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Artificial Intelligence (AI) is revolutionizing healthcare delivery by improving patient care, treatment planning, and diagnostics. AI applications in healthcare such as robotic surgery, virtual health assistants, predictive analytics, and medical imaging analysis. Machine learning (ML) algorithms aid in the early diagnosis of disease, such as AI-powered technologies can accurately detect cancer in radiology images (1). Natural language processing (NLP) helps in extracting insights from electronic health records (EHRs), improving clinical decision-making (2).
AI-driven predictive models help identify patients who are at risk hence require early interventions, as seen in sepsis where prediction of possible onset of sepsis with the help of AI reduces mortality rates (3). AI advances precision medicine by customizing treatments based on genetic data (4).
Application of AI-based drug discovery, where machine learning algorithms speed up the identification of novel compounds and drastically lower research and development (R&D) expenses (5). One of the most prominent applications of robotics in healthcare is robotic-assisted surgery. AI algorithms enhances surgeon’s capabilities by providing real-time data analysis, decision support, and precision control of robotic instruments (6).
in medicine has challenges, such as ethical issues, privacy threats with data, and algorithmic biases that can result in differences in treatment outcomes (7). AI also lacks human intuition and empathy, which are key to patient care, and the question is whether AI can substitute healthcare professionals or complement them (8). As AI takes over routine tasks, human intervention is still crucial for complicated decision-making and ethical decision-making in medicine (2). Further research and regulatory frameworks are essential to the ethical and effective application of AI.
References:
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. //doi.org/10.1038/nature21056
- Topol, E. (2016). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Psnet.ahrq.gov. //psnet.ahrq.gov/issue/deep-medicine-how-artificial-intelligence-can-make-healthcare-human-again
- Shashikumar, S. P., Stanley, M. D., Sadiq, I., Li, Q., Holder, A., Clifford, G. D., & Nemati, S. (2017). Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of Electrocardiology, 50(6), 739–743. //doi.org/10.1016/j.jelectrocard.2017.08.013
- Chayakrit Krittanawong, Virk, H., Sripal Bangalore, Wang, Z., Johnson, K. W., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., Takeshi Kitai, Baber, U., Halperin, J. L., & Tang, W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific Reports, 10(1). //doi.org/10.1038/s41598-020-72685-1
- Kaushik, A. C., & Raj, U. (2020). AI-driven drug discovery: A boon against COVID-19? AI Open, 1, 1–4. //doi.org/10.1016/j.aiopen.2020.07.001
- Panesar, S., Cagle, Y., Chander, D., Morey, J., Fernandez-Miranda, J., & Kliot, M. (2019). Artificial Intelligence and the Future of Surgical Robotics. Annals of Surgery, 270(2), 223–226. //doi.org/10.1097/sla.0000000000003262
- Obermeyer, Z., Powers, B., Vogeli, C., & Sendhil Mullainathan. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. //doi.org/10.1126/science.aax2342
- Enrico Coiera. (2018). The fate of medicine in the time of AI. The Lancet, 392(10162), 2331–2332. //doi.org/10.1016/s0140-6736(18)31925-1
Posted : February 15, 2025 9:19 pm
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Mitigating algorithm bias of Artificial intelligence in Healthcare
Algorithms play an integral role in healthcare ranging from diagnostics, treatment and overall population health management. Biased AI algorithm may lead to inadequate delivery of care. Addressing the bias is important for optimal health outcomes. The assessments are required in context of use eg. the data leveraged during the development would be different than the point of application. There is a need of regular monitoring to avoid drift when algorithm performance reduces over time and there are chances of new bias. For a well-resourced facilities, development of processes to assess and monitor AI algorithmic bias may be feasible but not for under resourced facilities which may lead to poor adoption.
The mitigation is only possible when all key partners together take action to address the bias be it the facility, developers or regulatory bodies. In following ways, Algorithm bias can be addressed by
1. Developers should support bias assessment and monitoring with transparency
2. Establishment of standards and guidelines for assessing and monitoring
3. Low cost tools for testing AI algorithm
Ref: //jamanetwork.com/journals/jama/fullarticle/2823006
Posted : February 17, 2025 1:51 pm
The article "Unlocking Precision Medicine: Clinical Applications of Integrating Health Records, Genetics, and Immunology through Artificial Intelligence" explores how AI enhances precision medicine by integrating complex datasets from electronic health records (EHRs), genomics, and immunology. AI-driven algorithms analyze vast patient data to identify patterns, predict disease risks, and recommend tailored treatments. This approach enables more accurate diagnostics, early disease detection, and personalized therapies, particularly for autoimmune and genetic disorders. Machine learning models improve decision-making by processing multi-source data efficiently, leading to better patient outcomes. However, challenges such as data privacy, bias in AI models, and the need for robust infrastructure limit full-scale implementation.
Despite these challenges, refining AI models, improving data standardization, and ensuring ethical considerations can enhance precision medicine’s effectiveness. With advancements in deep learning and federated learning, AI can overcome limitations like fragmented data and biases, leading to more reliable and equitable healthcare solutions. As AI technology matures, it holds immense potential to optimize treatment plans, improve disease management, and ultimately revolutionize healthcare delivery. By addressing its current limitations, AI-integrated precision medicine offers the opportunity for a more accurate, efficient, and patient-centered healthcare system, improving clinical outcomes and reducing healthcare costs.
Refrences:
Chen, Y.-M., Hsiao, T.-H., Lin, C.-H., & Fann, Y. C. (2025). Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence. Journal of Biomedical Science, 32(1). //doi.org/10.1186/s12929-024-01110-w
Posted : February 18, 2025 8:58 pm
Although medical experts are highly optimistic about incorporating artificial intelligence (AI) in healthcare, limited research has focused on understanding patient viewpoints on these technologies. Because patients are the primary recipients of many AI-driven innovations, it is essential to thoroughly understand their needs, values, and priorities. This approach ensures that these advancements are accepted, ethically developed, and applied to enhance patient care.
Qualitative Research conducted to analyze patient apprehension about using artificial intelligence in healthcare found that although they are largely optimistic about AI's potential to improve their care, they also have concerns regarding the safety and regulation of healthcare AI. They also highlighted that a high level of cautiousness is required while developing and implementing AI tools, and it should be well-tested and accurate.
Further, clinicians were expected to ensure AI safety by retaining final discretion over treatment plans and maintaining responsibility for patient care. They should also have the right to choose the AI tool used in their care and the freedom to opt out if they feel it is unsafe.
Concerns were raised about insurance coverage for treatments, as the AI algorithm might recommend unaffordable treatments. Patients also felt that the electronic health record was not reliable enough for teaching healthcare AI because they found errors in their records. In addition, mass technology failure or system-level crashes and their impacts were also mentioned.
In conclusion, innovative approaches are needed to alleviate patient concerns about healthcare AI. These approaches should integrate trust-building at the system level, forward-thinking technological advancements, and an awareness of the broader social dynamics involved.
References:
Richardson, J.P., Smith, C., Curtis, S. et al. Patient apprehensions about the use of artificial intelligence in healthcare. npj Digit. Med. 4, 140 (2021). //doi.org/10.1038/s41746-021-00509-1
Posted : February 19, 2025 12:40 pm
AI has shown promise in enhancing diagnostic accuracy, improving surgical outcomes, and optimizing healthcare delivery For instance, AI algorithms can analyze medical data significantly faster than traditional methods, leading to better early interventions and reduced complications in surgeries AI's ability to process vast amounts of data allows it to outperform human capabilities in specific tasks, such as detecting skin cancer and predicting patient outcomes.
Despite its advantages, the paper identifies several ethical issues associated with AI in healthcare, including algorithmic bias, lack of transparency, data privacy concerns, and the potential deskilling of healthcare professionals. Several authors emphasize that the "black box" nature of many AI algorithms raises questions about their interpretability and the validity of their predictions. Furthermore, the reliance on biased data can exacerbate existing health disparities, leading to unfair treatment decisions. The need for extensive patient data to train AI models conflicts with the necessity to protect patient privacy. While synthetic data generation techniques can help mitigate privacy risks, the utility of such data remains debatable. Additionally, the integration of AI in clinical settings raises concerns about cybersecurity and the potential for data breaches.
Many research papers called for updated regulations and legal frameworks to address the unique challenges posed by AI in medicine. Existing laws may not adequately cover scenarios where AI systems make autonomous decisions, necessitating the development of new guidelines that ensure accountability and ethical use of AI technologies. There should be transparency in AI operations, ongoing education for healthcare professionals, and the establishment of clear liability standards for AI-related errors. The interrogation of AI should enhance, rather than replace, the human elements of medical practice, ensuring that ethical standards are upheld.
References:
ElHassan, B. T., & Arabi, A. A. (2024). Ethical forethoughts on the use of artificial intelligence in medicine. International Journal of Ethics and Systems (Print). //doi.org/10.1108/ijoes-08-2023-0190
This post was modified 1 year ago by shravani.R
Posted : February 19, 2025 4:21 pm
In order for AI systems to be used in healthcare applications, they must first be "trained" using data from clinical procedures like screening, diagnosis, treatment assignment, and so forth. This will allow the systems to learn about similar subject groups and correlations between subject characteristics and relevant outcomes. Demographics, medical notes, electronic recordings from medical equipment, physical examinations, clinical laboratory results, and pictures are just a few examples of the kinds of clinical data that are frequently available (Jiang et al., 2017).
Even though there is a growing body of research on AI in healthcare, it mostly focuses on three illness types: cancer, diseases of the nervous system, and cardiovascular diseases.
The ML (Machine Learning) component for managing structured data (such as pictures, EP data, and genetic information) and the NLP (Natural Language Processing) component for mining unstructured texts are essential for a successful AI system. Then, the advanced algorithms must be educated using medical data before the system can help doctors diagnose illnesses and recommend treatments.
In this area, the IBM Watson system is a pioneer. Both ML and NLP modules are part of the system, which has shown encouraging results in oncology. For instance, in a cancer study, 99 percent of Watson's therapy suggestions align with the choices made by the doctors. Additionally, Watson developed the AI Genetic Diagnostic Analysis in partnership with Quest Diagnostics. The technology also began to influence real-world clinical procedures (Jiang et al., 2017).
References
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. //doi.org/10.1136/svn-2017-000101
Posted : February 19, 2025 4:58 pm
Artificial Intelligence (AI) is revolutionizing healthcare delivery system by improving diagnostic, treatment and operational efficiency. AI is changing healthcare by helping doctors to plan better treatment and make the hospital work easier. Some tools and machines help in the early disease detection and treatment. However, some challenges are there i.e. data privacy and ethical concerns. AI has great potential to improve healthcare but it must be used carefully with proper rules and monitoring.
References:
Olawade, D. B., David-Olawade, A. C., Wada, O. Z., Asaolu, A. J., Adereni, T., & Ling, J. (2024). Artificial intelligence in healthcare delivery: Prospects and pitfalls. Journal of Medicine, Surgery, and Public Health, 100108.
Posted : February 22, 2025 2:54 pm
Artificial Intelligence (AI) is revolutionizing healthcare delivery by enhancing diagnostics, improving patient care, and optimizing hospital operations. AI-driven technologies, including machine learning (ML), natural language processing (NLP), and robotics, are transforming medical decision-making, disease prediction, and treatment outcomes. This paper explores the key applications of AI in healthcare and its potential to improve efficiency, reduce errors, and enhance patient experiences.
Applications of AI in Healthcare
1. Medical Diagnostics and Imaging
AI algorithms are extensively used in radiology, pathology, and medical imaging to detect diseases such as cancer, cardiovascular conditions, and neurological disorders. Deep learning models analyze medical images with remarkable accuracy, often surpassing human radiologists. For instance, AI-powered tools like Google's DeepMind have demonstrated high precision in diagnosing eye diseases using retinal scans (De Fauw et al., 2018).
2. Predictive Analytics and Disease Forecasting
AI models help predict disease outbreaks, patient deterioration, and individual health risks. AI can forecast chronic disease progression and recommend preventive measures by analyzing large datasets from electronic health records (EHRs). For example, IBM Watson Health utilizes AI to analyze patient history and suggest personalized treatment options (Jiang et al., 2017).
3. Personalized Medicine and Drug Discovery
AI-driven precision medicine tailors treatments to individuals based on their genetic makeup and medical history. AI accelerates drug discovery by analyzing molecular structures, predicting drug interactions, and identifying potential compounds. Companies like BenevolentAI and Atomwise use AI to develop new treatments faster and more efficiently (Mak & Pichika, 2019).
4. Virtual Health Assistants and Chatbots
AI-powered virtual assistants, such as chatbots and voice recognition systems, support healthcare professionals by handling administrative tasks and assisting patients with medical queries. Chatbots like Ada Health and Babylon Health provide symptom assessment and triage recommendations, reducing the burden on healthcare providers (Topol, 2019).
5. Robotic Surgery and Automation
AI-powered robotic systems assist in complex surgeries with high precision, reducing the risk of complications. The da Vinci Surgical System enables minimally invasive procedures, enhancing patient recovery times (Hashimoto et al., 2018). AI-driven automation also optimizes hospital workflows, from scheduling appointments to managing supply chains.
Challenges and Ethical Considerations
Despite its advantages, AI in healthcare faces challenges, including data privacy concerns, bias in algorithms, and regulatory hurdles. Ensuring transparency, fairness, and patient safety is crucial in AI deployment. The ethical use of AI requires robust policies and collaboration between stakeholders, including governments, healthcare providers, and technology firms (McCradden et al., 2020).
Conclusion
AI is transforming healthcare delivery by enhancing diagnostics, treatment planning, and operational efficiency. While challenges exist, AI's potential to revolutionize patient care is undeniable. Future research and ethical considerations will determine the successful integration of AI into global healthcare systems.
References
- De Fauw, J., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350.
- Jiang, F., et al. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, 2(4), 230-243.
- Mak, K.-K., & Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773-780.
- Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
- Hashimoto, D. A., et al. (2018). Artificial intelligence in surgery: Promises and perils. Annals of Surgery, 268(1), 70-76.
- McCradden, M. D., et al. (2020). Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet Digital Health, 2(5), e221-e223.
Posted : February 23, 2025 2:27 am
Artificial intelligence is transforming healthcare by boosting patient care, streamlining processes and strengthening diagnostics. Precision medicine, telehealth and automated diagnostics like diabetic retinopathy screening can all benefit from AI's ability to evaluate large datasets through recent developments in machine learning and deep learning. But integrating AI in healthcare calls for a methodical strategy that takes into account ethical issues, thorough validation, regulatory compliance and stakeholder participation. Even while AI is currently helping with imaging and clinical decision-making, its future holds the potential for digital twin models for predictive healthcare, individualized treatments and AI-driven drug development. Issues like bias, capacity and data privacy still exist despite its potential. Adoption of AI may encourage innovation in healthcare and make it more patient-centered, equitable and efficient if done responsibly and cooperatively.
Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med. 2023 Jun 5;13(6):951. doi: 10.3390/jpm13060951. PMID: 37373940; PMCID: PMC10301994.
Posted : February 24, 2025 10:11 am
The predictive powers of artificial intelligence (AI) are improved when individual patient data is combined with other data from the health system to create better decision-making through enhanced public health monitoring in addition to generating more focused, efficient, and lean research and development.(1) As AI becomes a crucial component of healthcare, it will be able to help all medical professionals, whether it be with automated patient data documentation, fast-tracking image analysis, virtual observation, diagnosis, rehabilitation, mental health support, and patient outreach.(2-4)
AI can prove to be an efficient tool for identification of early infections, developing treatment protocols, drugs and vaccine development. (5) In India, artificial intelligence is revolutionizing healthcare, especially by expanding access to high-quality healthcare in rural areas. Prominent technology firms like Google and Microsoft are actively funding AI infrastructure, working with hospital chains, and launching pilot programs to improve healthcare delivery nationwide. (6-7)
Indian policy space accelerating use of AI in healthcare delivery
The Indian government through NITI Aayog has been actively advancing AI in healthcare since 2018. The motto adopted by NITI Aayog in this venture is ‘AI for all’ (#AIforAll). Healthcare, one of the five major industries designated for AI-driven change, is getting targeted policy support to guarantee data security, privacy, and moral AI application. In order to promote sustainable AI solutions for better healthcare delivery, NITI Aayog suggests establishing a National Artificial Intelligence Marketplace, encouraging collaboration, and filling data gaps. (8)
Promotion of AI in India
The Indian government has implemented NITI Aayog. AI in healthcare is being actively promoted by the Indian government, with NITI Aayog and the Union Health Ministry investigating its potential in public health. Indian AI developers are being supported through initiatives like Big Data sets and AI-driven solutions. The lack of awareness is a significant obstacle to the rapid advancement of AI in several states. The establishment of an AI database is suggested as a solution to this problem in order to make information easily accessible and promote the use of AI nationwide.(9)
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Gujral G, Shivarama J, Mariappan M. Artificial intelligence and data science for developing intelligent health informatics systems. Proceedings of the National Conference on AI in HI & VR, SHSS-TISS; 2019 Aug 30-31; Mumbai. Available online: //www. researchgate.net/publication/338375465_ARTIFICIAL_ INTELLIGENCE_AND_DATA_SCIENCE_FOR_ DEVELOPING_INTELLIGENT_HEALTH_ INFORMATICS_SYSTEMS
- Murali, A, Jayadevan, PK. India’s bid to harness AI for healthcare. Factor Daily [Internet] 2019 April 4. Available online: //factordaily.com/ai-for-healthcare-in-india/
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Mahajan A, Vaidya T, Gupta A, et al. Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Res Stat Treat 2019;2:182-9
Posted : February 24, 2025 11:00 am
Artificial intelligence is the leading technology that can significantly help in each and every facet of the healthcare industry ranging from prevention, screening, diagnosis to treatment. From diagnosis to robotic assisted surgeries, it improves disease prediction, diagnosis and treatment.
AI performs tasks in fraction of minutes and at a low cost. It assists in finding codes, administrative tasks, robotic surgeries to personalized treatment. It enhances the experience of both the healthcare professional as well as for patients.
AI in medical diagnosis
On an average out of 400,000 hospitalizations, 100,000 deaths occur which are preventable. Incomplete medical histories and larger caseloads can lead to deadly human errors which can be corrected by AI diagnostic tools.
AI in drug discovery
Drug industry spends a hefty amount of money on development and testing of a drug. Out of which only 10% drugs are brought to market. Due to breakthroughs in technology, AI is speeding up this process from designing a drug to selecting groups for clinical trials and predicting the side effects.
AI for patients
AI has been supporting patient documentation, tailored treatment plans, scheduled reminders, to digital communication. This in turn has been helping the medical industry to treat more patients on a daily basis due to less time on patient counselling and less paperwork. Several AI tools for medical scribing to coding helps the healthcare industry to do tasks more efficiently and accurately.
AI in data management
Because of the ability of AI to manage massive amounts of data that used to take years to process, it can reduce time and cost of administrative work.
AI in robotic surgery
Use of AI in hospitals has done tremendous job ranging from minimally invasive procedures to open heart surgeries. Robotic assisted surgeries provide magnified and three dimensional view to surgeons which helps in quick recovery of the patient with less pain and post surgical complications.
References:
1. Daley Sam. (n.d.). Artificial intelligence in healthcare: 39 examples improving the future of medicine. Built In. //builtin.com/artificial-intelligence/artificial-intelligence-healthcare
2. Saadat M. Alhashmi, Ibrahim Abaker Targio Hashem, Islam Al-Qudah. (2023, November 30). Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis. elsevier. //www.sciencedirect.com/science/article/pii/S2667305323001242
Posted : February 27, 2025 3:38 pm
The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services. It is imperative to document and disseminate information regarding AI’s role in clinical practice, to equip healthcare providers with the knowledge and tools necessary for effective implementation in patient care. This review article aims to explore the current state of AI in healthcare, its potential benefits, limitations, and challenges, and to provide insights into its future development.
The integration of AI in healthcare has immense potential to revolutionize patient care and outcomes. AI-driven predictive analytics can enhance the accuracy, efficiency, and cost-effectiveness of disease diagnosis and clinical laboratory testing. Additionally, AI can aid in population health management and guideline establishment, providing real-time, accurate information and optimizing medication choices. Integrating AI in virtual health and mental health support has shown promise in improving patient care. However, it is important to address limitations such as bias and lack of personalization to ensure equitable and effective use of AI.
Several measures must be taken to ensure responsible and effective implementation of AI in healthcare.
Firstly, comprehensive cybersecurity strategies and robust security measures should be developed and implemented to protect patient data and critical healthcare operations. Collaboration between healthcare organizations, AI researchers, and regulatory bodies is crucial to establishing guidelines and standards for AI algorithms and their use in clinical decision-making. Investment in research and development is also necessary to advance AI technologies tailored to address healthcare challenges.
AI algorithms can continuously examine factors such as population demographics, disease prevalence, and geographical distribution. This can identify patients at a higher risk of certain conditions, aiding in prevention or treatment. Edge analytics can also detect irregularities and predict potential healthcare events, ensuring that resources like vaccines are available where most needed.
Public perception of AI in healthcare varies, with individuals expressing willingness to use AI for health purposes while still preferring human practitioners in complex issues. Trust-building and patient education are crucial for the successful integration of AI in healthcare practice. Overcoming challenges like data quality, privacy, bias, and the need for human expertise is essential for responsible and effective AI integration.
Collaboration among stakeholders is vital for robust AI systems, ethical guidelines, and patient and provider trust. Continued research, innovation, and interdisciplinary collaboration are important to unlock the full potential of AI in healthcare. With successful integration, AI is anticipated to revolutionize healthcare, leading to improved patient outcomes, enhanced efficiency, and better access to personalized treatment and quality care.
Reference:
Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23, 689 (2023). //doi.org/10.1186/s12909-023-04698-z
Posted : March 1, 2025 3:58 pm
With the potential to drastically alter medical practice and healthcare delivery, artificial intelligence (AI) is a formidable and innovative field of computer science. In this review paper, authors analyze the potential future direction of AI-augmented healthcare systems, explain recent advancements in the use of AI in healthcare, and establish a roadmap for creating safe, dependable, and successful AI systems.
Read in detail: //pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
Posted : March 1, 2025 4:40 pm