variety of applications within healthcare environments including diagnostics, treatment planning, and treatment summation, for drug discovery and development, robotics, personalization of healthcare, patient involvement, cost reduction, and administration, where a few are discussed in the subsections below.

1.2.2.1 Applications in clinical settings

In terms of AI development, increasing attention is turning to support rather than replacing the clinician. This has come to be known as the inclusion of artificial intelligence into the care pathway. Clinicians have also welcomed the opportunity to individualize care beyond the reach of evidence-based medicine.

1.2.2.2 AI-assisted diagnostics

Broadly speaking, diagnostic applications of AI within healthcare are used to support medical professionals, from assisting to significantly complexifying workflows. In reflection of growing sensitivity and specificity, commercially available AI-driven diagnostic systems span across pathologies. In terms of worked-upon conditions, there is a balanced interest between prevalent tumors and rarer pathologies. A number of diagnostic support tools can be sighted across imaging-based studies. The extension of combining results of multiple imaging modalities is demonstrated truly helpful because each modality may provide different information on the anatomical structures or provide different contrast components.

3. Challenges and Opportunities in Implementing AI in Healthcare

Healthcare and medicine have always been envisioned as disciplines driven by data, and AI, particularly in the form of various machine learning technologies, is becoming a driving force behind data analysis and interpretation. AI applications in healthcare are numerous and its deployment is becoming feasible thanks to the growing volume of digital medical data collected for various purposes, the expanding adaptation of electronic health records (EHRs), and the development of privacy-preserving machine learning methods. The application of AI in healthcare is expected to improve processes of diagnosis and predictions of patient course and outcome by, for example, combining patients’ multi-omics data with their phenotypic and lifestyle data.

In addition, wearable devices enable continuous monitoring of patient health (including vital signs, patient activity, and sleep patterns), while in-home sensors collect daily living data (food intake, movement patterns, social interactions). Each of these data represents one of the six categories used to identify a patient as an individual. While AI used in tandem with EHRs has the potential to help healthcare practitioners extract valuable insights from the ever-increasing volume of clinical data for patient care, several challenges and ethical, regulatory considerations have to be considered, which presents hurdles for integrating AI more per the greater healthcare landscape. Patient privacy and protection of sensitive personal data, alongside the resource-intensive methods of AI development and patient risk contained within AI, are some of the important areas of focus for advancing AI and healthcare as a positive tool for patient care.

3.1. 2.1 Ethical and Regulatory Considerations

Ethical and Regulatory Considerations. The integration of AI in seeking healthcare can lead to situations that involve ethical dilemmas and decisions based on the professional’s moral values. It challenges and complicates the relationship between the professional and patient, as well as that between the professional and the patient’s family as professional and institutional-associated values are questioned. As described by Kim et al., it “engenders a myriad of sociocultural, ethical, and economic issues”, such as characterization of “what might be meant by better,” assessment of “what features of imaging data have relevance to patient outcomes,” and determination of “what outcomes a given population is most worried about.” Common questions of an ethical nature revolve around confidentiality, consent, and responsibility. A major critique of the use of AI lies in the lack of understanding of these systems, as stated by Townsend and Noel: “most individuals are simply mystified regarding how AI-enabled algorithms operate.”

On the regulatory side, AI does not exist in a regulatory vacuum. Healthcare regulations may provide guidance on AI, either as a general information technology or as part of medical devices or in vitro diagnostics, with which they have specific regulatory obligations. For example, in the United States, multi-stakeholder guidelines have been suggested by the FDA to assure responsible and reliable AI regulation. These guidelines discuss various AI and ML topics that are relevant in a medical context, ensuring that any AI capabilities are built to “be ‘interpretable and ‘trouble-shootable'”, rather than “unexplained and untrouble-shootable.” Consequently, given the necessity for healthcare professionals to adhere to regulatory frameworks in the provision of care, the regulations governing AI need to be followed. While AI capabilities are emerging at a fast pace, integrating AI systems in healthcare should not overly encode the AI with the potential to disregard what is best for the patient. Healthcare innovation is only Irish when it is patient-first, data-driven, and ethically sound.

3.2. 2.2 Data Privacy and Security

As AI systems are increasingly used to assist in clinical decision making and processing of patient data, there are growing concerns about the security and privacy risks. The clinical data, especially some sensitive patient-related information, is crucial to enable the AI model to generate trustable and useful predictions. The diversity of data sources including historical and new patient data, from different sites and locations, poses potential significant privacy risks to the individuals in the dataset. Additionally, the sharing or transmitting of this data between healthcare centers, patients, or other connecting intermediaries, needs to be carefully monitored to prevent potential data breaches and unauthorized access. The European General Data Protection Regulation (GDPR) and the US Health Insurance Portability and Accountability Act (HIPAA) frameworks are crucial to inform how to govern and manage this. Importantly, health departments and researchers need to take extra precautions and follow regulations to ensure accurate data protection and licensures are in place. Health data access should be granted to only authorized personnel after these factors have been approved.

Healthcare data privacy and security is an emerging issue for AI development. A lack of trust increases the risk of patients or other individuals withholding data, thereby leading to the under-production of top-notch AI technologies. Confidentiality, integrity of healthcare information and first-class care should always be provided to the patient who trusts the clinician with their information. Any sensor output data and storage of or manipulation of clinical information outside health-based services must meet patient and clinician privacy requirements. At the same time, the benefits of using transparent data to train broader AI systems offer the potential to change the landscape of medical diagnostic services. Data protection, privacy, consent practices, socio-ethical issues, and public benefits will continue to influence global, local, and political decisions on AI futures. The same rules must be applied to those involved in initiatives such as international databases of clinical findings and possible future drug and surgical clinical trials.

4. Case Studies and Examples of AI in Healthcare

Medicine and healthcare are just beginning to tap into the tremendous power of artificial intelligence. The biggest potential of AI lies in the increasing interaction between patients, doctors, and technology. Currently, the integration of automated technology in clinics and hospitals is being embraced to enhance the safety and efficiency for both doctors and patients. However, some of this information may be outdated for the general public, particularly for practitioners and stakeholders. It is important to note that the current robots used in hospitals are much more advanced, thanks to proper GPU systems. As artificial intelligence impacts everyone, it is crucial to consider the specific needs of each individual’s body parts for better and more accurate human or robot-assisted surgeries. Two areas of study in this field include preoperative planning and the use of intelligent vision, which focuses on innate convolutional neural networks and the processing of fundus images to detect diabetic retinopathy using canny edge detection algorithms.

5. Future Directions and Implications for Patient Care

The impact on patient care

In the future, AI and machine learning may lead to specific advancements in patient care. AI in health data analysis can identify patient behavior patterns and patient therapy-specific responses. AI can also enhance existing healthcare paradigms, create new health assessment processes, and promote personalized treatment pathways. From a patient recruitment perspective, AI could affect patient screening, stratification, and lead identification, as well as identify genetic predispositions for health. One emerging trend is precision health, propelled forward by precision medicine. AI and machine learning could provide further insight into a patient’s individual environment by analyzing multi-dimensional health data. For example, multi-omics data such as genomics, transcriptomics, and epigenomics can paint a detailed picture of a person’s molecular health. Human phenome data, including physiology, biochemistry, and lifestyle, can also be examined. An international collaborative document first mentioned precision health as an umbrella term to incorporate the many factors outside an individual’s body that affect human health.

One of the most transformative healthcare trends is the move toward personalized medicine. In years to come, AI can be used to develop patient- or disease-specific treatments. For example, AI-generated 3D images of diseases or computer simulations could predict treatment response or identify complications. Predictive medicine could also be used to identify susceptible patients and change their behavior to prevent disease. Many of the potential future AI applications presented in this section support precision, personalized, and predictive healthcare at the patient-specific level. Precision medicine is a new approach for disease treatment and prevention that takes into account individual variability in environment, lifestyle, and genes for each patient. This approach allows doctors and researchers to predict more accurately which treatment and prevention strategy for a disease will work in which groups of people. It is in contrast to a one-size-fits-all approach, where a single treatment strategy for a disease, irrespective of patient-specific parameters, is equally effective for all the patients. The Precision Medicine Initiative was started in 2016 by US President Barack Obama and, among other goals, aimed to develop a reservoir of highly qualified patient data to advance precision medicine. The collaborative document characterizes precision medicine as the ability to predict disease before the onset of symptoms and prescribe a patient-specific treatment. Predictive analytics allows useful information, such as patients’ likelihood of developing chronic conditions, to make better-informed decisions. The document also references the need to further develop the tools and technologies required to analyze the large amount of data generated by patients, including the use of machine learning algorithms. Predictive medicine can be used for population as well as individual benefits.

Speaking of AI and its impact on healthcare, you might be interested in learning more about Artificial Intelligence in Healthcare, which explores the various applications and implications of AI technologies in medical environments. Additionally, understanding Ethics of Artificial Intelligence could provide insight into the moral considerations that come with integrating AI into patient care. For further reading, the concept of Precision Medicine highlights how tailored treatments can improve health outcomes, making it essential knowledge for anyone interested in the future of medical advancements.