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230513P - THE CHALLENGE OF AI TO MEDICINE: AN ISLAMIC PERSPECTIVE

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Presented at the International University College of Medical Sciences at Batu Muda Gombak by Professor Omar Hasan Kasule Sr. MB ChB (MUK), MPH (Harvard), DrPH (Harvard)


CONCEPT OF TASKHIIR IN THE QUR’AN

  • وَسَخَّرَ لَكُم مَّا فِي السَّمَاوَاتِ وَمَا فِي الْأَرْضِ جَمِيعًا مِّنْهُ ۚ إِنَّ فِي ذَٰلِكَ لَآيَاتٍ لِّقَوْمٍ يَتَفَكَّرُونَ . الجاثية: 13
  • اللَّهُ الَّذِي خَلَقَ السَّمَاوَاتِ وَالْأَرْضَ وَأَنْزَلَ مِنَ السَّمَاءِ مَاءً فَأَخْرَجَ بِهِ مِنَ الثَّمَرَاتِ رِزْقًا لَكُمْ وَسَخَّرَ لَكُمُ الْفُلْكَ لِتَجْرِيَ فِي الْبَحْرِ بِأَمْرِهِ وَسَخَّرَ لَكُمُ الْأَنْهَارَ وَسَخَّرَ لَكُمُ الشَّمْسَ وَالْقَمَرَ دَائِبَيْنِ وَسَخَّرَ لَكُمُ اللَّيْلَ وَالنَّهَارَ. إبراهيم ٣٢-٣٣ 
  • Human in charge because of largest cerebral surface area to body weight
  • Human controls animals and plants
  • Human manipulates the environment
  • Human makes tools to extend his power of taskhiir
  • Machines and all tools are under human control

2.0 HUMAN-MACHINE INTERACTION BEFORE IT
  • Before the human programmer was in charge, the computer could not work outside what he envisioned and could not come up with unpredictable things.
  • The computer was an extension of human faculties and a mere tool
  • The advantage of the computer was the speed in handling a big amount of data and a huge memory
  • Human control was never challenged

3.0 HUMAN-MACHINE INTERACTION AFTER AI
  • The computer now has some ability to think and solve some problems creatively
  • The computer can control itself and control other computers
  • The computer can make products and machines, and the machine makes more products
  • Has the human lost control?
  • What are the ethical implications of the loss of human control?

4.0 OLD ROLE OF THE COMPUTER IN MEDICINE: Decision support
  • The computer relieved the human physician of routine mechanical thinking processes. It had the advantage of speed and consistency (fewer systematic errors)
  • The computer was an aid to diagnosis by processing a lot of data quickly to narrow down diagnostic choices but in the end, it was the human who decided
  • The computer summarizes information for the human physician to make a decision
  • The computer saves the human from routine mechanistic brain operations that do not involve higher thinking

5.0 NEW ROLE OF THE COMPUTER
  • Deep learning or machine learning technologies have enabled AI to enter widely into medicine. These involve some form of thought and creativity
  • AI is used in disease diagnosis, triage or screening, risk analysis, and surgical operations[1]. AI is used in the early detection of skin cancer in community settings[2].
  • Use of AI in image analysis for breast cancer screening[3].
  • AI is used in cardiology[4]
  • AI used to diagnose secondary hypertension[5]

6.0 USE OF AI IN IMAGING
  • Labelling thyroid nodules[6]
  • Diagnosing lung cancer from chest CT images[7]
  • Diagnosis of liver tumors[8]

7.0 USE OF AI TO CONTROL INSULIN PUMPS
  • Decision making how much insulin to inject is based on monitoring blood sugar.
  • A comparison was made between physicians and AI and found no significant disagreement[9,10]

8.0 IMAGE ON AN AI-CONTROLLED INSULIN PUMP


9.0 USE AI SURGERY

  • AI controls robotics that carries out surgical procedures but is always under the control/supervision of the surgeon
  • Machine learning involves computers deriving information from images and videos and reaching some conclusions then taking action
  • Surgical robots such as the da Vinci Surgical System allow remote surgery
  • Algorithms of AI: reliable?

10.0 ETHICAL QUESTION OF LIABILITY
  • Who is responsible for errors in the computer: the programmer, the manufacturer, the attending physician, or the hospital?
  • Mistakes in diagnosis
  • Mistakes in procedures
  • Mistakes in prognosis
  • Criminal manipulation

REFERENCES:

  1. Jiamin Yin, Kee Yuan Ngiam, Hock Hai Teo. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res.. 2021 Apr 22;23(4)
  2. Jones OT, Matin RN, van der Schaar M, Prathivadi Bhayankaram K, Ranmuthu CKI, Islam MS, Behiyat D, Boscott R, Calanzani N, Emery J, Williams HC, Walter FM. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Lancet Digit Health. 2022 Jun;4(6)
  3. Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, Taylor-Phillips S. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ. 2021 Sep 1;374:n1872. doi: 10.1136/bmj.n1872.
  4. Dipti Itchhaporia Artificial intelligence in cardiology. Cardiovasc Med. 2022 Jan;32(1):34-41.
  5. Lin Wu a b c 1, Liying Huang d 1, Mei Li e 1, Zhaojun Xiong c, Dinghui Liu c, Yong Liu c, Suzhen Liang c, Hua Liang a b, Zifeng Liu f, Xiaoxian Qian c, Jiangtao Ren d, Yanming Chen a bDifferential diagnosis of secondary hypertension based on deep learning. Artificial Intelligence in Medicine. Volume 141, July 2023, 102554
  6. Jikai Zhang, Maciej A. Mazurowski, Brian C. Allen, Benjamin Wildman-Tobriner  . Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. Artificial Intelligence in Medicine. Volume 141, July 2023, 102553
  7. Arash Heidari, Danial Javaheri, Shiva Toumaj, Nima Jafari Navimipour, Mahsa Rezaei, Mehmet Unal. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artificial Intelligence in Medicine. Volume 141, July 2023, 102572
  8. Lakshmipriya, Biju Pottakkat, G. Ramkumar. Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review. Artificial Intelligence in Medicine. Volume 141, July 2023, 102557
  9. Revital Nimri, Amir Tirosh, Ido Muller, Yael Shtrit, Ivana Kraljevic, Montserrat Martín Alonso, Tanja Milicic, Banshi Saboo, Asma Deeb, Athanasios Christoforidis, Marieke den Brinker, Lutgarda Bozzetto, Andrea Mario Bolla, Michal Krcma, Rosa Anna Rabini, Shadi Tabba, Andriani Gerasimidi-Vazeou, Giulio Maltoni, Elisa Giani, Idit Dotan, Idit F Liberty, Yoel Toledano, Olga Kordonouri, Natasa Bratina, Klemen Dovc, Torben Biester, Eran Atlas, Moshe Phillip. Comparison of Insulin Dose Adjustments Made by Artificial Intelligence-Based Decision Support Systems and by Physicians in People with Type 1 Diabetes Using Multiple Daily Injections Therapy. Diabetes Technol Ther. 2022 Aug;24(8):564-572.
  10. Revital Nimri, Tadej Battelino, Lori M Laffel, Robert H Slover, Desmond Schatz, Stuart A Weinzimer, Klemen Dovc, Thomas Danne, Moshe Phillip; NextDREAM Consortium. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Natt Med. 2020 Sep;26(9):1380-1384.