The future of Medicine and AI Technologies, by D. Conterno (2023)

 



Artificial intelligence (AI) has already significantly impacted the field of medicine, and this impact will only increase in the coming years. The integration of AI and medicine has the potential to revolutionise healthcare delivery, enhance treatment outcomes, and improve patient satisfaction. The future of AI in medicine is vast and can change how healthcare is delivered.

 

Where are we today?

The healthcare industry is witnessing unprecedented growth in medical information. With the rise of digital healthcare, electronic health records (EHRs), and other forms of health data, the volume of medical information available to physicians is immense. The traditional approach of diagnosing and treating illnesses based on a physician's experience and expertise is no longer sufficient. Today's medicine requires a multidisciplinary approach and collaboration among physicians, nurses, and other healthcare professionals.


AI can play a critical role in simplifying this complexity by analysing vast amounts of data and providing actionable insights to improve patient outcomes. AI can also help physicians with decision-making, diagnosis, and treatment planning. For example, AI can identify patterns and trends in patient data that might not be immediately apparent to human physicians. This information allows the AI to create personalised treatment plans tailored to each patient's needs.


However, the adoption of AI in medicine has been slow, mainly due to the lack of trust and knowledge among healthcare professionals. In addition, physicians are concerned that AI might replace them, and patients are worried about the privacy and security of their health data. Addressing these concerns and ensuring that AI is used ethically and responsibly is crucial to successfully integrating AI into medicine.

 

The complexity challenge in the medical data evolution

The field of medicine is rapidly evolving, and discoveries are being made every day. AI has the potential to accelerate this process by identifying new treatments and therapies that might not be immediately apparent to human physicians. AI can also help in drug discovery and development by predicting the efficacy and toxicity of potential drug candidates.


However, the rapid pace of change also means that healthcare professionals must continuously update their knowledge and skills to keep up with the latest advancements. General Practitioners and even Specialists might find it challenging to keep up with the complexity and speed of these advancements. This is where AI can play a critical role by assisting healthcare professionals with decision-making and providing up-to-date information.


AI-powered decision support systems can provide physicians real-time information about patient conditions, treatment options, and drug interactions. This information can help physicians make informed decisions based on the latest research and clinical evidence. AI can also help healthcare professionals keep up with the latest advancements by providing personalised learning opportunities.

The future rise of AI and nanobots

The integration of AI and nanotechnology has the potential to revolutionise healthcare delivery by enabling the development of intelligent medical devices that can monitor and treat patients in real-time. For example, nanobots embedded in the human body can monitor vital signs and detect the early signs of illnesses and other physical issues. In addition, these nanobots can communicate with each other and external devices, providing physicians with real-time information about patient conditions.


The use of nanobots can also enhance the precision and efficacy of drug delivery. For example, nanobots can target specific cells or tissues, enabling the delivery of drugs directly to the affected areas. This can reduce the risk of side effects and improve treatment outcomes.


However, integrating nanobots and AI also raises several ethical and regulatory concerns. Ensuring the safety and efficacy of these devices is crucial, and regulations must govern their development, deployment, and use. Additionally, using nanobots raises concerns about privacy and security, as these devices will collect sensitive patient data.

While the integration of AI in medicine has the potential to revolutionise healthcare delivery and enhance treatment outcomes, caution must also be exercised regarding its involvement with the pharmaceutical industry and political organisations. The pharmaceutical industry is notorious for putting profit before the benefit of humankind, and the integration of AI in this sector can exacerbate this problem.

AI-powered drug discovery and development may prioritise profit over patient needs, leading to the creation of drugs that are not effective or have adverse side effects. In addition, the lack of transparency and accountability in the pharmaceutical industry can also lead to the misuse of AI technology for financial gain rather than improving patient outcomes.

Similarly, political organisations prioritising their interests over the population's well-being can misuse AI technology for their benefit. For example, AI-powered healthcare policies and decisions may prioritise cost-cutting over patient care, leading to a decline in the quality of healthcare services.

Therefore, ensuring that AI is used ethically and responsibly in the healthcare industry is crucial. Regulations must be in place to govern the development, deployment, and use of AI in medicine, and transparency and accountability must be ensured to prevent the misuse of AI technology for financial gain or political interests.

 

Conclusion

The integration of AI in medicine has the potential to revolutionise healthcare delivery, enhance treatment outcomes, and improve patient satisfaction. AI can simplify the complexity of medicine by analysing vast amounts of data and providing actionable insights that can help healthcare professionals make informed decisions. Rapid treatment change means that healthcare professionals must continuously update their knowledge and skills to keep up with the latest advancements. Integrating AI and nanotechnology can enable the development of smart medical devices to monitor and treat patients in real-time. However, ensuring the ethical and responsible use of AI and nanotechnology in medicine is crucial to their successful integration.


The future of AI in medicine is vast, and it is challenging to predict how it will unfold. However, one thing is clear: the integration of AI in medicine has the potential to revolutionise healthcare delivery, enhance treatment outcomes, and improve patient satisfaction. Healthcare professionals must embrace AI to realise this potential and work towards responsible and ethical medical integration. The future of medicine is exciting, and AI will undoubtedly play a crucial role in shaping it. Finally, caution must be exercised regarding its involvement with the pharmaceutical industry and political organisations. The focus must remain on improving patient outcomes, not financial gain or political interests.

 

Further readings:

  • Bodenheimer T, Smith M. Primary care: proposed solutions to the physician shortage without training more physicians. Health Aff (Millwood). 2013 Nov;32(11):1881-6. doi: 10.1377/hlthaff.2013.0054. PMID: 24191089.
  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Feb;25(1):44-56. doi: 10.1038/s41591-018-0300-7. PMID: 30617339.
  • Chen X, Li S, Liu Y, Su L, Han M, Zhang L, Zhang Q, Zhang Y, Zhu X, Zhou G. Nanorobots: versatile and multi-functional biomedical tools. Chem Soc Rev. 2020 Feb 17;49(4):1192-1229. doi: 10.1039/c9cs00769d. PMID: 31960014.
  • Lu Y, Aimetti AA, Langer R, Gu Z. Bioresponsive materials. Nat Rev Mater. 2016;2(8):16075. doi: 10.1038/natrevmats.2016.75. Epub 2016 Jul 29. PMID: 31057848.
  • Magnus D, Cho MK. Issues in the oversight of nanobiotechnology: an expert panel report. J Law Med Ethics. 2009 Summer;37(2):271-86. doi: 10.1111/j.1748-720x.2009.00367.x. PMID: 19493093.
  • Wong J, Wasserfallen JB. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging. 2020 Jan 22;11(1):16. doi: 10.1186/s13244-020-0827-6. PMID: 31970454.
  • Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016 Dec 7;37(46):3523-3531. doi: 10.1093/eurheartj/ehw480. Epub 2016 Oct 7. PMID: 27729332.
  • Iyawa GE, Adebayo OS, Adetiba E. Artificial Intelligence (AI) in Healthcare: Past, Present and Future. Int J Biomed Eng Sci. 2020;10(3):100-117. doi: 10.11648/j.bio.20200103.11.
  • Chen H, Linstead E. Artificial Intelligence in Drug Discovery: Recent Advances and Future Prospects. Expert Opin Drug Discov. 2020
  • Jin X, Wong ST. Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today. 2014 Jan;19(1):6370. doi: 10.1016/j.drudis.2013.11.008. Epub 2013 Nov 21. PMID: 24269698.
  • Singh RP, Sharma G, Soni R, Jaswal S. Artificial Intelligence and Machine Learning to Revolutionize Healthcare: A Review. J Med Syst. 2020 Nov 5;44(12):241. doi: 10.1007/s10916-020-01638-1. PMID: 33151406.
  • Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. PMID: 30943339.
  • Majeed MH, Doja MN, Oh J, Haddad PM, Bhatti U, Tewfik MA, De Vito A. Artificial intelligence (AI) in healthcare education: a systematic review. Med Educ Online. 2020 Dec;25(1):1818430. doi: 10.1080/10872981.2020.1818430. PMID: 32933441.
  • Gagnon MA. The drug industry's influence over the FDA. J Law Med Ethics. 2010 Fall;38(3):560-8. doi: 10.1111/j.1748-720X.2010.00505.x. PMID: 20880326.
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