Authors: Sesha Bhargavi Velagaleti, A.Vijaya Krishna, M. Jyothi, D. Sree Lakshmi
Certificate: View Certificate
Artificial Intelligence (AI) is rapidly transforming the healthcare industry, with the potential to improve patient outcomes, reduce costs, and enhance the overall quality of care. This paper explores the emerging trends in AI in health tech, including machine learning, natural language processing, robotics, and computer vision. It also examines the challenges and opportunities associated with these emerging technologies, as well as their impact on healthcare delivery, research, and patient engagement. The availability of an enormous amount of data supported by limitless cloud storage is a significant benefit that aids in the development of many AI applications. The learning algorithms are trained using this data to improve their accuracy. The algorithms interact with the practise data to provide fresh perspectives on diagnosis and therapy. As a result, it enhances patients\' results. A case study on Diagnosis and Treatment of Skin Cancer was discussed and a suitable architecture is proposed.
Artificial Intelligence has been the buzzword in healthcare for the past few years, and its applications in the industry are growing rapidly. AI is being used to improve patient outcomes, reduce costs, and enhance the overall quality of care. The use of AI in health tech is driven by the need to improve efficiency, reduce errors, and provide better care to patients. The purpose of this paper is to explore the emerging trends in AI in health tech, including machine learning, natural language processing, robotics, and computer vision. We will also examine the challenges and opportunities associated with these emerging technologies, as well as their impact on healthcare delivery, research, and patient engagement.
Machine Learning: Machine learning is one of the most significant emerging trends in AI in health tech. Machine learning algorithms can be used to analyze vast amounts of data, including patient data, to identify patterns and make predictions. This can help clinicians to make more informed decisions, and also enable personalized medicine. Machine learning can also be used to automate tasks such as image analysis and diagnosis, reducing the burden on healthcare professionals.
Natural Language Processing: Natural Language Processing (NLP) is another emerging trend in AI in health tech. NLP enables computers to understand and interpret human language, which can be used to improve patient engagement and communication. NLP can be used to analyse patient feedback, reviews, and comments to identify trends and improve the overall quality of care.
Robotics: Robotics is also emerging as an important trend in AI in health tech. Robots can be used to perform tasks such as surgery, patient monitoring, and medication dispensing. Robotics can also be used to assist healthcare professionals in tasks such as lifting and moving patients, reducing the risk of injury.
Computer Vision: Computer vision is another emerging trend in AI in health tech. Computer vision algorithms can be used to analyze medical images such as X-rays and CT scans, enabling automated diagnosis and improving accuracy. Computer vision can also be used to monitor patient movement and activity, helping to prevent falls and other accidents.
Challenges and Opportunities: The use of AI in health tech presents both challenges and opportunities. One of the main challenges is ensuring the privacy and security of patient data.
Another challenge is ensuring that the technology is used ethically and does not perpetuate biases. However, the opportunities presented by AI in health tech are enormous. AI has the potential to improve patient outcomes, reduce costs, and enhance the overall quality of care. AI can also enable personalized medicine, improve patient engagement and communication, and assist healthcare professionals in performing their duties.
II. RELATED WORK
III. A CASE STUDY ON AI-POWERED DIAGNOSIS AND TREATMENT OF SKIN CANCER
Skin cancer is the most common form of cancer in the world, with over 5 million cases diagnosed each year. Early detection is critical for successful treatment, but diagnosing skin cancer can be challenging even for experienced dermatologists. However, emerging trends in artificial intelligence in health tech have the potential to improve diagnosis and treatment of skin cancer.
One company that is leveraging AI for skin cancer diagnosis and treatment is SkinVision. SkinVision uses a smartphone app to capture images of moles and lesions, which are then analyzed using a machine learning algorithm to identify potential signs of skin cancer. The app is designed to help users detect changes in their skin over time, which can be an early indicator of skin cancer.
SkinVision's algorithm is based on a large dataset of skin images, which have been labeled by dermatologists to train the algorithm to recognize patterns and identify potential signs of skin cancer. The algorithm can detect over 90% of skin cancer cases, with a false-positive rate of only 12%.
Once a user's skin images have been analyzed, SkinVision provides a risk assessment and recommends next steps, such as scheduling a visit to a dermatologist for further evaluation. The app also provides educational resources on skin cancer prevention and self-examination.
SkinVision's AI-powered approach has the potential to improve skin cancer detection and treatment outcomes by enabling earlier diagnosis and treatment. The app is currently available in several countries and has been used by over 1 million people.
However, there are also concerns related to the accuracy and reliability of AI-powered skin cancer diagnosis. Critics have pointed out that the algorithm may be less accurate for certain skin types and may miss some cases of skin cancer. Additionally, there are concerns related to privacy and data security, as users' skin images are stored on SkinVision's servers.
Overall, the SkinVision case study highlights the potential of AI in healthcare for improving diagnosis and treatment outcomes. However, it also underscores the need for continued research and development to ensure that AI-powered technologies are safe, effective, and ethical.
IV. PROPOSED ARCHITECTURE FOR AI-POWERED DIAGNOSIS AND TREATMENT OF SKIN CANCER
The architecture for an AI-powered diagnosis and treatment system for skin cancer could include the following components:
Overall, the proposed architecture would allow for the accurate diagnosis and treatment of skin cancer using machine learning algorithms and computer vision techniques. The integration with clinical decision support systems would help ensure that the system is providing safe and effective care. Ongoing monitoring and evaluation would also help improve the system's accuracy and effectiveness over time.
There are several potential future implications of emerging trends in artificial intelligence in health tech, some of which are:
a. Improved Patient Outcomes: With the use of AI in healthcare, there is the potential to improve patient outcomes by providing more accurate and personalized diagnoses, predicting diseases at an earlier stage, and developing more effective treatments.
b. Increased Efficiency: AI can help healthcare providers optimize workflows, reduce wait times, and make better use of their time, leading to more efficient delivery of care.
c. Greater Accessibility: By leveraging AI-powered telemedicine and remote monitoring, healthcare providers can increase access to care for patients in underserved or remote areas, as well as those with mobility or transportation issues.
d. Ethical Considerations: As AI becomes more widespread in healthcare, it will be important to consider the ethical implications of its use, including issues related to privacy, data security, and bias.
e. Need for Skilled Workforce: As healthcare providers increasingly rely on AI, there will be a growing need for skilled workers who can develop and maintain these technologies, as well as those who can interpret and act on the data they generate.
f. Regulatory Challenges: As AI continues to advance in healthcare, there will be a need for regulations to ensure that these technologies are safe, effective, and ethical. This will require collaboration between healthcare providers, regulators, and technology developers.
Overall, the future implications of emerging trends in AI in health tech are promising, but will require careful consideration of ethical, regulatory, and workforce challenges in order to fully realize their potential.
AI is transforming the healthcare industry, and the emerging trends in AI in health tech are poised to have a significant impact on healthcare delivery, research, and patient engagement. Machine learning, natural language processing, robotics, and computer vision are some of the most significant emerging trends in AI in health tech. While there are challenges associated with the use of AI in health tech, the opportunities presented by these emerging technologies are enormous. As the healthcare industry continues to adopt AI, it will be important to ensure that the technology is used ethically and responsibly, to ensure that it benefits patients and healthcare professionals alike. In conclusion, emerging trends in artificial intelligence in health tech have the potential to revolutionize the healthcare industry. AI-powered technologies can help healthcare providers deliver more accurate diagnoses, develop more effective treatments, optimize workflows, and increase accessibility to care. However, there are also challenges that need to be addressed, including ethical considerations, regulatory challenges, and the need for a skilled workforce. Moving forward, the future scope of AI in healthcare is vast. There are several areas in which AI can be applied, including drug discovery, disease diagnosis and prediction, personalized medicine, and remote monitoring. As these technologies continue to evolve, it will be important for healthcare providers, regulators, and technology developers to work together to ensure that they are safe, effective, and ethical. One key area for future research is the development of explainable AI, which can help address concerns related to the lack of transparency and interpretability of some AI-powered technologies. Additionally, further research is needed to explore the potential of AI in addressing health disparities and improving healthcare access for underserved populations. Overall, the potential benefits of AI in healthcare are substantial, and with careful consideration of the challenges and opportunities, the future of AI in health tech is bright.
 \"Artificial Intelligence in Healthcare: Past, Present and Future\" by Paul Sonnier and Theodora Bloom.  \"Artificial Intelligence in Healthcare: A Comprehensive Review\" by Shihab Jimaa and M. Shamim Hossain.  \"Applications of Artificial Intelligence in Healthcare Delivery\" by David B. Hoyt and Shamsi Daneshvari.  \"Artificial Intelligence in Medicine: Current Trends and Future Possibilities\" by E. Schreiber and S. Mengelkamp.  \"Artificial Intelligence in Healthcare: Opportunities and Challenges\" by Yasar Khan and Samina Riaz.  \"Artificial Intelligence in Healthcare: A Systematic Review\" by Rina Alawadhi and Nidhi Gupta.  \"Artificial Intelligence in Healthcare: Challenges and Opportunities for Patient-Centered Care\" by Anna V. Doudchenko and Julia S. Polak.  \"Artificial Intelligence in Healthcare: A Review of Current Applications and Ethical Implications\" by Elinor B. Sullivan and Pilar Ossorio.  \"Predictive Analytics for Early Detection of Sepsis: A Literature Survey\" by Yashika Arora, Pratibha Goyal, and Rajesh Kumar.  \"Predictive Analytics for Early Detection of Sepsis: A Systematic Literature Review\" by Hicham Hajj, Mohamed Reda Bouadjenek, and Othmane Bouhali.  \"Early Detection of Sepsis Using Machine Learning Techniques: A Literature Survey\" by Shweta Garg and Vivek Kumar Singh.  \"Predictive Analytics for Sepsis: A Review of Current Approaches and Future Directions\" by Sharmila Devi Ramalingam, Harini Suresh, and Vidya Sagar.  Sesha Bhargavi, V., Spandana, T. (2017). Recommendation Based P2P File Sharing on Disconnected MANET. In: Deiva Sundari, P., Dash, S., Das, S., Panigrahi, B. (eds) Proceedings of 2nd International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-1645-5_18  Velagaleti, Sesha Bhargavi, M. Seetha, and S. Viswanadha Raju. \"A Simulation and Analysis Of Secured AODV Protocol in Mobile Ad Hoc Networks.\" IJRET International Journal of Research in Engineering and Technology (2013).  Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94. PMID: 31363513; PMCID: PMC6616181.  Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. 2020:25–60. doi: 10.1016/B978-0-12-818438-7.00002-2. Epub 2020 Jun 26. PMCID: PMC7325854.  Impact of Artificial Intelligence in the field of Health Care,V Ragavi et al 2021 J. Phys.: Conf. Ser. 1831 012006  Guoguang Rong, Arnaldo Mendez, Elie Bou Assi, Bo Zhao, Mohamad Sawan, Artificial Intelligence in Healthcare: Review and Prediction Case Studies,  Engineering, Volume 6, Issue 3, 2020, Pages 291-301, ISSN 2095-8099.  Zubin Mishra, Birendra Mishra and Oldooz Aloosh, 2020. Impact of Artificial Intelligence on the Healthcare Industry. Trends in Applied Sciences Research, 15: 59-65.  I.Ravi Prakash Reddy, “Intrusion Detection System using SMIFS and Multi class Multi layer Perceptron”, Journal of Computational and Theoretical Nano science, Vol:15, Special Issue:12, ISSN: 1546-1963, Dec-2018  Lee, D.; Yoon, S.N. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int. J. Environ. Res. Public Health 2021, 18, 271.
Copyright © 2023 Sesha Bhargavi Velagaleti, A.Vijaya Krishna, M. Jyothi, D. Sree Lakshmi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.