Introduction:
The integration of artificial intelligence (AI) in healthcare has revolutionized diagnostics, treatment, and patient monitoring. AI’s ability to quickly analyze vast clinical data has improved disease detection and treatment personalization. It has applications in various areas, from early disease detection in radiology to predicting outcomes from health records.
AI in Healthcare enhances hospital and clinic efficiency, making healthcare smarter and faster globally. IBM’s Watson marked the beginning of AI in healthcare, focusing on natural language processing. Today, tech giants like Apple and Microsoft are investing in AI for healthcare. AI has transformed medical imaging, diagnostics, patient care, research, drug discovery, and administrative tasks, improving clinical outcomes and research speed. However, challenges like privacy and safety remain, requiring responsible governance. Despite challenges, AI offers hope for better healthcare, especially in the face of challenges like the COVID-19 pandemic.
Role of AI in Healthcare
AI technologies play a crucial role in healthcare, aiding in medical imaging, diagnostics, pandemic response, virtual care, patient engagement, and reducing administrative burdens. Additionally, AI can be utilized for a variety of purposes, from enhancing photos with new AI photo trick to transforming industries.
Medical Imaging and Diagnostic Services
AI in healthcare is revolutionizing early disease detection and reducing diagnostic errors, particularly in radiology. It analyzes echocardiography scans for heart disease, identifies cancer and pneumonia, and screens for neurological disorders like Parkinson’s.
During the COVID-19 pandemic, AI in healthcare plays a crucial role in early diagnosis using X-rays, CT scans, and ultrasound. The transformer model, for instance, distinguishes COVID-19 from pneumonia in imaging. It also categorizes breast tissues from ultrasound images more effectively than conventional methods.
However, the use of AI-driven medical advice, such as ChatGPT, raises concerns about its application in diagnoses and treatment, emphasizing the need for caution. In medical education, AI’s Generative Adversarial Networks (GANs) create realistic images for training and simulations.
In medical practice, known as MeTAI, AI aids in medical imaging-guided diagnosis and therapy. Despite its advantages, AI in healthcare faces challenges, including security, disparity, and privacy. While AI enhances clinical decision-making, it requires improved outcome assessment to prevent overdiagnosis.
Virtual Patient Care
The use of wearable technology and AI in healthcare has transformed patient care through virtual monitoring and management, now a standard practice. AI controls chronic diseases like diabetes and hypertension using wearable sensors. Smart sensor systems monitor home environments and health, aiding in elderly care. Wearable devices detect conditions like atrial fibrillation, supporting precise diagnoses. AI models predict emotional states using mobile sensor data, showing promise for assessing patient moods.
During the COVID-19 pandemic, wearable devices measuring physiological changes enable early COVID-19 prediction and real-time case monitoring. AI, ML, and big data predict disease progression and diagnose SARS-CoV-2. Telemedicine, especially in metaverse applications, offers better experiences than traditional telemedicine, utilizing technologies like AR for real-time interactions with clinicians.
Remote patient monitoring (RPM) allows providers to monitor patients outside traditional settings, using sensors and communication tech. AI-powered RPM designs detect early deterioration and personalize health monitoring. However, AI in RPM faces challenges such as privacy and data volume. ChatGPT serves as a chatbot providing precise medical information, assisting in RPM and patient health maintenance. Challenges for Installed Wearable Patient-Monitoring systems include data connectivity, cost, and user awareness.
Medical Research and Drug Discovery
AI is key in medical research and drug discovery, analyzing data, and aiding innovation. It helps predict candidates for trials, model biological processes, and streamline drug development. ML improves trial outcomes, aids participant selection, and enhances data analysis. NLP tools assist in participant management, while generative AI creates synthetic data for research.
Metaverse apps offer controlled settings for trials and remote collaboration. In drug discovery, AI reduces time and cost. ML, bioinformatics, and cheminformatics models speed up the process. AI-based robot scientists accelerate drug development, identify new targets, and aid in drug structure design and repurposing.
ChatGPT assists in drug discovery by analyzing evidence and generating ideas. Algorithms predict drug activity and toxicity. For vaccines, AI reviews viral proteins to identify immune response triggers, aiding in design.
In healthcare, AI supports viral genomic sequencing and drug development for diseases like COVID-19. AI is crucial in advancing medical research and drug discovery, offering solutions to complex challenges.
Patient Engagement and Compliance
Patient engagement and compliance are critical factors in healthcare that significantly impact health outcomes. Healthcare providers often develop treatment plans to improve patients’ acute or chronic health conditions, but these plans are less effective if patients do not make the necessary behavioral changes, such as managing weight, scheduling follow-up visits, and adhering to treatment plans.
Apps and online portals that facilitate communication between patients and healthcare providers have shown significant improvements in engagement rates. ChatGPT is being integrated into various healthcare apps to automate tasks such as summarizing medical notes, writing reports, and producing summaries, making these tasks more efficient and time-saving.
Rehabilitation
AI is revolutionizing rehabilitation through its applications in physical (robotics) and virtual (informatics) realms. Technological advancements in AI and robotics are transforming rehabilitation practices. ChatGPT is used in rehabilitation sessions to complement traditional therapy, providing tailored and collaborative assistance.
Metaverse neurorehabilitation integrates AI-based systems for gross motor function classification, rehabilitation rewards, virtual character movements, and deep learning-based movement assessments. In sports medicine, AI integrated into wearable technologies can monitor physiological measurements and positional data to improve athlete performance and injury prediction models.
Administrative Applications
AI can streamline administrative tasks by populating structured data areas from therapeutic notes, retrieving key data from past medical records, and collecting documented patient encounters. Amazon is developing an ML solution to extract valuable information from unstructured EHR data and scientific notes. Robotic Process Automation (RPA) is used for healthcare functions such as clinical records, revenue cycle administration, claim handling, and medical record management. A hybrid ML-based decision support system (ML rule-based expert system) was found to be highly accurate in detecting prescribing errors in clinical settings. Pharmacists should leverage these tools while maintaining their social relationships with patients and healthcare teams.
Challenges Faced by AI in Healthcare
Ethical and Social Challenges
The increased use of AI in healthcare raises ethical and social challenges such as accountability, potential errors in judgments, data protection, biases, and public confidence. Safety concerns include error detection and severe consequences.
Transparency and accountability issues involve AI-made decisions, compensation, and authentication. AI’s evolving nature poses challenges in explaining decisions. Data scarcity affects AI training and can lead to biased outcomes. Privacy and security challenges arise from using sensitive data. Upholding medical-ethical principles like beneficence and autonomy is crucial before integrating AI into healthcare systems.
Governance Challenges
Governance challenges in AI implementation in healthcare require addressing regulatory, ethical, and trust issues. Hospital-level governance can address these issues effectively. Governing AI at the healthcare system level is crucial for patient safety, clinician confidence, and health outcomes. Comprehensive governance structures should encompass clinical, operational, and leadership domains. AI’s rapid development requires national and international regulations to ensure ethical and safe use, as seen in the EU’s GDPR and the proposed AIA, which aim to regulate AI and protect personal data.
Technical Challenges
AI models must be simple for HCPs to operate efficiently. However, challenges in adopting AI in healthcare include the lack of capacity to develop and maintain IT infrastructure, increased costs associated with storing and backing up data, and ensuring data validity. AI algorithms may suffer from inapplicability outside the training domain, bias, and brittleness. Important considerations include dataset shifts, unintended biases in clinical practice, interpretability of algorithms, and generalization to different populations. Healthcare providers should develop an effective strategic plan to address these issues.
HCPs often mistrust or poorly understand AI clinical decision support systems due to unidentified risks, hindering adoption. Explainable AI (XAI) solutions can enhance end-user trust. Factors such as risks, trustworthiness of AI, workload, and willingness to receive AI training influence clinicians’ perceptions. The lack of AI accountability is an inhibiting factor. AI training should be included in medical and nursing curricula.
AI systems pose a black-box problem, where HCPs are unaware of how results are derived. Blaming healthcare practitioners for AI errors could hinder adoption. Policies and measures should be developed to protect doctors and AI. Improving healthcare professionals’ risk perception and performance expectations of AI is crucial. A user-friendly AI interface with clinically relevant information is essential. Engaging all stakeholders and understanding clinical needs is necessary before promoting AI in healthcare.
Disadvantages of AI in Healthcare
AI in healthcare faces challenges such as the need for huge datasets, data accessibility issues, and data security concerns. ML-based systems can improve with more data but face resistance to data sharing.
Data security is crucial due to the value and vulnerability of health records. Overfitting occurs when algorithms inaccurately predict outcomes due to many influencing variables. Data leakage can lead to inaccurate predictions beyond the training dataset.
Deep learning algorithms lack explanation for their predictions, which can lead to legal and trust issues. The workforce may fear AI replacing their jobs, requiring re-engineering and training costs. Insufficient experimental data and reluctance to adopt AI-based solutions due to a lack of empirical data are also challenges. Overall, AI in healthcare faces issues of cost, job displacement, and the need for human-like qualities.
Conclusions
AI technologies play a crucial role in healthcare, aiding in medical imaging, diagnostics, pandemic response, virtual care, patient engagement, and reducing administrative burdens. They also spur innovation in drug development and rehabilitation.
Despite these benefits, AI in healthcare encounters challenges such as data security, privacy, and its inability to replicate human qualities like compassion. While AI in healthcare proves efficient, it cannot supplant human connections and teamwork.
Future governance of AI in healthcare must prioritize people’s interests and encompass technical, ethical, and social considerations. This study contributes to the existing literature on AI applications in healthcare and tackles challenges in adoption faced by healthcare professionals.
References
- Snowdon, A. Digital Health: A Framework for Healthcare Transformation. 2020. Available online: https://www.gs1ca.org/documents/digital_health-affht.pdf(accessed on 23 January 2023).
- Williams, O.D. COVID-19 and Private Health: Market and Governance Failure. Development2020, 63, 181–190. [Google Scholar] [CrossRef] [PubMed]
- Tabriz, A.A.; Nouri, E.; Vu, H.T.; Nghiem, V.T.; Bettilyon, B.; Gholamhoseyni, P.; Kiapour, N. What should accountable care organizations learn from the failure of health maintenance organizations? A theory based systematic review of the literature. Determ. Health2017, 3, 222–247. [Google Scholar] [CrossRef]
- Rand Review. Chronic Conditions in America: Price and Prevalence. 2017. Available online: https://www.rand.org/blog/rand-review/2017/07/chronic-conditions-in-america-price-and-prevalence.html(accessed on 11 July 2021).
- World Health Organization. Naming the Coronavirus Disease (COVID-19) and the Virus that Causes It. 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(COVID-2019)-and-the-virus-that-causes-it(accessed on 6 July 2021).
- Butcher, C.J.T.; Hussain, W. Digital Healthcare: The Future, RCP Journals. Royal College of Physicians. 2022. Available online: https://www.rcpjournals.org/content/futurehosp/9/2/113(accessed on 16 January 2023).
- Siriwardhana, Y.; Gür, G.; Ylianttila, M.; Liyanage, M. The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges. ICT Express2020, 7, 244–252. [Google Scholar] [CrossRef]
